diff options
92 files changed, 1285 insertions, 1670 deletions
diff --git a/.Rbuildignore b/.Rbuildignore index b9501610..dc1506da 100644 --- a/.Rbuildignore +++ b/.Rbuildignore @@ -2,6 +2,7 @@ ^build.log$ ^check.log$ ^test.log$ +^test_dev.log$ ^tests_slow.log$ ^test.R$ ^README.html$ diff --git a/DESCRIPTION b/DESCRIPTION index 782fb543..9e0d63d7 100644 --- a/DESCRIPTION +++ b/DESCRIPTION @@ -26,7 +26,7 @@ LazyData: yes Encoding: UTF-8 Language: en-GB VignetteBuilder: knitr -BugReports: http://github.com/jranke/mkin/issues -URL: https://pkgdown.jrwb.de/mkin +BugReports: http://github.com/jranke/mkin/issues/ +URL: https://pkgdown.jrwb.de/mkin/ Roxygen: list(markdown = TRUE) RoxygenNote: 7.1.1 diff --git a/GNUmakefile b/GNUmakefile index 1adac4e0..fea87d6b 100644 --- a/GNUmakefile +++ b/GNUmakefile @@ -80,7 +80,7 @@ test: install devtest: install "$(RDEVBIN)/Rscript" -e 'devtools::test()' 2>&1 | tee test_dev.log - sed -i -e "s/\r.*\r//" test.log + sed -i -e "s/\r.*\r//" test_dev.log slowtests: install NOT_CRAN=true "$(RBIN)/Rscript" -e 'library(mkin); testthat::test_dir("tests/testthat/slow")' 2>&1 | tee tests_slow.log @@ -72,8 +72,6 @@ export(parms) export(plot_err) export(plot_res) export(plot_sep) -export(saemix_data) -export(saemix_model) export(sigma_twocomp) export(transform_odeparms) import(deSolve) @@ -90,8 +88,6 @@ importFrom(parallel,mclapply) importFrom(parallel,parLapply) importFrom(pkgbuild,has_compiler) importFrom(purrr,map_dfr) -importFrom(saemix,saemixData) -importFrom(saemix,saemixModel) importFrom(stats,AIC) importFrom(stats,BIC) importFrom(stats,aggregate) @@ -116,6 +112,5 @@ importFrom(stats,residuals) importFrom(stats,rnorm) importFrom(stats,shapiro.test) importFrom(stats,update) -importFrom(stats,var) importFrom(utils,getFromNamespace) importFrom(utils,write.table) @@ -516,7 +516,7 @@ # mkin 0.9-27 (2014-05-10) -- Fork the GUI into a separate package [gmkin](http://github.com/jranke/gmkin) +- Fork the GUI into a separate package [gmkin](https://github.com/jranke/gmkin) - DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information @@ -1,9 +1,9 @@ #' Evaluate parent kinetics using the NAFTA guidance -#' +#' #' The function fits the SFO, IORE and DFOP models using \code{\link{mmkin}} #' and returns an object of class \code{nafta} that has methods for printing #' and plotting. -#' +#' #' @param ds A dataframe that must contain one variable called "time" with the #' time values specified by the \code{time} argument, one column called #' "name" with the grouping of the observed values, and finally one column of @@ -23,17 +23,17 @@ #' Pesticides #' \url{https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation} #' accessed 2019-02-22 -#' +#' #' US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to #' Calculate Representative Half-life Values and Characterizing Pesticide #' Degradation #' \url{https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance} #' @examples -#' +#' #' nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1) #' print(nafta_evaluation) #' plot(nafta_evaluation) -#' +#' #' @export nafta <- function(ds, title = NA, quiet = FALSE, ...) { if (length(levels(ds$name)) > 1) { @@ -51,7 +51,7 @@ nafta <- function(ds, title = NA, quiet = FALSE, ...) { dimnames = list(models, c("DT50", "DT90", "DT50_rep"))) result$distimes["SFO", ] <- distimes[[1]][c(1, 2, 1)] result$distimes["IORE", ] <- distimes[[2]][c(1, 2, 3)] - result$distimes["DFOP", ] <- distimes[[3]][c(1, 2, 4)] + result$distimes["DFOP", ] <- distimes[[3]][c(1, 2, 5)] # Get parameters with statistics result$parameters <- lapply(result$mmkin, function(x) { @@ -75,13 +75,13 @@ nafta <- function(ds, title = NA, quiet = FALSE, ...) { return(result) } -#' Plot the results of the three models used in the NAFTA scheme. -#' +#' Plot the results of the three models used in the NAFTA scheme. +#' #' The plots are ordered with increasing complexity of the model in this #' function (SFO, then IORE, then DFOP). -#' +#' #' Calls \code{\link{plot.mmkin}}. -#' +#' #' @param x An object of class \code{\link{nafta}}. #' @param legend Should a legend be added? #' @param main Possibility to override the main title of the plot. @@ -98,10 +98,10 @@ plot.nafta <- function(x, legend = FALSE, main = "auto", ...) { } #' Print nafta objects -#' +#' #' Print nafta objects. The results for the three models are printed in the #' order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP. -#' +#' #' @param x An \code{\link{nafta}} object. #' @param digits Number of digits to be used for printing parameters and #' dissipation times. @@ -39,9 +39,9 @@ at the package vignettes ## Documentation The HTML documentation of the latest version released to CRAN is available at -[jrwb.de](https://pkgdown.jrwb.de/mkin) and -[github](http://jranke.github.io/mkin). Documentation of the development -version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev). +[jrwb.de](https://pkgdown.jrwb.de/mkin/) and +[github](https://jranke.github.io/mkin/). Documentation of the development +version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev/). ## Features @@ -104,7 +104,7 @@ version is found in the ['dev' subdirectory](https://pkgdown.jrwb.de/mkin/dev). ## GUI There is a graphical user interface that may be useful. Please -refer to its [documentation page](http://kinfit.r-forge.r-project.org/gmkin_static) +refer to its [documentation page](https://pkgdown.jrwb.de/gmkin/) for installation instructions and a manual. ## News @@ -142,7 +142,7 @@ CRAN on 01 May 2010. The first `mkin` code was [published on 11 May 2010](https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8) and the -[first CRAN version](https://cran.r-project.org/src/contrib/Archive/mkin) +[first CRAN version](https://cran.r-project.org/src/contrib/Archive/mkin/) on 18 May 2010. In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named @@ -161,7 +161,7 @@ find a zip archive of the R scripts derived from `mkin`, published under the GPL license. Finally, there is -[KineticEval](http://github.com/zhenglei-gao/KineticEval), which contains +[KineticEval](https://github.com/zhenglei-gao/KineticEval), which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well. diff --git a/_pkgdown.yml b/_pkgdown.yml index 3882ae4b..74abeeba 100644 --- a/_pkgdown.yml +++ b/_pkgdown.yml @@ -43,8 +43,6 @@ reference: - nlme.mmkin - plot.nlme.mmkin - nlme_function - - saemix_model - - saemix_data - get_deg_func - title: Datasets and known results contents: @@ -7,10 +7,8 @@ * checking extension type ... Package * this is package ‘mkin’ version ‘0.9.50.3’ * package encoding: UTF-8 -* checking CRAN incoming feasibility ... NOTE +* checking CRAN incoming feasibility ... Note_to_CRAN_maintainers Maintainer: ‘Johannes Ranke <jranke@uni-bremen.de>’ - -The Date field is over a month old. * checking package namespace information ... OK * checking package dependencies ... OK * checking if this is a source package ... OK @@ -69,9 +67,5 @@ The Date field is over a month old. * checking for detritus in the temp directory ... OK * DONE -Status: 1 NOTE -See - ‘/home/jranke/git/mkin/mkin.Rcheck/00check.log’ -for details. - +Status: OK diff --git a/docs/dev/articles/FOCUS_D.html b/docs/dev/articles/FOCUS_D.html index 7d5dd732..02701431 100644 --- a/docs/dev/articles/FOCUS_D.html +++ b/docs/dev/articles/FOCUS_D.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/FOCUS_D.rmd"><code>vignettes/FOCUS_D.rmd</code></a></small> <div class="hidden name"><code>FOCUS_D.rmd</code></div> @@ -171,18 +171,20 @@ <div class="sourceCode" id="cb7"><html><body><pre class="r"><span class="no">fit</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOCUS_2006_D</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div> <pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value ## of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Shapiro-Wilk test for +## standardized residuals: p = 0.0165</code></pre> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> -<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">fit</span>, <span class="kw">lpos</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>))</pre></body></html></div> +<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="fu"><a href="../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">fit</span>, <span class="kw">lpos</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="st">"topright"</span>, <span class="st">"bottomright"</span>))</pre></body></html></div> <p><img src="FOCUS_D_files/figure-html/plot-1.png" width="768"></p> <p>Confidence intervals for the parameter estimates are obtained using the <code>mkinparplot</code> function.</p> -<div class="sourceCode" id="cb10"><html><body><pre class="r"><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(<span class="no">fit</span>)</pre></body></html></div> +<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="fu"><a href="../reference/mkinparplot.html">mkinparplot</a></span>(<span class="no">fit</span>)</pre></body></html></div> <p><img src="FOCUS_D_files/figure-html/plot_2-1.png" width="768"></p> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> -<div class="sourceCode" id="cb11"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">fit</span>)</pre></body></html></div> +<div class="sourceCode" id="cb12"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">fit</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:36 2020 -## Date of summary: Wed May 27 07:51:37 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:03 2020 +## Date of summary: Thu Oct 8 09:14:03 2020 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -190,7 +192,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 421 model solutions performed in 0.173 s +## Fitted using 421 model solutions performed in 0.171 s ## ## Error model: Constant variance ## @@ -214,6 +216,11 @@ ## value type ## m1_0 0 state ## +## +## Warning(s): +## Observations with value of zero were removed from the data +## Shapiro-Wilk test for standardized residuals: p = 0.0165 +## ## Results: ## ## AIC BIC logLik @@ -229,11 +236,11 @@ ## ## Parameter correlation: ## parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma -## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -3.190e-07 +## parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -3.214e-07 ## log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 3.168e-07 -## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 -1.406e-07 -## f_parent_ilr_1 -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 -1.587e-10 -## sigma -3.190e-07 3.168e-07 -1.406e-07 -1.587e-10 1.000e+00 +## log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 -1.410e-07 +## f_parent_ilr_1 -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 5.093e-10 +## sigma -3.214e-07 3.168e-07 -1.410e-07 5.093e-10 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. diff --git a/docs/dev/articles/FOCUS_L.html b/docs/dev/articles/FOCUS_L.html index d69815ab..ffc0bebf 100644 --- a/docs/dev/articles/FOCUS_L.html +++ b/docs/dev/articles/FOCUS_L.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/FOCUS_L.rmd"><code>vignettes/FOCUS_L.rmd</code></a></small> <div class="hidden name"><code>FOCUS_L.rmd</code></div> @@ -126,30 +126,30 @@ <div class="sourceCode" id="cb2"><html><body><pre class="r"><span class="no">m.L1.SFO</span> <span class="kw"><-</span> <span class="fu"><a href="../reference/mkinfit.html">mkinfit</a></span>(<span class="st">"SFO"</span>, <span class="no">FOCUS_2006_L1_mkin</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>) <span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">m.L1.SFO</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:39 2020 -## Date of summary: Wed May 27 07:51:39 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:05 2020 +## Date of summary: Thu Oct 8 09:14:05 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.031 s +## Fitted using 133 model solutions performed in 0.032 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.85 state -## k_parent_sink 0.10 deparm +## value type +## parent_0 89.85 state +## k_parent 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 89.850000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -160,25 +160,25 @@ ## 93.88778 96.5589 -43.94389 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 6.186e-01 -1.712e-09 -## log_k_parent_sink 6.186e-01 1.000e+00 -3.237e-09 -## sigma -1.712e-09 -3.237e-09 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -227,21 +227,16 @@ <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:39 2020 -## Date of summary: Wed May 27 07:51:39 2020 -## -## -## Warning: Optimisation did not converge: -## false convergence (8) -## +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:05 2020 +## Date of summary: Thu Oct 8 09:14:05 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 899 model solutions performed in 0.204 s +## Fitted using 380 model solutions performed in 0.088 s ## ## Error model: Constant variance ## @@ -262,34 +257,39 @@ ## Fixed parameter values: ## None ## +## +## Warning(s): +## Optimisation did not converge: +## false convergence (8) +## ## Results: ## ## AIC BIC logLik -## 95.88835 99.44984 -43.94418 +## 95.88778 99.44927 -43.94389 ## ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper -## parent_0 92.47 1.2800 89.730 95.220 -## log_alpha 10.58 NaN NaN NaN -## log_beta 12.93 NaN NaN NaN -## sigma 2.78 0.4507 1.813 3.747 +## parent_0 92.47 1.2820 89.720 95.220 +## log_alpha 16.92 NaN NaN NaN +## log_beta 19.26 NaN NaN NaN +## sigma 2.78 0.4501 1.814 3.745 ## ## Parameter correlation: -## parent_0 log_alpha log_beta sigma -## parent_0 1.00000 NaN NaN 0.01452 -## log_alpha NaN 1 NaN NaN -## log_beta NaN NaN 1 NaN -## sigma 0.01452 NaN NaN 1.00000 +## parent_0 log_alpha log_beta sigma +## parent_0 1.000000 NaN NaN 0.002218 +## log_alpha NaN 1 NaN NaN +## log_beta NaN NaN 1 NaN +## sigma 0.002218 NaN NaN 1.000000 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47 72.13000 1.052e-19 89.730 95.220 -## alpha 39440.00 0.02397 4.906e-01 NA NA -## beta 412500.00 0.02397 4.906e-01 NA NA -## sigma 2.78 6.00000 1.628e-05 1.813 3.747 +## Estimate t value Pr(>t) Lower Upper +## parent_0 9.247e+01 NA NA 89.720 95.220 +## alpha 2.223e+07 NA NA NA NA +## beta 2.325e+08 NA NA NA NA +## sigma 2.780e+00 NA NA 1.814 3.745 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -297,8 +297,8 @@ ## parent 3.619 3 6 ## ## Estimated disappearance times: -## DT50 DT90 DT50back -## parent 7.249 24.08 7.249</code></pre> +## DT50 DT90 DT50back +## parent 7.25 24.08 7.25</code></pre> <p>We get a warning that the default optimisation algorithm <code>Port</code> did not converge, which is an indication that the model is overparameterised, <em>i.e.</em> contains too many parameters that are ill-defined as a consequence.</p> <p>And in fact, due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the <span class="math inline">\(\chi^2\)</span> error level is actually higher for the FOMC model (3.6%) than for the SFO model (3.4%). Additionally, the parameters <code>log_alpha</code> and <code>log_beta</code> internally fitted in the model have excessive confidence intervals, that span more than 25 orders of magnitude (!) when backtransformed to the scale of <code>alpha</code> and <code>beta</code>. Also, the t-test for significant difference from zero does not indicate such a significant difference, with p-values greater than 0.1, and finally, the parameter correlation of <code>log_alpha</code> and <code>log_beta</code> is 1.000, clearly indicating that the model is overparameterised.</p> <p>The <span class="math inline">\(\chi^2\)</span> error levels reported in Appendix 3 and Appendix 7 to the FOCUS kinetics report are rounded to integer percentages and partly deviate by one percentage point from the results calculated by mkin. The reason for this is not known. However, mkin gives the same <span class="math inline">\(\chi^2\)</span> error levels as the kinfit package and the calculation routines of the kinfit package have been extensively compared to the results obtained by the KinGUI software, as documented in the kinfit package vignette. KinGUI was the first widely used standard package in this field. Also, the calculation of <span class="math inline">\(\chi^2\)</span> error levels was compared with KinGUII, CAKE and DegKin manager in a project sponsored by the German Umweltbundesamt <span class="citation">(Ranke 2014)</span>.</p> @@ -335,16 +335,16 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-9-1.png" width="672"></p> <div class="sourceCode" id="cb17"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">m.L2.FOMC</span>, <span class="kw">data</span> <span class="kw">=</span> <span class="fl">FALSE</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:40 2020 -## Date of summary: Wed May 27 07:51:40 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:06 2020 +## Date of summary: Thu Oct 8 09:14:06 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.048 s +## Fitted using 239 model solutions performed in 0.049 s ## ## Error model: Constant variance ## @@ -379,10 +379,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.637e-09 +## parent_0 1.000e+00 -1.151e-01 -2.085e-01 -7.436e-09 ## log_alpha -1.151e-01 1.000e+00 9.741e-01 -1.617e-07 -## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.387e-07 -## sigma -7.637e-09 -1.617e-07 -1.387e-07 1.000e+00 +## log_beta -2.085e-01 9.741e-01 1.000e+00 -1.386e-07 +## sigma -7.436e-09 -1.617e-07 -1.386e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -414,9 +414,9 @@ <p><img src="FOCUS_L_files/figure-html/unnamed-chunk-10-1.png" width="672"></p> <div class="sourceCode" id="cb20"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">m.L2.DFOP</span>, <span class="kw">data</span> <span class="kw">=</span> <span class="fl">FALSE</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:40 2020 -## Date of summary: Wed May 27 07:51:40 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:06 2020 +## Date of summary: Thu Oct 8 09:14:06 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -425,7 +425,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 572 model solutions performed in 0.131 s +## Fitted using 572 model solutions performed in 0.136 s ## ## Error model: Constant variance ## @@ -456,18 +456,18 @@ ## Optimised, transformed parameters with symmetric confidence intervals: ## Estimate Std. Error Lower Upper ## parent_0 93.9500 9.998e-01 91.5900 96.3100 -## log_k1 3.1370 2.376e+03 -5616.0000 5622.0000 +## log_k1 3.1370 2.376e+03 -5615.0000 5622.0000 ## log_k2 -1.0880 6.285e-02 -1.2370 -0.9394 ## g_ilr -0.2821 7.033e-02 -0.4484 -0.1158 ## sigma 1.4140 2.886e-01 0.7314 2.0960 ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_ilr sigma -## parent_0 1.000e+00 5.155e-07 2.371e-09 2.665e-01 -6.849e-09 -## log_k1 5.155e-07 1.000e+00 8.434e-05 -1.659e-04 -7.791e-06 -## log_k2 2.371e-09 8.434e-05 1.000e+00 -7.903e-01 -1.262e-08 -## g_ilr 2.665e-01 -1.659e-04 -7.903e-01 1.000e+00 3.241e-08 -## sigma -6.849e-09 -7.791e-06 -1.262e-08 3.241e-08 1.000e+00 +## parent_0 1.000e+00 5.157e-07 2.376e-09 2.665e-01 -6.837e-09 +## log_k1 5.157e-07 1.000e+00 8.434e-05 -1.659e-04 -7.786e-06 +## log_k2 2.376e-09 8.434e-05 1.000e+00 -7.903e-01 -1.263e-08 +## g_ilr 2.665e-01 -1.659e-04 -7.903e-01 1.000e+00 3.248e-08 +## sigma -6.837e-09 -7.786e-06 -1.263e-08 3.248e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -486,8 +486,8 @@ ## parent 2.53 4 2 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 0.5335 5.311 0.03009 2.058</code></pre> +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 0.5335 5.311 1.599 0.03009 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -517,9 +517,9 @@ <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <div class="sourceCode" id="cb24"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">mm.L3</span><span class="kw">[[</span><span class="st">"DFOP"</span>, <span class="fl">1</span>]])</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:41 2020 -## Date of summary: Wed May 27 07:51:41 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:07 2020 +## Date of summary: Thu Oct 8 09:14:07 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -528,7 +528,7 @@ ## ## Model predictions using solution type analytical ## -## Fitted using 373 model solutions performed in 0.079 s +## Fitted using 373 model solutions performed in 0.086 s ## ## Error model: Constant variance ## @@ -566,11 +566,11 @@ ## ## Parameter correlation: ## parent_0 log_k1 log_k2 g_ilr sigma -## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -6.872e-07 -## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 3.200e-07 -## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 7.673e-07 -## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -8.731e-07 -## sigma -6.872e-07 3.200e-07 7.673e-07 -8.731e-07 1.000e+00 +## parent_0 1.000e+00 1.732e-01 2.282e-02 4.009e-01 -6.868e-07 +## log_k1 1.732e-01 1.000e+00 4.945e-01 -5.809e-01 3.175e-07 +## log_k2 2.282e-02 4.945e-01 1.000e+00 -6.812e-01 7.631e-07 +## g_ilr 4.009e-01 -5.809e-01 -6.812e-01 1.000e+00 -8.694e-07 +## sigma -6.868e-07 3.175e-07 7.631e-07 -8.694e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. @@ -589,8 +589,8 @@ ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 7.464 123 1.343 50.37 +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 7.464 123 37.03 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -626,30 +626,30 @@ <p>The <span class="math inline">\(\chi^2\)</span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline">\(\chi^2\)</span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <div class="sourceCode" id="cb29"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">mm.L4</span><span class="kw">[[</span><span class="st">"SFO"</span>, <span class="fl">1</span>]], <span class="kw">data</span> <span class="kw">=</span> <span class="fl">FALSE</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:41 2020 -## Date of summary: Wed May 27 07:51:41 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:07 2020 +## Date of summary: Thu Oct 8 09:14:07 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.028 s +## Fitted using 142 model solutions performed in 0.03 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.6 state -## k_parent_sink 0.1 deparm +## value type +## parent_0 96.6 state +## k_parent 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 96.600000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -660,25 +660,25 @@ ## 47.12133 47.35966 -20.56067 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 5.938e-01 3.440e-07 -## log_k_parent_sink 5.938e-01 1.000e+00 5.885e-07 -## sigma 3.440e-07 5.885e-07 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 5.938e-01 3.387e-07 +## log_k_parent 5.938e-01 1.000e+00 5.830e-07 +## sigma 3.387e-07 5.830e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -690,16 +690,16 @@ ## parent 106 352</code></pre> <div class="sourceCode" id="cb31"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">mm.L4</span><span class="kw">[[</span><span class="st">"FOMC"</span>, <span class="fl">1</span>]], <span class="kw">data</span> <span class="kw">=</span> <span class="fl">FALSE</span>)</pre></body></html></div> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Wed May 27 07:51:41 2020 -## Date of summary: Wed May 27 07:51:41 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:14:07 2020 +## Date of summary: Thu Oct 8 09:14:07 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.044 s +## Fitted using 224 model solutions performed in 0.046 s ## ## Error model: Constant variance ## @@ -734,10 +734,10 @@ ## ## Parameter correlation: ## parent_0 log_alpha log_beta sigma -## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.563e-07 -## log_alpha -4.696e-01 1.000e+00 9.889e-01 4.066e-08 -## log_beta -5.543e-01 9.889e-01 1.000e+00 6.818e-08 -## sigma -2.563e-07 4.066e-08 6.818e-08 1.000e+00 +## parent_0 1.000e+00 -4.696e-01 -5.543e-01 -2.456e-07 +## log_alpha -4.696e-01 1.000e+00 9.889e-01 2.169e-08 +## log_beta -5.543e-01 9.889e-01 1.000e+00 4.910e-08 +## sigma -2.456e-07 2.169e-08 4.910e-08 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png Binary files differindex 2e5071d9..db54326e 100644 --- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png +++ b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-15-1.png diff --git a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png Binary files differindex 16235059..bfa271dd 100644 --- a/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png +++ b/docs/dev/articles/FOCUS_L_files/figure-html/unnamed-chunk-6-1.png diff --git a/docs/dev/articles/mkin.html b/docs/dev/articles/mkin.html index 4cc06a43..6865fe96 100644 --- a/docs/dev/articles/mkin.html +++ b/docs/dev/articles/mkin.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/mkin.rmd"><code>vignettes/mkin.rmd</code></a></small> <div class="hidden name"><code>mkin.rmd</code></div> @@ -110,7 +110,7 @@ -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> +<p><a href="https://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br><a href="http://chem.uft.uni-bremen.de/ranke/">Privatdozent at the University of Bremen</a></p> <div id="abstract" class="section level1"> <h1 class="hasAnchor"> <a href="#abstract" class="anchor"></a>Abstract</h1> @@ -151,7 +151,7 @@ <p>Many approaches are possible regarding the evaluation of chemical degradation data.</p> <p>The <code>mkin</code> package <span class="citation">(Ranke 2019)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span> for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p> <p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> +<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> <p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</p> <p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</p> <p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> @@ -161,7 +161,7 @@ <p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> <p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p> <p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> -<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> +<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> <p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> <div id="recent-developments" class="section level2"> @@ -227,7 +227,7 @@ <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> </div> <div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="http://www.jstatsoft.org/v33/i03/" class="uri">http://www.jstatsoft.org/v33/i03/</a>.</p> +<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/" class="uri">https://www.jstatsoft.org/v33/i03/</a>.</p> </div> </div> </div> diff --git a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png Binary files differindex 62ea16f2..bdc067c1 100644 --- a/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png +++ b/docs/dev/articles/mkin_files/figure-html/unnamed-chunk-2-1.png diff --git a/docs/dev/articles/twa.html b/docs/dev/articles/twa.html index 29be6c95..d1093e13 100644 --- a/docs/dev/articles/twa.html +++ b/docs/dev/articles/twa.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/twa.rmd"><code>vignettes/twa.rmd</code></a></small> <div class="hidden name"><code>twa.rmd</code></div> @@ -141,7 +141,7 @@ <p><span class="math display">\[f_\textrm{twa} = \frac{1}{t} \left( \frac{1}{k_1} \left( 1 - e^{- k_1 t_b} \right) + \frac{e^{- k_1 t_b}}{k_2} \left( 1 - e^{- k_2 (t - t_b)} \right) \right) \]</span></p> -<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://rdrr.io/pkg/pfm/man/max_twa.html">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> +<p>Note that a method for calculating maximum moving window time weighted average concentrations for a model fitted by ‘mkinfit’ or from parent decline model parameters is included in the <code><a href="../reference/max_twa_parent.html">max_twa_parent()</a></code> function. If the same is needed for metabolites, the function <code><a href="https://pkgdown.jrwb.de/pfm/reference/max_twa.html">pfm::max_twa()</a></code> from the ‘pfm’ package can be used.</p> <div id="refs" class="references"> <div id="ref-FOCUSkinetics2014"> <p>FOCUS Work Group on Degradation Kinetics. 2014. <em>Generic Guidance for Estimating Persistence and Degradation Kinetics from Environmental Fate Studies on Pesticides in Eu Registration</em>. 1.1 ed. <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics" class="uri">http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics</a>.</p> diff --git a/docs/dev/articles/web_only/FOCUS_Z.html b/docs/dev/articles/web_only/FOCUS_Z.html index 270232d7..763ca9be 100644 --- a/docs/dev/articles/web_only/FOCUS_Z.html +++ b/docs/dev/articles/web_only/FOCUS_Z.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Example evaluation of FOCUS dataset Z</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/web_only/FOCUS_Z.rmd"><code>vignettes/web_only/FOCUS_Z.rmd</code></a></small> <div class="hidden name"><code>FOCUS_Z.rmd</code></div> @@ -217,25 +217,25 @@ <div class="sourceCode" id="cb33"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.FOCUS</span>)</pre></body></html></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png" width="700"></p> <div class="sourceCode" id="cb34"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/summary.html">summary</a></span>(<span class="no">m.Z.FOCUS</span>, <span class="kw">data</span> <span class="kw">=</span> <span class="fl">FALSE</span>)$<span class="no">bpar</span></pre></body></html></div> -<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper -## Z0_0 96.840695 1.994285 48.5591 4.0254e-42 92.828744 100.85265 -## k_Z0 2.215467 0.118463 18.7018 1.0417e-23 1.989524 2.46707 -## k_Z1 0.478325 0.028259 16.9265 6.2441e-22 0.424725 0.53869 -## k_Z2 0.451638 0.042139 10.7177 1.6309e-14 0.374346 0.54489 -## k_Z3 0.058692 0.015245 3.8498 1.7807e-04 0.034806 0.09897 -## f_Z2_to_Z3 0.471484 0.058348 8.0805 9.6599e-11 0.357736 0.58827 -## sigma 3.984431 0.383402 10.3923 4.5576e-14 3.213126 4.75574</code></pre> +<pre><code>## Estimate se_notrans t value Pr(>t) Lower Upper +## Z0_0 96.838721 1.994275 48.5584 4.0283e-42 92.826878 100.850563 +## k_Z0 2.215400 0.118459 18.7019 1.0414e-23 1.989462 2.466998 +## k_Z1 0.478301 0.028257 16.9267 6.2411e-22 0.424705 0.538662 +## k_Z2 0.451623 0.042138 10.7176 1.6313e-14 0.374336 0.544867 +## k_Z3 0.058694 0.015246 3.8499 1.7804e-04 0.034809 0.098967 +## f_Z2_to_Z3 0.471510 0.058352 8.0804 9.6640e-11 0.357775 0.588283 +## sigma 3.984431 0.383402 10.3923 4.5575e-14 3.213126 4.755736</code></pre> <div class="sourceCode" id="cb36"><html><body><pre class="r"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span>(<span class="no">m.Z.FOCUS</span>)</pre></body></html></div> <pre><code>## $ff ## Z2_Z3 Z2_sink -## 0.47148 0.52852 +## 0.47151 0.52849 ## ## $distimes ## DT50 DT90 -## Z0 0.31287 1.0393 -## Z1 1.44911 4.8138 -## Z2 1.53474 5.0983 -## Z3 11.80989 39.2316</code></pre> +## Z0 0.31288 1.0394 +## Z1 1.44919 4.8141 +## Z2 1.53479 5.0985 +## Z3 11.80955 39.2305</code></pre> <p>This fit corresponds to the final result chosen in Appendix 7 of the FOCUS report. Confidence intervals returned by mkin are based on internally transformed parameters, however.</p> </div> <div id="using-the-sforb-model" class="section level1"> @@ -277,51 +277,57 @@ <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div> <pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. ## 3$bparms.ode, : Observations with value of zero were removed from the data</code></pre> -<div class="sourceCode" id="cb54"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.4</span>)</pre></body></html></div> +<pre><code>## Warning in mkinfit(Z.mkin.4, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. +## 3$bparms.ode, : Shapiro-Wilk test for standardized residuals: p = 0.0449</code></pre> +<div class="sourceCode" id="cb55"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.4</span>)</pre></body></html></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png" width="700"></p> <p>The error level of the fit, but especially of metabolite Z3, can be improved if the SFORB model is chosen for this metabolite, as this model is capable of representing the tailing of the metabolite decline phase.</p> -<div class="sourceCode" id="cb55"><html><body><pre class="r"><span class="no">Z.mkin.5</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">Z0</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, <span class="kw">sink</span> <span class="kw">=</span> <span class="fl">FALSE</span>), +<div class="sourceCode" id="cb56"><html><body><pre class="r"><span class="no">Z.mkin.5</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">Z0</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>, <span class="st">"Z1"</span>, <span class="kw">sink</span> <span class="kw">=</span> <span class="fl">FALSE</span>), <span class="kw">Z1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z2"</span>, <span class="kw">sink</span> <span class="kw">=</span> <span class="fl">FALSE</span>), <span class="kw">Z2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"Z3"</span>), <span class="kw">Z3</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFORB"</span>))</pre></body></html></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> -<div class="sourceCode" id="cb57"><html><body><pre class="r"><span class="no">m.Z.mkin.5</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span>(<span class="no">Z.mkin.5</span>, <span class="no">FOCUS_2006_Z_mkin</span>, +<div class="sourceCode" id="cb58"><html><body><pre class="r"><span class="no">m.Z.mkin.5</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span>(<span class="no">Z.mkin.5</span>, <span class="no">FOCUS_2006_Z_mkin</span>, <span class="kw">parms.ini</span> <span class="kw">=</span> <span class="no">m.Z.mkin.4</span>$<span class="no">bparms.ode</span>[<span class="fl">1</span>:<span class="fl">4</span>], <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div> <pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. ## 4$bparms.ode[1:4], : Observations with value of zero were removed from the data</code></pre> -<div class="sourceCode" id="cb59"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.5</span>)</pre></body></html></div> +<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = m.Z.mkin. +## 4$bparms.ode[1:4], : Shapiro-Wilk test for standardized residuals: p = 0.00785</code></pre> +<div class="sourceCode" id="cb61"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.5</span>)</pre></body></html></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png" width="700"></p> <p>The summary view of the backtransformed parameters shows that we get no confidence intervals due to overparameterisation. As the optimized is excessively small, it seems reasonable to fix it to zero.</p> -<div class="sourceCode" id="cb60"><html><body><pre class="r"><span class="no">m.Z.mkin.5a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span>(<span class="no">Z.mkin.5</span>, <span class="no">FOCUS_2006_Z_mkin</span>, +<div class="sourceCode" id="cb62"><html><body><pre class="r"><span class="no">m.Z.mkin.5a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinfit.html">mkinfit</a></span>(<span class="no">Z.mkin.5</span>, <span class="no">FOCUS_2006_Z_mkin</span>, <span class="kw">parms.ini</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">m.Z.mkin.5</span>$<span class="no">bparms.ode</span>[<span class="fl">1</span>:<span class="fl">7</span>], <span class="kw">k_Z3_bound_free</span> <span class="kw">=</span> <span class="fl">0</span>), <span class="kw">fixed_parms</span> <span class="kw">=</span> <span class="st">"k_Z3_bound_free"</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>)</pre></body></html></div> <pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin. ## 5$bparms.ode[1:7], : Observations with value of zero were removed from the data</code></pre> -<div class="sourceCode" id="cb62"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> +<pre><code>## Warning in mkinfit(Z.mkin.5, FOCUS_2006_Z_mkin, parms.ini = c(m.Z.mkin. +## 5$bparms.ode[1:7], : Shapiro-Wilk test for standardized residuals: p = 0.00785</code></pre> +<div class="sourceCode" id="cb65"><html><body><pre class="r"><span class="fu"><a href="../../reference/plot.mkinfit.html">plot_sep</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png" width="700"></p> <p>As expected, the residual plots for Z0 and Z3 are more random than in the case of the all SFO model for which they were shown above. In conclusion, the model is proposed as the best-fit model for the dataset from Appendix 7 of the FOCUS report.</p> <p>A graphical representation of the confidence intervals can finally be obtained.</p> -<div class="sourceCode" id="cb63"><html><body><pre class="r"><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb66"><html><body><pre class="r"><span class="fu"><a href="../../reference/mkinparplot.html">mkinparplot</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> <p><img src="FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png" width="700"></p> <p>The endpoints obtained with this model are</p> -<div class="sourceCode" id="cb64"><html><body><pre class="r"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb67"><html><body><pre class="r"><span class="fu"><a href="../../reference/endpoints.html">endpoints</a></span>(<span class="no">m.Z.mkin.5a</span>)</pre></body></html></div> <pre><code>## $ff ## Z0_free Z2_Z3 Z2_sink Z3_free ## 1.00000 0.53656 0.46344 1.00000 ## ## $SFORB ## Z0_b1 Z0_b2 Z3_b1 Z3_b2 -## 2.4471337 0.0075125 0.0800071 0.0000000 +## 2.4471358 0.0075126 0.0800073 0.0000000 ## ## $distimes -## DT50 DT90 DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 -## Z0 0.3043 1.1848 0.28325 92.266 NA NA -## Z1 1.5148 5.0320 NA NA NA NA -## Z2 1.6414 5.4526 NA NA NA NA -## Z3 NA NA NA NA 8.6636 Inf</code></pre> +## DT50 DT90 DT50back DT50_Z0_b1 DT50_Z0_b2 DT50_Z3_b1 DT50_Z3_b2 +## Z0 0.3043 1.1848 0.35666 0.28325 92.265 NA NA +## Z1 1.5148 5.0320 NA NA NA NA NA +## Z2 1.6414 5.4526 NA NA NA NA NA +## Z3 NA NA NA NA NA 8.6636 Inf</code></pre> <p>It is clear the degradation rate of Z3 towards the end of the experiment is very low as DT50_Z3_b2 (the second Eigenvalue of the system of two differential equations representing the SFORB system for Z3, corresponding to the slower rate constant of the DFOP model) is reported to be infinity. However, this appears to be a feature of the data.</p> </div> <div id="references" class="section level1"> diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png Binary files differindex 96738dd0..d3702fb6 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_10-1.png diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png Binary files differindex 4f3c2554..4a6fce4f 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11-1.png diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png Binary files differindex b8c3ed26..dd6537b7 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11a-1.png diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png Binary files differindex 132a7317..b986c30b 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_11b-1.png diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png Binary files differindex b25bf26a..47d806c0 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_6-1.png diff --git a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png Binary files differindex dd5d89cd..0c698299 100644 --- a/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png +++ b/docs/dev/articles/web_only/FOCUS_Z_files/figure-html/FOCUS_2006_Z_fits_9-1.png diff --git a/docs/dev/articles/web_only/NAFTA_examples.html b/docs/dev/articles/web_only/NAFTA_examples.html index 12499452..09f40a7f 100644 --- a/docs/dev/articles/web_only/NAFTA_examples.html +++ b/docs/dev/articles/web_only/NAFTA_examples.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/web_only/NAFTA_examples.rmd"><code>vignettes/web_only/NAFTA_examples.rmd</code></a></small> <div class="hidden name"><code>NAFTA_examples.rmd</code></div> @@ -138,23 +138,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 95.8401 4.67e-21 92.245 99.4357 -## k_parent_sink 0.0102 3.92e-12 0.009 0.0117 -## sigma 4.8230 3.81e-06 3.214 6.4318 +## Estimate Pr(>t) Lower Upper +## parent_0 95.8401 4.67e-21 92.245 99.4357 +## k_parent 0.0102 3.92e-12 0.009 0.0117 +## sigma 4.8230 3.81e-06 3.214 6.4318 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.91e+01 1.02e+02 -## k__iore_parent_sink 1.54e-05 NA 4.08e-06 5.84e-05 -## N_parent 2.57e+00 NA 2.25e+00 2.89e+00 -## sigma 1.68e+00 NA 1.12e+00 2.24e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.01e+02 NA 9.91e+01 1.02e+02 +## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05 +## N_parent 2.57e+00 NA 2.25e+00 2.89e+00 +## sigma 1.68e+00 NA 1.12e+00 2.24e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 ## k1 2.67e-02 5.05e-06 0.0243 0.0295 -## k2 2.86e-12 5.00e-01 0.0000 Inf +## k2 2.17e-12 5.00e-01 0.0000 Inf ## g 6.47e-01 3.67e-06 0.6248 0.6677 ## sigma 1.27e+00 8.91e-06 0.8395 1.6929 ## @@ -163,7 +163,7 @@ ## DT50 DT90 DT50_rep ## SFO 67.7 2.25e+02 6.77e+01 ## IORE 58.2 1.07e+03 3.22e+02 -## DFOP 55.5 4.42e+11 2.42e+11 +## DFOP 55.5 5.83e+11 3.20e+11 ## ## Representative half-life: ## [1] 321.51</code></pre> @@ -186,23 +186,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.497 2.32e-24 94.85271 98.14155 -## k_parent_sink 0.008 3.42e-14 0.00737 0.00869 -## sigma 2.295 1.22e-05 1.47976 3.11036 +## Estimate Pr(>t) Lower Upper +## parent_0 96.497 2.32e-24 94.85271 98.14155 +## k_parent 0.008 3.42e-14 0.00737 0.00869 +## sigma 2.295 1.22e-05 1.47976 3.11036 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01 -## k__iore_parent_sink 1.53e-04 6.50e-03 7.21e-05 3.26e-04 -## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00 -## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01 +## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04 +## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00 +## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 ## k1 1.55e-02 4.10e-04 0.0143 0.0167 -## k2 1.16e-11 5.00e-01 0.0000 Inf +## k2 1.04e-11 5.00e-01 0.0000 Inf ## g 6.89e-01 2.92e-03 0.6626 0.7142 ## sigma 6.48e-01 2.38e-05 0.4147 0.8813 ## @@ -211,7 +211,7 @@ ## DT50 DT90 DT50_rep ## SFO 86.6 2.88e+02 8.66e+01 ## IORE 85.5 7.17e+02 2.16e+02 -## DFOP 83.6 9.80e+10 5.98e+10 +## DFOP 83.6 1.09e+11 6.67e+10 ## ## Representative half-life: ## [1] 215.87</code></pre> @@ -234,23 +234,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7759 7.29e-24 92.3478 97.2039 -## k_parent_sink 0.0179 8.02e-16 0.0166 0.0194 -## sigma 3.0696 3.81e-06 2.0456 4.0936 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7759 7.29e-24 92.3478 97.2039 +## k_parent 0.0179 8.02e-16 0.0166 0.0194 +## sigma 3.0696 3.81e-06 2.0456 4.0936 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 97.12446 2.63e-26 95.62461 98.62431 -## k__iore_parent_sink 0.00252 1.95e-03 0.00134 0.00472 -## N_parent 1.49587 4.07e-13 1.33896 1.65279 -## sigma 1.59698 5.05e-06 1.06169 2.13227 +## Estimate Pr(>t) Lower Upper +## parent_0 97.12446 2.63e-26 95.62461 98.62431 +## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472 +## N_parent 1.49587 4.07e-13 1.33896 1.65279 +## sigma 1.59698 5.05e-06 1.06169 2.13227 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.66e+01 1.57e-25 95.3476 97.8979 ## k1 2.55e-02 7.33e-06 0.0233 0.0278 -## k2 4.90e-11 5.00e-01 0.0000 Inf +## k2 3.88e-11 5.00e-01 0.0000 Inf ## g 8.61e-01 7.55e-06 0.8314 0.8867 ## sigma 1.46e+00 6.93e-06 0.9661 1.9483 ## @@ -259,7 +259,7 @@ ## DT50 DT90 DT50_rep ## SFO 38.6 1.28e+02 3.86e+01 ## IORE 34.0 1.77e+02 5.32e+01 -## DFOP 34.1 6.66e+09 1.41e+10 +## DFOP 34.1 8.42e+09 1.79e+10 ## ## Representative half-life: ## [1] 53.17</code></pre> @@ -282,23 +282,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.41796 4.80e-53 93.32245 99.51347 -## k_parent_sink 0.00735 7.64e-21 0.00641 0.00843 -## sigma 7.94557 1.83e-15 6.46713 9.42401 +## Estimate Pr(>t) Lower Upper +## parent_0 96.41796 4.80e-53 93.32245 99.51347 +## k_parent 0.00735 7.64e-21 0.00641 0.00843 +## sigma 7.94557 1.83e-15 6.46713 9.42401 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.92e+01 NA 9.55e+01 1.03e+02 -## k__iore_parent_sink 1.60e-05 NA 1.45e-07 1.77e-03 -## N_parent 2.45e+00 NA 1.35e+00 3.54e+00 -## sigma 7.42e+00 NA 6.04e+00 8.80e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.92e+01 NA 9.55e+01 1.03e+02 +## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03 +## N_parent 2.45e+00 NA 1.35e+00 3.54e+00 +## sigma 7.42e+00 NA 6.04e+00 8.80e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 1.97e-10 5.00e-01 0.0000 Inf +## k2 2.30e-10 5.00e-01 0.0000 Inf ## g 6.06e-01 2.19e-01 0.4826 0.7178 ## sigma 7.40e+00 2.97e-15 6.0201 8.7754 ## @@ -307,7 +307,7 @@ ## DT50 DT90 DT50_rep ## SFO 94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 6.97e+09 3.52e+09 +## DFOP 96.4 5.95e+09 3.01e+09 ## ## Representative half-life: ## [1] 454.55</code></pre> @@ -320,7 +320,7 @@ <h2 class="hasAnchor"> <a href="#example-on-page-8" class="anchor"></a>Example on page 8</h2> <p>For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.</p> -<div class="sourceCode" id="cb25"><html><body><pre class="r"><span class="no">p8</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p8"</span>]], <span class="kw">parms.ini</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">k__iore_parent_sink</span> <span class="kw">=</span> <span class="fl">1e-3</span>))</pre></body></html></div> +<div class="sourceCode" id="cb25"><html><body><pre class="r"><span class="no">p8</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p8"</span>]], <span class="kw">parms.ini</span> <span class="kw">=</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="kw">k__iore_parent</span> <span class="kw">=</span> <span class="fl">1e-3</span>))</pre></body></html></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <div class="sourceCode" id="cb28"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p8</span>)</pre></body></html></div> @@ -335,17 +335,17 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.16549 6.53e-29 83.37344 92.95754 -## k_parent_sink 0.00803 1.67e-13 0.00674 0.00957 -## sigma 7.44786 4.17e-10 5.66209 9.23363 +## Estimate Pr(>t) Lower Upper +## parent_0 88.16549 6.53e-29 83.37344 92.95754 +## k_parent 0.00803 1.67e-13 0.00674 0.00957 +## sigma 7.44786 4.17e-10 5.66209 9.23363 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02 -## k__iore_parent_sink 6.14e-05 3.20e-02 2.12e-05 1.78e-04 -## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00 -## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02 +## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04 +## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00 +## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper @@ -387,23 +387,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.1933 3.06e-12 79.9447 96.4419 -## k_parent_sink 0.0409 2.07e-07 0.0324 0.0516 -## sigma 7.2429 3.92e-05 4.4768 10.0090 +## Estimate Pr(>t) Lower Upper +## parent_0 88.1933 3.06e-12 79.9447 96.4419 +## k_parent 0.0409 2.07e-07 0.0324 0.0516 +## sigma 7.2429 3.92e-05 4.4768 10.0090 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02 -## k__iore_parent_sink 1.93e-05 1.13e-01 3.49e-06 1.06e-04 -## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00 -## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02 +## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04 +## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00 +## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.85e+01 2.54e-20 97.390 99.672 ## k1 1.38e-01 3.52e-05 0.131 0.146 -## k2 6.02e-13 5.00e-01 0.000 Inf +## k2 6.69e-13 5.00e-01 0.000 Inf ## g 6.52e-01 8.13e-06 0.642 0.661 ## sigma 7.88e-01 6.13e-02 0.481 1.095 ## @@ -412,7 +412,7 @@ ## DT50 DT90 DT50_rep ## SFO 16.9 5.63e+01 1.69e+01 ## IORE 11.6 3.37e+02 1.01e+02 -## DFOP 10.5 2.07e+12 1.15e+12 +## DFOP 10.5 1.86e+12 1.04e+12 ## ## Representative half-life: ## [1] 101.43</code></pre> @@ -422,16 +422,11 @@ <h2 class="hasAnchor"> <a href="#example-on-page-9-lower-panel" class="anchor"></a>Example on page 9, lower panel</h2> <div class="sourceCode" id="cb37"><html><body><pre class="r"><span class="no">p9b</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p9b"</span>]])</pre></body></html></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb44"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p9b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb40"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p9b</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p9b-1.png" width="700"></p> -<div class="sourceCode" id="cb45"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p9b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb41"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p9b</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 35.64867 23.22334 35.64867 @@ -441,24 +436,24 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 2.15e-19 93.178 96.2464 -## k_parent_sink 0.0389 4.47e-14 0.037 0.0408 -## sigma 1.5957 1.28e-04 0.932 2.2595 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7123 2.15e-19 93.178 96.2464 +## k_parent 0.0389 4.47e-14 0.037 0.0408 +## sigma 1.5957 1.28e-04 0.932 2.2595 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 93.863 2.32e-18 92.4565 95.269 -## k__iore_parent_sink 0.127 1.85e-02 0.0504 0.321 -## N_parent 0.711 1.88e-05 0.4843 0.937 -## sigma 1.288 1.76e-04 0.7456 1.830 +## Estimate Pr(>t) Lower Upper +## parent_0 93.863 2.32e-18 92.4565 95.269 +## k__iore_parent 0.127 1.85e-02 0.0504 0.321 +## N_parent 0.711 1.88e-05 0.4843 0.937 +## sigma 1.288 1.76e-04 0.7456 1.830 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 94.7123 1.61e-16 93.1355 96.2891 ## k1 0.0389 1.43e-06 0.0312 0.0485 ## k2 0.0389 6.67e-03 0.0186 0.0812 -## g 0.7742 NaN NA NA +## g 0.7742 5.00e-01 0.0000 1.0000 ## sigma 1.5957 2.50e-04 0.9135 2.2779 ## ## @@ -475,12 +470,12 @@ <div id="example-on-page-10" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-10" class="anchor"></a>Example on page 10</h2> -<div class="sourceCode" id="cb47"><html><body><pre class="r"><span class="no">p10</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p10"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb43"><html><body><pre class="r"><span class="no">p10</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p10"</span>]])</pre></body></html></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb50"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p10</span>)</pre></body></html></div> +<div class="sourceCode" id="cb46"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p10</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p10-1.png" width="700"></p> -<div class="sourceCode" id="cb51"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p10</span>)</pre></body></html></div> +<div class="sourceCode" id="cb47"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p10</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 899.4089 336.4348 899.4089 @@ -490,25 +485,25 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 6.42e-11 91.9259 111.5371 -## k_parent_sink 0.0495 1.70e-07 0.0404 0.0607 -## sigma 8.0152 1.28e-04 4.6813 11.3491 +## Estimate Pr(>t) Lower Upper +## parent_0 101.7315 6.42e-11 91.9259 111.5371 +## k_parent 0.0495 1.70e-07 0.0404 0.0607 +## sigma 8.0152 1.28e-04 4.6813 11.3491 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.86 3.32e-12 90.848 102.863 -## k__iore_parent_sink 2.96 7.91e-02 0.687 12.761 -## N_parent 0.00 5.00e-01 -0.372 0.372 -## sigma 4.90 1.77e-04 2.837 6.968 +## Estimate Pr(>t) Lower Upper +## parent_0 96.86 3.32e-12 90.848 102.863 +## k__iore_parent 2.96 7.91e-02 0.687 12.761 +## N_parent 0.00 5.00e-01 -0.372 0.372 +## sigma 4.90 1.77e-04 2.837 6.968 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 1.41e-09 91.6534 111.8097 -## k1 0.0495 6.42e-04 0.0301 0.0814 -## k2 0.0495 1.66e-02 0.0200 0.1225 -## g 0.6634 5.00e-01 0.0000 1.0000 -## sigma 8.0152 2.50e-04 4.5886 11.4418 +## Estimate Pr(>t) Lower Upper +## parent_0 101.7315 1.41e-09 91.6534 111.810 +## k1 0.0495 6.48e-04 0.0303 0.081 +## k2 0.0495 1.67e-02 0.0201 0.122 +## g 0.6634 5.00e-01 0.0000 1.000 +## sigma 8.0152 2.50e-04 4.5886 11.442 ## ## ## DTx values: @@ -528,12 +523,12 @@ <div id="example-on-page-11" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-11" class="anchor"></a>Example on page 11</h2> -<div class="sourceCode" id="cb53"><html><body><pre class="r"><span class="no">p11</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p11"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb49"><html><body><pre class="r"><span class="no">p11</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p11"</span>]])</pre></body></html></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb56"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p11</span>)</pre></body></html></div> +<div class="sourceCode" id="cb52"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p11</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p11-1.png" width="700"></p> -<div class="sourceCode" id="cb57"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p11</span>)</pre></body></html></div> +<div class="sourceCode" id="cb53"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p11</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 579.6805 204.7932 144.7783 @@ -543,17 +538,17 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 -## k_parent_sink 0.00321 4.71e-05 0.00222 4.64e-03 -## sigma 6.43473 1.28e-04 3.75822 9.11e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 +## k_parent 0.00321 4.71e-05 0.00222 4.64e-03 +## sigma 6.43473 1.28e-04 3.75822 9.11e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.05e+02 NA 9.90e+01 1.10e+02 -## k__iore_parent_sink 3.11e-17 NA 1.35e-20 7.18e-14 -## N_parent 8.36e+00 NA 6.62e+00 1.01e+01 -## sigma 3.82e+00 NA 2.21e+00 5.44e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.05e+02 NA 9.90e+01 1.10e+02 +## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14 +## N_parent 8.36e+00 NA 6.62e+00 1.01e+01 +## sigma 3.82e+00 NA 2.21e+00 5.44e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper @@ -571,7 +566,7 @@ ## DFOP 4.21e+11 2.64e+12 9.56e+11 ## ## Representative half-life: -## [1] 41148169</code></pre> +## [1] 41148171</code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> @@ -582,14 +577,14 @@ <div id="example-on-page-12-upper-panel" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-12-upper-panel" class="anchor"></a>Example on page 12, upper panel</h2> -<div class="sourceCode" id="cb59"><html><body><pre class="r"><span class="no">p12a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p12a"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb55"><html><body><pre class="r"><span class="no">p12a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p12a"</span>]])</pre></body></html></div> <pre><code>## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance ## matrix</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb63"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p12a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb59"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p12a</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p12a-1.png" width="700"></p> -<div class="sourceCode" id="cb64"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p12a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb60"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p12a</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 695.4440 220.0685 695.4440 @@ -599,23 +594,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 8.75e-12 92.461 108.581 -## k_parent_sink 0.124 3.61e-08 0.104 0.148 -## sigma 7.048 1.28e-04 4.116 9.980 +## Estimate Pr(>t) Lower Upper +## parent_0 100.521 8.75e-12 92.461 108.581 +## k_parent 0.124 3.61e-08 0.104 0.148 +## sigma 7.048 1.28e-04 4.116 9.980 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.823 NA NA NA -## k__iore_parent_sink 2.436 NA NA NA -## N_parent 0.263 NA NA NA -## sigma 3.965 NA NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 96.823 NA NA NA +## k__iore_parent 2.436 NA NA NA +## N_parent 0.263 NA NA NA +## sigma 3.965 NA NA NA ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 100.521 2.74e-10 92.2366 108.805 -## k1 0.124 5.74e-06 0.0958 0.161 -## k2 0.124 6.61e-02 0.0319 0.484 +## k1 0.124 5.75e-06 0.0958 0.161 +## k2 0.124 6.72e-02 0.0319 0.484 ## g 0.877 5.00e-01 0.0000 1.000 ## sigma 7.048 2.50e-04 4.0349 10.061 ## @@ -632,7 +627,7 @@ <div id="example-on-page-12-lower-panel" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-12-lower-panel" class="anchor"></a>Example on page 12, lower panel</h2> -<div class="sourceCode" id="cb66"><html><body><pre class="r"><span class="no">p12b</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p12b"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb62"><html><body><pre class="r"><span class="no">p12b</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p12b"</span>]])</pre></body></html></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> <pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> @@ -643,9 +638,9 @@ ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb76"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p12b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb72"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p12b</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p12b-1.png" width="700"></p> -<div class="sourceCode" id="cb77"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p12b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb73"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p12b</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 58.90242 19.06353 58.90242 @@ -655,17 +650,17 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 0.00039 85.9388 109.4292 -## k_parent_sink 0.0589 0.00261 0.0431 0.0805 -## sigma 3.4323 0.04356 -1.2377 8.1023 +## Estimate Pr(>t) Lower Upper +## parent_0 97.6840 0.00039 85.9388 109.4292 +## k_parent 0.0589 0.00261 0.0431 0.0805 +## sigma 3.4323 0.04356 -1.2377 8.1023 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.523 0.0055 74.539157 116.51 -## k__iore_parent_sink 0.333 0.1433 0.000717 154.57 -## N_parent 0.568 0.0677 -0.989464 2.13 -## sigma 1.953 0.0975 -5.893100 9.80 +## Estimate Pr(>t) Lower Upper +## parent_0 95.523 0.0055 74.539157 116.51 +## k__iore_parent 0.333 0.1433 0.000717 154.57 +## N_parent 0.568 0.0677 -0.989464 2.13 +## sigma 1.953 0.0975 -5.893100 9.80 ## ## $DFOP ## Estimate Pr(>t) Lower Upper @@ -688,16 +683,12 @@ <div id="example-on-page-13" class="section level2"> <h2 class="hasAnchor"> <a href="#example-on-page-13" class="anchor"></a>Example on page 13</h2> -<div class="sourceCode" id="cb79"><html><body><pre class="r"><span class="no">p13</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p13"</span>]])</pre></body></html></div> -<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> -<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is -## doubtful</code></pre> +<div class="sourceCode" id="cb75"><html><body><pre class="r"><span class="no">p13</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p13"</span>]])</pre></body></html></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb85"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p13</span>)</pre></body></html></div> +<div class="sourceCode" id="cb78"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p13</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p13-1.png" width="700"></p> -<div class="sourceCode" id="cb86"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p13</span>)</pre></body></html></div> +<div class="sourceCode" id="cb79"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p13</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 174.5971 142.3951 174.5971 @@ -707,24 +698,24 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 92.73500 5.99e-17 89.61936 95.85065 -## k_parent_sink 0.00258 2.42e-09 0.00223 0.00299 -## sigma 3.41172 7.07e-05 2.05455 4.76888 +## Estimate Pr(>t) Lower Upper +## parent_0 92.73500 5.99e-17 89.61936 95.85065 +## k_parent 0.00258 2.42e-09 0.00223 0.00299 +## sigma 3.41172 7.07e-05 2.05455 4.76888 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 91.6016 6.34e-16 88.53086 94.672 -## k__iore_parent_sink 0.0396 2.36e-01 0.00207 0.759 -## N_parent 0.3541 1.46e-01 -0.35153 1.060 -## sigma 3.0811 9.64e-05 1.84296 4.319 +## Estimate Pr(>t) Lower Upper +## parent_0 91.6016 6.34e-16 88.53086 94.672 +## k__iore_parent 0.0396 2.36e-01 0.00207 0.759 +## N_parent 0.3541 1.46e-01 -0.35153 1.060 +## sigma 3.0811 9.64e-05 1.84296 4.319 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 92.73500 9.25e-15 8.95e+01 9.59e+01 -## k1 0.00258 4.28e-01 1.70e-08 3.92e+02 +## k1 0.00258 4.28e-01 1.45e-08 4.61e+02 ## k2 0.00258 3.69e-08 2.20e-03 3.03e-03 -## g 0.00442 5.00e-01 NA NA +## g 0.00442 5.00e-01 0.00e+00 1.00e+00 ## sigma 3.41172 1.35e-04 2.02e+00 4.80e+00 ## ## @@ -741,16 +732,16 @@ <div id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="section level1"> <h1 class="hasAnchor"> <a href="#dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="anchor"></a>DT50 not observed in the study and DFOP problems in PestDF</h1> -<div class="sourceCode" id="cb88"><html><body><pre class="r"><span class="no">p14</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p14"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb81"><html><body><pre class="r"><span class="no">p14</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p14"</span>]])</pre></body></html></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb94"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p14</span>)</pre></body></html></div> +<div class="sourceCode" id="cb87"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p14</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p14-1.png" width="700"></p> -<div class="sourceCode" id="cb95"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p14</span>)</pre></body></html></div> +<div class="sourceCode" id="cb88"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p14</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 48.43249 28.67746 27.26248 @@ -760,23 +751,23 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 -## k_parent_sink 0.00279 3.75e-15 0.00256 3.04e-03 -## sigma 1.55616 3.81e-06 1.03704 2.08e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 +## k_parent 0.00279 3.75e-15 0.00256 3.04e-03 +## sigma 1.55616 3.81e-06 1.03704 2.08e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.00e+02 NA NaN NaN -## k__iore_parent_sink 9.44e-08 NA NaN NaN -## N_parent 3.31e+00 NA NaN NaN -## sigma 1.20e+00 NA 0.796 1.6 +## Estimate Pr(>t) Lower Upper +## parent_0 1.00e+02 NA NaN NaN +## k__iore_parent 9.44e-08 NA NaN NaN +## N_parent 3.31e+00 NA NaN NaN +## sigma 1.20e+00 NA 0.796 1.6 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1 9.53e-03 1.20e-01 0.00638 0.0143 -## k2 7.29e-12 5.00e-01 0.00000 Inf +## k2 7.70e-12 5.00e-01 0.00000 Inf ## g 3.98e-01 2.19e-01 0.30481 0.4998 ## sigma 1.17e+00 7.68e-06 0.77406 1.5610 ## @@ -785,7 +776,7 @@ ## DT50 DT90 DT50_rep ## SFO 2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 -## DFOP 2.54e+10 2.46e+11 9.51e+10 +## DFOP 2.41e+10 2.33e+11 9.00e+10 ## ## Representative half-life: ## [1] 6697.44</code></pre> @@ -794,17 +785,16 @@ <div id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="section level1"> <h1 class="hasAnchor"> <a href="#n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="anchor"></a>N is less than 1 and DFOP fraction parameter is below zero</h1> -<div class="sourceCode" id="cb97"><html><body><pre class="r"><span class="no">p15a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p15a"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb90"><html><body><pre class="r"><span class="no">p15a</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p15a"</span>]])</pre></body></html></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb104"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p15a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb96"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p15a</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p15a-1.png" width="700"></p> -<div class="sourceCode" id="cb105"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p15a</span>)</pre></body></html></div> +<div class="sourceCode" id="cb97"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p15a</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 245.5248 135.0132 245.5248 @@ -814,25 +804,25 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 2.00e-15 94.32049 101.615 -## k_parent_sink 0.00952 4.93e-09 0.00824 0.011 -## sigma 4.18778 1.28e-04 2.44588 5.930 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96751 2.00e-15 94.32049 101.615 +## k_parent 0.00952 4.93e-09 0.00824 0.011 +## sigma 4.18778 1.28e-04 2.44588 5.930 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.874 2.94e-15 92.937 98.811 -## k__iore_parent_sink 0.629 2.11e-01 0.044 8.982 -## N_parent 0.000 5.00e-01 -0.642 0.642 -## sigma 3.105 1.78e-04 1.795 4.416 +## Estimate Pr(>t) Lower Upper +## parent_0 95.874 2.94e-15 92.937 98.811 +## k__iore_parent 0.629 2.11e-01 0.044 8.982 +## N_parent 0.000 5.00e-01 -0.642 0.642 +## sigma 3.105 1.78e-04 1.795 4.416 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96752 2.85e-13 94.21914 101.7159 -## k1 0.00952 6.80e-02 0.00277 0.0327 -## k2 0.00952 3.82e-06 0.00902 0.0100 -## g 0.17247 NaN NA NA -## sigma 4.18778 2.50e-04 2.39747 5.9781 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96752 NA 94.21914 101.7159 +## k1 0.00952 NA 0.00241 0.0377 +## k2 0.00952 NA 0.00747 0.0121 +## g 0.17247 NA NA NA +## sigma 4.18778 NA 2.39747 5.9781 ## ## ## DTx values: @@ -843,16 +833,16 @@ ## ## Representative half-life: ## [1] 41.33</code></pre> -<div class="sourceCode" id="cb107"><html><body><pre class="r"><span class="no">p15b</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p15b"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb99"><html><body><pre class="r"><span class="no">p15b</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p15b"</span>]])</pre></body></html></div> <pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> <pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> -<div class="sourceCode" id="cb113"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p15b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb105"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p15b</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p15b-1.png" width="700"></p> -<div class="sourceCode" id="cb114"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p15b</span>)</pre></body></html></div> +<div class="sourceCode" id="cb106"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p15b</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 106.91629 68.55574 106.91629 @@ -862,25 +852,25 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 -## k_parent_sink 4.86e-03 2.48e-10 0.00435 5.42e-03 -## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 +## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03 +## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51349 102.14 -## k__iore_parent_sink 0.38 3.22e-01 0.00352 41.05 -## N_parent 0.00 5.00e-01 -1.07695 1.08 -## sigma 2.21 2.57e-04 1.23245 3.19 +## Estimate Pr(>t) Lower Upper +## parent_0 99.83 1.81e-16 97.51348 102.14 +## k__iore_parent 0.38 3.22e-01 0.00352 41.05 +## N_parent 0.00 5.00e-01 -1.07696 1.08 +## sigma 2.21 2.57e-04 1.23245 3.19 ## ## $DFOP ## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.82e+01 1.04e+02 -## k1 4.86e-03 NA 6.75e-04 3.49e-02 -## k2 4.86e-03 NA 3.37e-03 6.99e-03 +## parent_0 1.01e+02 NA 98.24464 1.04e+02 +## k1 4.86e-03 NA 0.00068 3.47e-02 +## k2 4.86e-03 NA 0.00338 6.99e-03 ## g 1.50e-01 NA NA NA -## sigma 2.76e+00 NA 1.58e+00 3.94e+00 +## sigma 2.76e+00 NA 1.58208 3.94e+00 ## ## ## DTx values: @@ -896,14 +886,14 @@ <div id="the-dfop-fraction-parameter-is-greater-than-1" class="section level1"> <h1 class="hasAnchor"> <a href="#the-dfop-fraction-parameter-is-greater-than-1" class="anchor"></a>The DFOP fraction parameter is greater than 1</h1> -<div class="sourceCode" id="cb116"><html><body><pre class="r"><span class="no">p16</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p16"</span>]])</pre></body></html></div> +<div class="sourceCode" id="cb108"><html><body><pre class="r"><span class="no">p16</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/nafta.html">nafta</a></span>(<span class="no">NAFTA_SOP_Attachment</span><span class="kw">[[</span><span class="st">"p16"</span>]])</pre></body></html></div> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The representative half-life of the IORE model is longer than the one corresponding</code></pre> <pre><code>## to the terminal degradation rate found with the DFOP model.</code></pre> <pre><code>## The representative half-life obtained from the DFOP model may be used</code></pre> -<div class="sourceCode" id="cb121"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p16</span>)</pre></body></html></div> +<div class="sourceCode" id="cb113"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/plot.html">plot</a></span>(<span class="no">p16</span>)</pre></body></html></div> <p><img src="NAFTA_examples_files/figure-html/p16-1.png" width="700"></p> -<div class="sourceCode" id="cb122"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p16</span>)</pre></body></html></div> +<div class="sourceCode" id="cb114"><html><body><pre class="r"><span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="no">p16</span>)</pre></body></html></div> <pre><code>## Sums of squares: ## SFO IORE DFOP ## 3831.804 2062.008 1550.980 @@ -913,22 +903,22 @@ ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 71.953 2.33e-13 60.509 83.40 -## k_parent_sink 0.159 4.86e-05 0.102 0.25 -## sigma 11.302 1.25e-08 8.308 14.30 +## Estimate Pr(>t) Lower Upper +## parent_0 71.953 2.33e-13 60.509 83.40 +## k_parent 0.159 4.86e-05 0.102 0.25 +## sigma 11.302 1.25e-08 8.308 14.30 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972 -## k__iore_parent_sink 4.55e-04 2.16e-01 3.48e-05 0.00595 -## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046 -## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062 +## Estimate Pr(>t) Lower Upper +## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972 +## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595 +## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046 +## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 88.5333 7.40e-18 79.9836 97.083 -## k1 18.5561 5.00e-01 0.0000 Inf +## k1 18.5560 5.00e-01 0.0000 Inf ## k2 0.0776 1.41e-05 0.0518 0.116 ## g 0.4733 1.41e-09 0.3674 0.582 ## sigma 7.1902 2.11e-08 5.2785 9.102 diff --git a/docs/dev/articles/web_only/benchmarks.html b/docs/dev/articles/web_only/benchmarks.html index 9e53f113..30b7a879 100644 --- a/docs/dev/articles/web_only/benchmarks.html +++ b/docs/dev/articles/web_only/benchmarks.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Benchmark timings for mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/web_only/benchmarks.rmd"><code>vignettes/web_only/benchmarks.rmd</code></a></small> <div class="hidden name"><code>benchmarks.rmd</code></div> @@ -132,13 +132,20 @@ <span class="no">DFOP_SFO</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>( <span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"FOMC"</span>, <span class="st">"m1"</span>), <span class="kw">m1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>)) -<span class="no">t3</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]] -<span class="no">t4</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>), +<span class="no">t3</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]]</pre></body></html></div> +<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): +## Shapiro-Wilk test for standardized residuals: p = 0.0165</code></pre> +<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): +## Shapiro-Wilk test for standardized residuals: p = 0.0499 + +## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): +## Shapiro-Wilk test for standardized residuals: p = 0.0499</code></pre> +<div class="sourceCode" id="cb5"><html><body><pre class="r"><span class="no">t4</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]] <span class="no">t5</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_SFO</span>, <span class="no">FOMC_SFO</span>, <span class="no">DFOP_SFO</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">FOCUS_D</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]]</pre></body></html></div> <p>Two metabolites, synthetic data:</p> -<div class="sourceCode" id="cb3"><html><body><pre class="r"><span class="no">m_synth_SFO_lin</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>), +<div class="sourceCode" id="cb6"><html><body><pre class="r"><span class="no">m_synth_SFO_lin</span> <span class="kw"><-</span> <span class="fu"><a href="../../reference/mkinmod.html">mkinmod</a></span>(<span class="kw">parent</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M1"</span>), <span class="kw">M1</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>, <span class="st">"M2"</span>), <span class="kw">M2</span> <span class="kw">=</span> <span class="fu"><a href="../../reference/mkinsub.html">mkinsub</a></span>(<span class="st">"SFO"</span>), <span class="kw">use_of_ff</span> <span class="kw">=</span> <span class="st">"max"</span>, <span class="kw">quiet</span> <span class="kw">=</span> <span class="fl">TRUE</span>) @@ -153,9 +160,10 @@ <span class="no">DFOP_par_c</span> <span class="kw"><-</span> <span class="no">synthetic_data_for_UBA_2014</span><span class="kw">[[</span><span class="fl">12</span>]]$<span class="no">data</span> <span class="no">t6</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]] -<span class="no">t7</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]] - -<span class="no">t8</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>), +<span class="no">t7</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>)))<span class="kw">[[</span><span class="st">"elapsed"</span>]]</pre></body></html></div> +<pre><code>## Warning in mkinfit(models[[model_index]], datasets[[dataset_index]], ...): +## Shapiro-Wilk test for standardized residuals: p = 0.000174</code></pre> +<div class="sourceCode" id="cb8"><html><body><pre class="r"><span class="no">t8</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_SFO_lin</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">SFO_lin_a</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]] <span class="no">t9</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"tc"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]] @@ -164,7 +172,7 @@ <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]] <span class="no">t11</span> <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/system.time.html">system.time</a></span>(<span class="fu">mmkin_bench</span>(<span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">m_synth_DFOP_par</span>), <span class="fu"><a href="https://rdrr.io/r/base/list.html">list</a></span>(<span class="no">DFOP_par_c</span>), <span class="kw">error_model</span> <span class="kw">=</span> <span class="st">"obs"</span>))<span class="kw">[[</span><span class="st">"elapsed"</span>]]</pre></body></html></div> -<div class="sourceCode" id="cb4"><html><body><pre class="r"><span class="no">mkin_benchmarks</span>[<span class="no">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(<span class="st">"t"</span>, <span class="fl">1</span>:<span class="fl">11</span>)] <span class="kw"><-</span> +<div class="sourceCode" id="cb9"><html><body><pre class="r"><span class="no">mkin_benchmarks</span>[<span class="no">system_string</span>, <span class="fu"><a href="https://rdrr.io/r/base/paste.html">paste0</a></span>(<span class="st">"t"</span>, <span class="fl">1</span>:<span class="fl">11</span>)] <span class="kw"><-</span> <span class="fu"><a href="https://rdrr.io/r/base/c.html">c</a></span>(<span class="no">t1</span>, <span class="no">t2</span>, <span class="no">t3</span>, <span class="no">t4</span>, <span class="no">t5</span>, <span class="no">t6</span>, <span class="no">t7</span>, <span class="no">t8</span>, <span class="no">t9</span>, <span class="no">t10</span>, <span class="no">t11</span>) <span class="fu"><a href="https://rdrr.io/r/base/save.html">save</a></span>(<span class="no">mkin_benchmarks</span>, <span class="kw">file</span> <span class="kw">=</span> <span class="st">"~/git/mkin/vignettes/web_only/mkin_benchmarks.rda"</span>)</pre></body></html></div> </div> @@ -216,8 +224,8 @@ </tr> <tr class="odd"> <td align="left">0.9.50.3</td> -<td align="right">1.746</td> -<td align="right">3.716</td> +<td align="right">1.707</td> +<td align="right">4.062</td> </tr> </tbody> </table> @@ -272,9 +280,9 @@ </tr> <tr class="odd"> <td align="left">0.9.50.3</td> -<td align="right">1.385</td> -<td align="right">6.562</td> -<td align="right">2.736</td> +<td align="right">1.372</td> +<td align="right">6.233</td> +<td align="right">2.779</td> </tr> </tbody> </table> @@ -350,12 +358,12 @@ </tr> <tr class="odd"> <td align="left">0.9.50.3</td> -<td align="right">0.760</td> -<td align="right">1.226</td> -<td align="right">1.455</td> -<td align="right">4.198</td> -<td align="right">2.007</td> -<td align="right">2.976</td> +<td align="right">0.768</td> +<td align="right">1.235</td> +<td align="right">1.302</td> +<td align="right">2.921</td> +<td align="right">2.078</td> +<td align="right">3.040</td> </tr> </tbody> </table> diff --git a/docs/dev/articles/web_only/compiled_models.html b/docs/dev/articles/web_only/compiled_models.html index 997e90ea..055d0646 100644 --- a/docs/dev/articles/web_only/compiled_models.html +++ b/docs/dev/articles/web_only/compiled_models.html @@ -101,7 +101,7 @@ <h1 data-toc-skip>Performance benefit by using compiled model definitions in mkin</h1> <h4 class="author">Johannes Ranke</h4> - <h4 class="date">2020-05-27</h4> + <h4 class="date">2020-10-08</h4> <small class="dont-index">Source: <a href="http://github.com/jranke/mkin/blob/master/vignettes/web_only/compiled_models.rmd"><code>vignettes/web_only/compiled_models.rmd</code></a></small> <div class="hidden name"><code>compiled_models.rmd</code></div> @@ -153,10 +153,10 @@ <span class="fu"><a href="https://rdrr.io/r/base/print.html">print</a></span>(<span class="st">"R package rbenchmark is not available"</span>) }</pre></body></html></div> <pre><code>## test replications relative elapsed -## 4 analytical 1 1.000 0.201 -## 3 deSolve, compiled 1 1.711 0.344 -## 2 Eigenvalue based 1 1.960 0.394 -## 1 deSolve, not compiled 1 39.881 8.016</code></pre> +## 4 analytical 1 1.000 0.195 +## 3 deSolve, compiled 1 1.769 0.345 +## 2 Eigenvalue based 1 2.087 0.407 +## 1 deSolve, not compiled 1 42.656 8.318</code></pre> <p>We see that using the compiled model is by more than a factor of 10 faster than using deSolve without compiled code.</p> </div> <div id="model-without-analytical-solution" class="section level2"> @@ -182,11 +182,11 @@ }</pre></body></html></div> <pre><code>## Successfully compiled differential equation model from auto-generated C code.</code></pre> <pre><code>## test replications relative elapsed -## 2 deSolve, compiled 1 1.000 0.467 -## 1 deSolve, not compiled 1 30.244 14.124</code></pre> -<p>Here we get a performance benefit of a factor of 30 using the version of the differential equation model compiled from C code!</p> +## 2 deSolve, compiled 1 1.000 0.474 +## 1 deSolve, not compiled 1 30.909 14.651</code></pre> +<p>Here we get a performance benefit of a factor of 31 using the version of the differential equation model compiled from C code!</p> <p>This vignette was built with mkin 0.9.50.3 on</p> -<pre><code>## R version 4.0.0 (2020-04-24) +<pre><code>## R version 4.0.2 (2020-06-22) ## Platform: x86_64-pc-linux-gnu (64-bit) ## Running under: Debian GNU/Linux 10 (buster)</code></pre> <pre><code>## CPU model: AMD Ryzen 7 1700 Eight-Core Processor</code></pre> diff --git a/docs/dev/index.html b/docs/dev/index.html index 37cd7d50..12df433a 100644 --- a/docs/dev/index.html +++ b/docs/dev/index.html @@ -127,7 +127,7 @@ <div id="documentation" class="section level2"> <h2 class="hasAnchor"> <a href="#documentation" class="anchor"></a>Documentation</h2> -<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin">jrwb.de</a> and <a href="http://jranke.github.io/mkin">github</a>. Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev">‘dev’ subdirectory</a>.</p> +<p>The HTML documentation of the latest version released to CRAN is available at <a href="https://pkgdown.jrwb.de/mkin/">jrwb.de</a> and <a href="https://jranke.github.io/mkin/">github</a>. Documentation of the development version is found in the <a href="https://pkgdown.jrwb.de/mkin/dev/">‘dev’ subdirectory</a>.</p> </div> <div id="features" class="section level2"> <h2 class="hasAnchor"> @@ -152,7 +152,7 @@ <div id="gui" class="section level2"> <h2 class="hasAnchor"> <a href="#gui" class="anchor"></a>GUI</h2> -<p>There is a graphical user interface that may be useful. Please refer to its <a href="http://kinfit.r-forge.r-project.org/gmkin_static">documentation page</a> for installation instructions and a manual.</p> +<p>There is a graphical user interface that may be useful. Please refer to its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> for installation instructions and a manual.</p> </div> <div id="news" class="section level2"> <h2 class="hasAnchor"> @@ -166,10 +166,10 @@ <p><code>mkin</code> could not have been written without me being introduced to regulatory fate modelling of pesticides by Adrian Gurney during my time at Harlan Laboratories Ltd (formerly RCC Ltd). <code>mkin</code> greatly profits from and largely follows the work done by the <a href="http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics">FOCUS Degradation Kinetics Workgroup</a>, as detailed in their guidance document from 2006, slightly updated in 2011 and in 2014.</p> <p>Also, it was inspired by the first version of KinGUI developed by BayerCropScience, which is based on the MatLab runtime environment.</p> <p>The companion package <a href="http://kinfit.r-forge.r-project.org/kinfit_static/index.html">kinfit</a> (now deprecated) was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=2">started in 2008</a> and <a href="https://cran.r-project.org/src/contrib/Archive/kinfit/">first published</a> on CRAN on 01 May 2010.</p> -<p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin">first CRAN version</a> on 18 May 2010.</p> +<p>The first <code>mkin</code> code was <a href="https://r-forge.r-project.org/scm/viewvc.php?view=rev&root=kinfit&revision=8">published on 11 May 2010</a> and the <a href="https://cran.r-project.org/src/contrib/Archive/mkin/">first CRAN version</a> on 18 May 2010.</p> <p>In 2011, Bayer Crop Science started to distribute an R based successor to KinGUI named KinGUII whose R code is based on <code>mkin</code>, but which added, among other refinements, a closed source graphical user interface (GUI), iteratively reweighted least squares (IRLS) optimisation of the variance for each of the observed variables, and Markov Chain Monte Carlo (MCMC) simulation functionality, similar to what is available e.g. in the <code>FME</code> package.</p> <p>Somewhat in parallel, Syngenta has sponsored the development of an <code>mkin</code> and KinGUII based GUI application called CAKE, which also adds IRLS and MCMC, is more limited in the model formulation, but puts more weight on usability. CAKE is available for download from the <a href="https://www.tessella.com/showcase/computer-assisted-kinetic-evaluation">CAKE website</a>, where you can also find a zip archive of the R scripts derived from <code>mkin</code>, published under the GPL license.</p> -<p>Finally, there is <a href="http://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> +<p>Finally, there is <a href="https://github.com/zhenglei-gao/KineticEval">KineticEval</a>, which contains a further development of the scripts used for KinGUII, so the different tools will hopefully be able to learn from each other in the future as well.</p> </div> <div id="references" class="section level2"> <h2 class="hasAnchor"> @@ -203,7 +203,7 @@ Ranke J, Wöltjen J, Meinecke S (2018) Comparison of software tools for kinetic </li> <li>Browse source code at <br><a href="http://github.com/jranke/mkin/">http://github.com/jranke/mkin/</a> </li> -<li>Report a bug at <br><a href="http://github.com/jranke/mkin/issues">http://github.com/jranke/mkin/issues</a> +<li>Report a bug at <br><a href="http://github.com/jranke/mkin/issues/">http://github.com/jranke/mkin/issues/</a> </li> </ul> </div> diff --git a/docs/dev/news/index.html b/docs/dev/news/index.html index 7e39aa46..dad1622d 100644 --- a/docs/dev/news/index.html +++ b/docs/dev/news/index.html @@ -141,15 +141,20 @@ <small>Source: <a href='http://github.com/jranke/mkin/blob/master/NEWS.md'><code>NEWS.md</code></a></small> </div> - <div id="mkin-0-9-50-3-unreleased" class="section level1"> + <div id="mkin-0-9-50-3" class="section level1"> <h1 class="page-header" data-toc-text="0.9.50.3"> -<a href="#mkin-0-9-50-3-unreleased" class="anchor"></a>mkin 0.9.50.3 (unreleased)<small> Unreleased </small> +<a href="#mkin-0-9-50-3" class="anchor"></a>mkin 0.9.50.3<small> Unreleased </small> </h1> <ul> <li><p>‘parms’: Add a method for mmkin objects</p></li> -<li><p>‘saemix_model’, ‘saemix_data’: Helper functions to fit nonlinear mixed-effects models for mmkin row objects using the saemix package</p></li> <li><p>‘mmkin’ and ‘confint(method = ’profile’): Use all cores detected by parallel::detectCores() per default</p></li> <li><p>‘confint(method = ’profile’): Choose accuracy based on ‘rel_tol’ argument, relative to the bounds obtained by the quadratic approximation</p></li> +<li><p>‘mkinfit’: Make ‘use_of_ff’ = “max” also the default for models specified using short names like “SFO” or “FOMC”</p></li> +<li><p>‘mkinfit’: Run ‘stats::shapiro.test()’ on standardized residuals and warn if p < 0.05</p></li> +<li><p>‘mkinfit’: ‘error_model_algorithm’ = ‘d_3’ does not fail if direct fitting fails, but reports that the results for the threestep algorithm are returned</p></li> +<li><p>‘mmkin’: Do not fail any more if one of the fits fails, but assign the try-error to the respective position in the mmkin object</p></li> +<li><p>‘mkinfit’: Ignore components of state.ini that do not correspond to state variables in the model</p></li> +<li><p>‘endpoints’: Back-calculate DT50 value from DT90 also for the biphasic models DFOP, HS and SFORB</p></li> </ul> </div> <div id="mkin-0-9-50-2-2020-05-12" class="section level1"> @@ -765,7 +770,7 @@ <a href="#mkin-0-9-27-2014-05-10" class="anchor"></a>mkin 0.9-27 (2014-05-10)<small> 2014-05-10 </small> </h1> <ul> -<li><p>Fork the GUI into a separate package <a href="http://github.com/jranke/gmkin">gmkin</a></p></li> +<li><p>Fork the GUI into a separate package <a href="https://github.com/jranke/gmkin">gmkin</a></p></li> <li><p>DESCRIPTION, NAMESPACE, TODO: Adapt and add copyright information</p></li> <li><p>Remove files belonging to the GUI</p></li> <li><p>Possibility to fit without parameter transformations, using bounds as implemented in FME</p></li> diff --git a/docs/dev/pkgdown.yml b/docs/dev/pkgdown.yml index a7e9e750..d80606cc 100644 --- a/docs/dev/pkgdown.yml +++ b/docs/dev/pkgdown.yml @@ -10,7 +10,7 @@ articles: NAFTA_examples: web_only/NAFTA_examples.html benchmarks: web_only/benchmarks.html compiled_models: web_only/compiled_models.html -last_built: 2020-05-27T06:53Z +last_built: 2020-10-08T07:26Z urls: reference: https://pkgdown.jrwb.de/mkin/reference article: https://pkgdown.jrwb.de/mkin/articles diff --git a/docs/dev/reference/AIC.mmkin.html b/docs/dev/reference/AIC.mmkin.html index 517aff12..a1418b82 100644 --- a/docs/dev/reference/AIC.mmkin.html +++ b/docs/dev/reference/AIC.mmkin.html @@ -73,7 +73,7 @@ same dataset." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -192,13 +192,13 @@ dataframe if there are several fits in the column).</p> <span class='co'># of parameters, the higher (worse) the AIC</span> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>])</div><div class='output co'>#> df AIC #> SFO 3 55.28197 -#> FOMC 4 57.28202 +#> FOMC 4 57.28211 #> DFOP 5 59.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>], <span class='kw'>k</span> <span class='kw'>=</span> <span class='fl'>0</span>) <span class='co'># If we do not penalize additional parameters, we get nearly the same</span></div><div class='output co'>#> df AIC #> SFO 3 49.28197 -#> FOMC 4 49.28202 +#> FOMC 4 49.28211 #> DFOP 5 49.28197</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>BIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS A"</span>]) <span class='co'># Comparing the BIC gives a very similar picture</span></div><div class='output co'>#> df BIC #> SFO 3 55.52030 -#> FOMC 4 57.59979 +#> FOMC 4 57.59987 #> DFOP 5 59.67918</div><div class='input'> <span class='co'># For FOCUS C, the more complex models fit better</span> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span>(<span class='no'>f</span>[, <span class='st'>"FOCUS C"</span>])</div><div class='output co'>#> df AIC diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index 48c3aabb..e7c69fc3 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index 8ed22157..f89f87c9 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -73,7 +73,7 @@ a decreasing rate constant." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 5053542a..4622ac80 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -73,7 +73,7 @@ between them." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index 9db7447c..26a34c73 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -73,7 +73,7 @@ a concentration dependent rate constant." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index 02b3cf22..930c2a97 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index 87d39b4e..845377a2 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -76,7 +76,7 @@ and no substance in the bound fraction." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/add_err.html b/docs/dev/reference/add_err.html index a4317cd7..852ae0d9 100644 --- a/docs/dev/reference/add_err.html +++ b/docs/dev/reference/add_err.html @@ -74,7 +74,7 @@ may depend on the predicted value and is specified as a standard deviation." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 074bed3e..5b683355 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -258,15 +258,15 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"SFO"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 71.8242430 93.1600766 -#> k_parent_sink 0.2109541 0.4440528 -#> sigma 1.9778868 7.3681380</div><div class='input'> +<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% +#> parent_0 71.8242430 93.1600766 +#> k_parent 0.2109541 0.4440528 +#> sigma 1.9778868 7.3681380</div><div class='input'> <span class='co'># \dontrun{</span> -<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>)</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 73.0641834 92.1392181 -#> k_parent_sink 0.2170293 0.4235348 -#> sigma 3.1307772 8.0628314</div><div class='input'> +<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>)</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> 2.5% 97.5% +#> parent_0 73.0641834 92.1392181 +#> k_parent 0.2170293 0.4235348 +#> sigma 3.1307772 8.0628314</div><div class='input'> <span class='co'># Set the number of cores for the profiling method for further examples</span> <span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/identical.html'>identical</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/Sys.getenv.html'>Sys.getenv</a></span>(<span class='st'>"NOT_CRAN"</span>), <span class='st'>"true"</span>)) { <span class='no'>n_cores</span> <span class='kw'><-</span> <span class='kw pkg'>parallel</span><span class='kw ns'>::</span><span class='fu'><a href='https://rdrr.io/r/parallel/detectCores.html'>detectCores</a></span>() - <span class='fl'>1</span> @@ -279,30 +279,29 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>SFO_SFO.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='no'>f_d_1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> user system elapsed -#> 3.610 1.071 3.378 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> +<span class='no'>f_d_1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> user system elapsed +#> 3.810 0.964 3.430 </div><div class='input'><span class='co'># Using more cores does not save much time here, as parent_0 takes up most of the time</span> <span class='co'># If we additionally exclude parent_0 (the confidence of which is often of</span> <span class='co'># minor interest), we get a nice performance improvement from about 50</span> <span class='co'># seconds to about 12 seconds if we use at least four cores</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>ci_profile_no_parent_0</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, - <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"k_parent_sink"</span>, <span class='st'>"k_parent_m1"</span>, <span class='st'>"k_m1_sink"</span>, <span class='st'>"sigma"</span>), <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>))</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> <span class='warning'>Warning: scheduled cores 2, 1, 3 encountered errors in user code, all values of the jobs will be affected</span></div><div class='output co'>#> <span class='error'>Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.005 0.04 0.206</span></div><div class='input'><span class='no'>ci_profile</span></div><div class='output co'>#> 2.5% 97.5% + <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"k_parent_sink"</span>, <span class='st'>"k_parent_m1"</span>, <span class='st'>"k_m1_sink"</span>, <span class='st'>"sigma"</span>), <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>))</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='output co'>#> <span class='warning'>Warning: scheduled cores 2, 1, 3 encountered errors in user code, all values of the jobs will be affected</span></div><div class='output co'>#> <span class='error'>Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.015 0.029 0.198</span></div><div class='input'><span class='no'>ci_profile</span></div><div class='output co'>#> 2.5% 97.5% #> parent_0 96.456003640 1.027703e+02 #> k_parent 0.090911032 1.071578e-01 #> k_m1 0.003892605 6.702778e-03 #> f_parent_to_m1 0.471328495 5.611550e-01 #> sigma 2.535612399 3.985263e+00</div><div class='input'><span class='no'>ci_quadratic_transformed</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>) <span class='no'>ci_quadratic_transformed</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403839476 1.027931e+02 +#> parent_0 96.403839460 1.027931e+02 #> k_parent 0.090823790 1.072543e-01 #> k_m1 0.004012216 6.897547e-03 #> f_parent_to_m1 0.469118713 5.595960e-01 #> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>ci_quadratic_untransformed</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_1</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) <span class='no'>ci_quadratic_untransformed</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403839429 1.027931e+02 +#> parent_0 96.403839413 1.027931e+02 #> k_parent 0.090491931 1.069035e-01 #> k_m1 0.003835483 6.685819e-03 -#> f_parent_to_m1 0.469113364 5.598386e-01 +#> f_parent_to_m1 0.469113365 5.598386e-01 #> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='co'># Against the expectation based on Bates and Watts (1988), the confidence</span> <span class='co'># intervals based on the internal parameter transformation are less</span> <span class='co'># congruent with the likelihood based intervals. Note the superiority of the</span> @@ -314,7 +313,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> k_parent TRUE TRUE #> k_m1 FALSE FALSE #> f_parent_to_m1 TRUE FALSE -#> sigma FALSE TRUE</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span>(<span class='no'>rel_diffs_transformed</span>, <span class='fl'>3</span>)</div><div class='output co'>#> 2.5% 97.5% +#> sigma FALSE FALSE</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/Round.html'>signif</a></span>(<span class='no'>rel_diffs_transformed</span>, <span class='fl'>3</span>)</div><div class='output co'>#> 2.5% 97.5% #> parent_0 0.000541 0.000222 #> k_parent 0.000960 0.000900 #> k_m1 0.030700 0.029100 @@ -327,24 +326,23 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> sigma 0.055000 0.032700</div><div class='input'> <span class='co'># Investigate a case with formation fractions</span> -<span class='no'>f_d_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='no'>ci_profile_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>)</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='input'><span class='no'>ci_profile_ff</span></div><div class='output co'>#> 2.5% 97.5% +<span class='no'>f_d_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='no'>ci_profile_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"profile"</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='no'>n_cores</span>)</div><div class='output co'>#> <span class='message'>Profiling the likelihood</span></div><div class='input'><span class='no'>ci_profile_ff</span></div><div class='output co'>#> 2.5% 97.5% #> parent_0 96.456003640 1.027703e+02 #> k_parent 0.090911032 1.071578e-01 #> k_m1 0.003892605 6.702778e-03 #> f_parent_to_m1 0.471328495 5.611550e-01 #> sigma 2.535612399 3.985263e+00</div><div class='input'><span class='no'>ci_quadratic_transformed_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>) <span class='no'>ci_quadratic_transformed_ff</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403839476 1.027931e+02 +#> parent_0 96.403839460 1.027931e+02 #> k_parent 0.090823790 1.072543e-01 #> k_m1 0.004012216 6.897547e-03 #> f_parent_to_m1 0.469118713 5.595960e-01 #> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>ci_quadratic_untransformed_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_d_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>) <span class='no'>ci_quadratic_untransformed_ff</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 96.403839429 1.027931e+02 +#> parent_0 96.403839413 1.027931e+02 #> k_parent 0.090491931 1.069035e-01 #> k_m1 0.003835483 6.685819e-03 -#> f_parent_to_m1 0.469113364 5.598386e-01 +#> f_parent_to_m1 0.469113365 5.598386e-01 #> sigma 2.396089689 3.854918e+00</div><div class='input'><span class='no'>rel_diffs_transformed_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_transformed_ff</span> - <span class='no'>ci_profile_ff</span>)/<span class='no'>ci_profile_ff</span>) <span class='no'>rel_diffs_untransformed_ff</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/MathFun.html'>abs</a></span>((<span class='no'>ci_quadratic_untransformed_ff</span> - <span class='no'>ci_profile_ff</span>)/<span class='no'>ci_profile_ff</span>) <span class='co'># While the confidence interval for the parent rate constant is closer to</span> @@ -356,17 +354,17 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, #> k_parent TRUE TRUE #> k_m1 FALSE FALSE #> f_parent_to_m1 TRUE FALSE -#> sigma FALSE TRUE</div><div class='input'><span class='no'>rel_diffs_transformed_ff</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408078 0.0002217796 +#> sigma FALSE FALSE</div><div class='input'><span class='no'>rel_diffs_transformed_ff</span></div><div class='output co'>#> 2.5% 97.5% +#> parent_0 0.0005408080 0.0002217794 #> k_parent 0.0009596417 0.0009003876 -#> k_m1 0.0307277372 0.0290579184 -#> f_parent_to_m1 0.0046884131 0.0027782558 -#> sigma 0.0550252516 0.0327066836</div><div class='input'><span class='no'>rel_diffs_untransformed_ff</span></div><div class='output co'>#> 2.5% 97.5% -#> parent_0 0.0005408083 0.000221780 -#> k_parent 0.0046100096 0.002373023 -#> k_m1 0.0146746467 0.002530101 -#> f_parent_to_m1 0.0046997600 0.002346022 -#> sigma 0.0550252516 0.032706684</div><div class='input'> +#> k_m1 0.0307277370 0.0290579182 +#> f_parent_to_m1 0.0046884130 0.0027782556 +#> sigma 0.0550252516 0.0327066836</div><div class='input'><span class='no'>rel_diffs_untransformed_ff</span></div><div class='output co'>#> 2.5% 97.5% +#> parent_0 0.0005408085 0.0002217799 +#> k_parent 0.0046100096 0.0023730229 +#> k_m1 0.0146746469 0.0025301011 +#> f_parent_to_m1 0.0046997599 0.0023460223 +#> sigma 0.0550252516 0.0327066836</div><div class='input'> <span class='co'># The profiling for the following fit does not finish in a reasonable time,</span> <span class='co'># therefore we use the quadratic approximation</span> <span class='no'>m_synth_DFOP_par</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"DFOP"</span>, <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>)), @@ -375,19 +373,19 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='no'>DFOP_par_c</span> <span class='kw'><-</span> <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span> <span class='no'>f_tc_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>, <span class='no'>DFOP_par_c</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, - <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='st'>"direct"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>iteration limit reached without convergence (10)</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 95.654015524 105.79279749 -#> k_M1 0.037723773 0.04447598 -#> k_M2 0.008586438 0.01078076 -#> f_parent_to_M1 0.230403596 0.61953014 -#> f_parent_to_M2 0.162909765 0.38019017 -#> k1 0.275434628 0.33331386 -#> k2 0.018602188 0.02249211 -#> g 0.675149759 0.73520889 -#> sigma_low 0.251416929 0.84272023 -#> rsd_high 0.040371818 0.07666540</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='st'>"parent_0"</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% -#> parent_0 95.65402 105.7928</div><div class='input'># } + <span class='kw'>error_model_algorithm</span> <span class='kw'>=</span> <span class='st'>"direct"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) +<span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% +#> parent_0 94.59613833 106.19939215 +#> k_M1 0.03760542 0.04490759 +#> k_M2 0.00856874 0.01087675 +#> f_parent_to_M1 0.02146166 0.62023888 +#> f_parent_to_M2 0.01516502 0.37975343 +#> k1 0.27389751 0.33388078 +#> k2 0.01861456 0.02250379 +#> g 0.67194349 0.73583256 +#> sigma_low 0.25128383 0.83992146 +#> rsd_high 0.04041100 0.07662001</div><div class='input'><span class='fu'><a href='https://rdrr.io/r/stats/confint.html'>confint</a></span>(<span class='no'>f_tc_2</span>, <span class='st'>"parent_0"</span>, <span class='kw'>method</span> <span class='kw'>=</span> <span class='st'>"quadratic"</span>)</div><div class='output co'>#> 2.5% 97.5% +#> parent_0 94.59614 106.1994</div><div class='input'># } </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/create_deg_func.html b/docs/dev/reference/create_deg_func.html index 59984b8c..a25fa165 100644 --- a/docs/dev/reference/create_deg_func.html +++ b/docs/dev/reference/create_deg_func.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -171,16 +171,14 @@ <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>FOCUS_D</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>) <span class='co'># to avoid warnings</span> -<span class='no'>fit_1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='no'>fit_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='co'># \dontrun{</span> +<span class='no'>fit_1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='no'>fit_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='co'># \dontrun{</span> <span class='kw'>if</span> (<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>rbenchmark</span>)) <span class='fu'><a href='https://rdrr.io/pkg/rbenchmark/man/benchmark.html'>benchmark</a></span>( <span class='kw'>analytical</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>deSolve</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), - <span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>2</span>)</div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.422 1.000 0.421 0 0 -#> 2 deSolve 2 0.722 1.711 0.721 0 0 + <span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>2</span>)</div><div class='output co'>#> <span class='message'>Loading required package: rbenchmark</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child +#> 1 analytical 2 0.423 1.000 0.423 0 0 +#> 2 deSolve 2 0.716 1.693 0.715 0 0 #> sys.child #> 1 0 #> 2 0</div><div class='input'> <span class='no'>DFOP_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( @@ -189,8 +187,8 @@ <span class='kw'>analytical</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>DFOP_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>deSolve</span> <span class='kw'>=</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>DFOP_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>2</span>)</div><div class='output co'>#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.907 1.000 0.906 0 0 -#> 2 deSolve 2 1.659 1.829 1.658 0 0 +#> 1 analytical 2 0.910 1.000 0.909 0 0 +#> 2 deSolve 2 1.734 1.905 1.733 0 0 #> sys.child #> 1 0 #> 2 0</div><div class='input'># } diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index 5751df93..1858e243 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -187,18 +187,22 @@ of these SFORB models, equivalent to DFOP rate constants</p> #> DT50 DT90 DT50back #> parent 1.785233 15.1479 4.559973 #> </div><div class='input'> <span class='co'># \dontrun{</span> - <span class='no'>fit_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) - <span class='fu'>endpoints</span>(<span class='no'>fit_2</span>)</div><div class='output co'>#> $ff -#> parent_free_sink -#> 1 + <span class='no'>fit_2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"DFOP"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) + <span class='fu'>endpoints</span>(<span class='no'>fit_2</span>)</div><div class='output co'>#> $distimes +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 1.886925 21.25106 6.397207 1.508293 38.83438 +#> </div><div class='input'> <span class='no'>fit_3</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) + <span class='fu'>endpoints</span>(<span class='no'>fit_3</span>)</div><div class='output co'>#> $ff +#> parent_free +#> 1 #> #> $SFORB #> parent_b1 parent_b2 #> 0.4595574 0.0178488 #> #> $distimes -#> DT50 DT90 DT50_parent_b1 DT50_parent_b2 -#> parent 1.886925 21.25106 1.508293 38.83438 +#> DT50 DT90 DT50back DT50_parent_b1 DT50_parent_b2 +#> parent 1.886925 21.25106 6.397208 1.508293 38.83438 #> </div><div class='input'> # } </div></pre> diff --git a/docs/dev/reference/get_deg_func.html b/docs/dev/reference/get_deg_func.html index 7500186b..ea0676cc 100644 --- a/docs/dev/reference/get_deg_func.html +++ b/docs/dev/reference/get_deg_func.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html index 245880f2..8f58949e 100644 --- a/docs/dev/reference/ilr.html +++ b/docs/dev/reference/ilr.html @@ -73,7 +73,7 @@ transformations." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index f1ab22e6..b72d0c85 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -331,12 +331,6 @@ of an mmkin object</p></td> </tr><tr> <td> - <p><code><a href="saemix.html">saemix_model()</a></code> <code><a href="saemix.html">saemix_data()</a></code> </p> - </td> - <td><p>Create saemix models from mmkin row objects</p></td> - </tr><tr> - - <td> <p><code><a href="get_deg_func.html">get_deg_func()</a></code> </p> </td> <td><p>Retrieve a degradation function from the mmkin namespace</p></td> diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 840a4821..7ab36f0d 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -193,7 +193,7 @@ and the fitted error model parameters.</p> <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>) )</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> <span class='no'>d_t</span> <span class='kw'><-</span> <span class='no'>FOCUS_2006_D</span> - <span class='no'>f_nw</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span></div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='no'>f_obs</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='no'>f_tc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>)</div><div class='output co'>#> df AIC + <span class='no'>f_nw</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='co'># no weighting (weights are unity)</span></div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'> <span class='no'>f_obs</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='no'>f_tc</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>sfo_sfo</span>, <span class='no'>d_t</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/AIC.html'>AIC</a></span>(<span class='no'>f_nw</span>, <span class='no'>f_obs</span>, <span class='no'>f_tc</span>)</div><div class='output co'>#> df AIC #> f_nw 5 204.4486 #> f_obs 6 205.8727 #> f_tc 6 141.9656</div><div class='input'> # } diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index 4804fc73..248edcda 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -232,18 +232,18 @@ Version 1.1, 18 December 2014 <span class='no'>m</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"logistic"</span>, <span class='no'>d_2_1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='fu'><a href='plot.mkinfit.html'>plot_sep</a></span>(<span class='no'>m</span>)</div><div class='img'><img src='logistic.solution-2.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>m</span>)$<span class='no'>bpar</span></div><div class='output co'>#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 1.9023449649 55.610120 3.768361e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 -#> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846685e-08 -#> r 2.263946e-01 0.1718110773 1.317695 1.061044e-01 4.335843e-02 +#> parent_0 1.057896e+02 1.9023449703 55.610119 3.768361e-16 1.016451e+02 +#> kmax 6.398190e-02 0.0143201031 4.467978 3.841829e-04 3.929235e-02 +#> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 +#> r 2.263946e-01 0.1718110715 1.317695 1.061044e-01 4.335843e-02 #> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper #> parent_0 109.9341588 #> kmax 0.1041853 -#> k0 0.4448750 -#> r 1.1821121 +#> k0 0.4448749 +#> r 1.1821120 #> sigma 7.3256566</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>m</span>)$<span class='no'>distimes</span></div><div class='output co'>#> DT50 DT90 DT50_k0 DT50_kmax -#> parent 36.86533 62.41511 4297.854 10.83349</div><div class='input'> +#> parent 36.86533 62.41511 4297.853 10.83349</div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/max_twa_parent.html b/docs/dev/reference/max_twa_parent.html index 77166d00..01aab55d 100644 --- a/docs/dev/reference/max_twa_parent.html +++ b/docs/dev/reference/max_twa_parent.html @@ -78,7 +78,7 @@ soil section of the FOCUS guidance." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/mccall81_245T.html b/docs/dev/reference/mccall81_245T.html index dc0dfbf8..fa352d0a 100644 --- a/docs/dev/reference/mccall81_245T.html +++ b/docs/dev/reference/mccall81_245T.html @@ -74,7 +74,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -178,15 +178,15 @@ <span class='kw'>phenol</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"anisole"</span>), <span class='kw'>anisole</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> <span class='co'># \dontrun{</span> <span class='no'>fit.1</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO_SFO</span>, <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>mccall81_245T</span>, <span class='no'>soil</span> <span class='kw'>==</span> <span class='st'>"Commerce"</span>), <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.1</span>)$<span class='no'>bpar</span></div><div class='output co'>#> Estimate se_notrans t value Pr(>t) -#> T245_0 1.038550e+02 2.184707509 47.537272 4.472189e-18 +#> T245_0 1.038550e+02 2.184707514 47.537272 4.472189e-18 #> k_T245 4.337042e-02 0.001898397 22.845818 2.276912e-13 -#> k_phenol 4.050581e-01 0.298699410 1.356073 9.756993e-02 +#> k_phenol 4.050581e-01 0.298699428 1.356073 9.756994e-02 #> k_anisole 6.678742e-03 0.000802144 8.326114 2.623179e-07 -#> f_T245_to_phenol 6.227599e-01 0.398534147 1.562626 6.949418e-02 -#> f_phenol_to_anisole 1.000000e+00 0.671844135 1.488440 7.867793e-02 -#> sigma 2.514628e+00 0.490755933 5.123989 6.233163e-05 +#> f_T245_to_phenol 6.227599e-01 0.398534167 1.562626 6.949418e-02 +#> f_phenol_to_anisole 1.000000e+00 0.671844168 1.488440 7.867794e-02 +#> sigma 2.514628e+00 0.490755943 5.123989 6.233164e-05 #> Lower Upper -#> T245_0 99.246061427 1.084640e+02 +#> T245_0 99.246061371 1.084640e+02 #> k_T245 0.039631621 4.746194e-02 #> k_phenol 0.218013878 7.525762e-01 #> k_anisole 0.005370739 8.305299e-03 @@ -194,7 +194,7 @@ #> f_phenol_to_anisole 0.000000000 1.000000e+00 #> sigma 1.706607296 3.322649e+00</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.1</span>)</div><div class='output co'>#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 1.005127e-10 +#> 6.227599e-01 3.772401e-01 1.000000e+00 1.748047e-10 #> #> $distimes #> DT50 DT90 @@ -206,7 +206,7 @@ <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>k_phenol_sink</span> <span class='kw'>=</span> <span class='fl'>0</span>), <span class='kw'>fixed_parms</span> <span class='kw'>=</span> <span class='st'>"k_phenol_sink"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Initial parameter(s) k_phenol_sink not used in the model</span></div><div class='output co'>#> <span class='error'>Error in data.frame(value = c(state.ini.fixed, parms.fixed)): row names contain missing values</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.2</span>)$<span class='no'>bpar</span></div><div class='output co'>#> <span class='error'>Error in summary(fit.2): object 'fit.2' not found</span></div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.1</span>)</div><div class='output co'>#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 1.005127e-10 +#> 6.227599e-01 3.772401e-01 1.000000e+00 1.748047e-10 #> #> $distimes #> DT50 DT90 diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html index 5c7d9490..a8641375 100644 --- a/docs/dev/reference/mkinds.html +++ b/docs/dev/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/mkinerrplot.html b/docs/dev/reference/mkinerrplot.html index 940a6861..104d1e3a 100644 --- a/docs/dev/reference/mkinerrplot.html +++ b/docs/dev/reference/mkinerrplot.html @@ -76,7 +76,7 @@ using the argument show_errplot = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html index e1e75767..90fb26be 100644 --- a/docs/dev/reference/mkinfit.html +++ b/docs/dev/reference/mkinfit.html @@ -423,16 +423,16 @@ Degradation Data. <em>Environments</em> 6(12) 124 <span class='co'># Use shorthand notation for parent only degradation</span> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:45 2020 -#> Date of summary: Wed May 27 07:43:45 2020 +#> R version used for fitting: 4.0.2 +#> Date of fit: Thu Oct 8 09:12:15 2020 +#> Date of summary: Thu Oct 8 09:12:15 2020 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.044 s +#> Fitted using 222 model solutions performed in 0.045 s #> #> Error model: Constant variance #> @@ -467,10 +467,10 @@ Degradation Data. <em>Environments</em> 6(12) 124 #> #> Parameter correlation: #> parent_0 log_alpha log_beta sigma -#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.770e-08 -#> log_alpha -1.565e-01 1.000e+00 9.564e-01 9.974e-08 -#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.468e-08 -#> sigma 4.770e-08 9.974e-08 8.468e-08 1.000e+00 +#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.758e-08 +#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.007e-07 +#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.568e-08 +#> sigma 4.758e-08 1.007e-07 8.568e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -503,426 +503,118 @@ Degradation Data. <em>Environments</em> 6(12) 124 #> 91 parent 3.9 1.441 2.4590 #> 119 parent 0.6 1.092 -0.4919</div><div class='input'> <span class='co'># One parent compound, one metabolite, both single first order.</span> +<span class='co'># We remove zero values from FOCUS dataset D in order to avoid warnings</span> +<span class='no'>FOCUS_D</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>FOCUS_2006_D</span>, <span class='no'>value</span> <span class='kw'>!=</span> <span class='fl'>0</span>) <span class='co'># Use mkinsub for convenience in model formulation. Pathway to sink included per default.</span> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, - <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)))</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> user system elapsed -#> 0.405 0.001 0.407 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma -#> 99.598481046 0.098697740 0.005260651 0.514475962 3.125503875 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit</span>)</div><div class='output co'>#> $ff + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> +<span class='co'># Fit the model quietly to the FOCUS example dataset D using defaults</span> +<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='co'># Since mkin 0.9.50.3, we get a warning about non-normality of residuals,</span> +<span class='co'># so we try an alternative error model</span> +<span class='no'>fit.tc</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>) +<span class='co'># This avoids the warning, and the likelihood ratio test confirms it is preferable</span> +<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span>(<span class='no'>fit.tc</span>, <span class='no'>fit</span>)</div><div class='output co'>#> Likelihood ratio test +#> +#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 6 -64.983 +#> 2 5 -97.224 -1 64.483 9.737e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='co'># We can also allow for different variances of parent and metabolite as error model</span> +<span class='no'>fit.obs</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>) +<span class='co'># This also avoids the warning about non-normality, but the two-component error model</span> +<span class='co'># has significantly higher likelihood</span> +<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span>(<span class='no'>fit.obs</span>, <span class='no'>fit.tc</span>)</div><div class='output co'>#> Likelihood ratio test +#> +#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 6 -64.983 +#> 2 6 -96.936 0 63.907 < 2.2e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1</div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.tc</span>)</div><div class='output co'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma_low +#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049891e-03 +#> rsd_high +#> 7.928117e-02 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.tc</span>)</div><div class='output co'>#> $ff #> parent_m1 parent_sink -#> 0.514476 0.485524 +#> 0.5083933 0.4916067 #> #> $distimes -#> DT50 DT90 -#> parent 7.022929 23.32966 -#> m1 131.760724 437.69965 -#> </div><div class='input'><span class='co'># \dontrun{</span> -<span class='co'># deSolve is slower when no C compiler (gcc) was available during model generation</span> -<span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fit.deSolve</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, - <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>)))</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='message'>Ordinary least squares optimisation</span></div><div class='output co'>#> Sum of squared residuals at call 1: 15156.12 -#> Sum of squared residuals at call 2: 15156.12 -#> Sum of squared residuals at call 6: 8243.645 -#> Sum of squared residuals at call 12: 6290.712 -#> Sum of squared residuals at call 13: 6290.683 -#> Sum of squared residuals at call 15: 6290.452 -#> Sum of squared residuals at call 18: 1700.749 -#> Sum of squared residuals at call 20: 1700.611 -#> Sum of squared residuals at call 24: 1190.923 -#> Sum of squared residuals at call 26: 1190.922 -#> Sum of squared residuals at call 29: 1017.417 -#> Sum of squared residuals at call 31: 1017.417 -#> Sum of squared residuals at call 33: 1017.416 -#> Sum of squared residuals at call 34: 644.0472 -#> Sum of squared residuals at call 36: 644.047 -#> Sum of squared residuals at call 38: 644.047 -#> Sum of squared residuals at call 39: 590.5025 -#> Sum of squared residuals at call 41: 590.5022 -#> Sum of squared residuals at call 43: 590.5016 -#> Sum of squared residuals at call 44: 543.2196 -#> Sum of squared residuals at call 45: 543.2193 -#> Sum of squared residuals at call 46: 543.2192 -#> Sum of squared residuals at call 50: 391.348 -#> Sum of squared residuals at call 51: 391.3479 -#> Sum of squared residuals at call 56: 386.479 -#> Sum of squared residuals at call 58: 386.479 -#> Sum of squared residuals at call 60: 386.4779 -#> Sum of squared residuals at call 61: 384.0686 -#> Sum of squared residuals at call 63: 384.0686 -#> Sum of squared residuals at call 66: 382.7813 -#> Sum of squared residuals at call 68: 382.7813 -#> Sum of squared residuals at call 70: 382.7813 -#> Sum of squared residuals at call 71: 378.9273 -#> Sum of squared residuals at call 73: 378.9273 -#> Sum of squared residuals at call 75: 378.9272 -#> Sum of squared residuals at call 76: 377.4847 -#> Sum of squared residuals at call 78: 377.4846 -#> Sum of squared residuals at call 81: 375.9738 -#> Sum of squared residuals at call 83: 375.9738 -#> Sum of squared residuals at call 86: 375.3387 -#> Sum of squared residuals at call 88: 375.3387 -#> Sum of squared residuals at call 91: 374.5774 -#> Sum of squared residuals at call 93: 374.5774 -#> Sum of squared residuals at call 95: 374.5774 -#> Sum of squared residuals at call 96: 373.5438 -#> Sum of squared residuals at call 100: 373.5438 -#> Sum of squared residuals at call 102: 373.265 -#> Sum of squared residuals at call 104: 373.265 -#> Sum of squared residuals at call 107: 372.6825 -#> Sum of squared residuals at call 111: 372.6825 -#> Sum of squared residuals at call 114: 372.6356 -#> Sum of squared residuals at call 116: 372.6356 -#> Sum of squared residuals at call 119: 372.6199 -#> Sum of squared residuals at call 121: 372.6199 -#> Sum of squared residuals at call 123: 372.6199 -#> Sum of squared residuals at call 124: 372.5881 -#> Sum of squared residuals at call 126: 372.5881 -#> Sum of squared residuals at call 129: 372.5418 -#> Sum of squared residuals at call 130: 372.4866 -#> Sum of squared residuals at call 131: 372.2242 -#> Sum of squared residuals at call 132: 371.5237 -#> Sum of squared residuals at call 134: 371.5237 -#> Sum of squared residuals at call 137: 371.292 -#> Sum of squared residuals at call 139: 371.292 -#> Sum of squared residuals at call 143: 371.2256 -#> Sum of squared residuals at call 144: 371.2256 -#> Sum of squared residuals at call 146: 371.2256 -#> Sum of squared residuals at call 149: 371.2194 -#> Sum of squared residuals at call 150: 371.2147 -#> Sum of squared residuals at call 153: 371.2147 -#> Sum of squared residuals at call 155: 371.2137 -#> Sum of squared residuals at call 156: 371.2137 -#> Sum of squared residuals at call 157: 371.2137 -#> Sum of squared residuals at call 160: 371.2134 -#> Sum of squared residuals at call 164: 371.2134 -#> Sum of squared residuals at call 165: 371.2134 -#> Sum of squared residuals at call 167: 371.2134 -#> Negative log-likelihood at call 177: 97.22429</div><div class='output co'>#> <span class='message'>Optimisation successfully terminated.</span></div><div class='output co'>#> user system elapsed -#> 0.361 0.000 0.361 </div><div class='input'><span class='fu'><a href='parms.html'>parms</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> parent_0 k_parent k_m1 f_parent_to_m1 sigma -#> 99.598480300 0.098697739 0.005260651 0.514475968 3.125503874 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fit.deSolve</span>)</div><div class='output co'>#> $ff -#> parent_m1 parent_sink -#> 0.514476 0.485524 -#> -#> $distimes -#> DT50 DT90 -#> parent 7.022929 23.32966 -#> m1 131.760721 437.69964 -#> </div><div class='input'><span class='co'># }</span> - -<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC</span> +#> DT50 DT90 +#> parent 6.89313 22.89848 +#> m1 134.15635 445.65776 +#> </div><div class='input'> +<span class='co'># We can show a quick (only one replication) benchmark for this case, as we</span> +<span class='co'># have several alternative solution methods for the model. We skip</span> +<span class='co'># uncompiled deSolve, as it is so slow. More benchmarks are found in the</span> +<span class='co'># benchmark vignette</span> +<span class='co'># \dontrun{</span> +<span class='kw'>if</span>(<span class='fu'><a href='https://rdrr.io/r/base/library.html'>require</a></span>(<span class='no'>rbenchmark</span>)) { + <span class='fu'><a href='https://rdrr.io/pkg/rbenchmark/man/benchmark.html'>benchmark</a></span>(<span class='kw'>replications</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>order</span> <span class='kw'>=</span> <span class='st'>"relative"</span>, <span class='kw'>columns</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"test"</span>, <span class='st'>"relative"</span>, <span class='st'>"elapsed"</span>), + <span class='kw'>deSolve_compiled</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, + <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), + <span class='kw'>eigen</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, + <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"eigen"</span>), + <span class='kw'>analytical</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, + <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>)) +}</div><div class='output co'>#> test relative elapsed +#> 3 analytical 1.000 0.750 +#> 1 deSolve_compiled 2.288 1.716 +#> 2 eigen 2.821 2.116</div><div class='input'><span class='co'># }</span> + +<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO</span> <span class='co'># \dontrun{</span> <span class='no'>FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"FOMC"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> -<span class='no'>fit.FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='co'># Use starting parameters from parent only FOMC fit</span> -<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='no'>fit.FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, - <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'> -<span class='co'># Use stepwise fitting, using optimised parameters from parent only fit, SFORB</span> -<span class='no'>SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( - <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFORB"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> -<span class='no'>fit.SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit.SFORB_SFO.deSolve</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"deSolve"</span>, - <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='co'># Use starting parameters from parent only SFORB fit (not really needed in this case)</span> -<span class='no'>fit.SFORB</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"SFORB"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='no'>fit.SFORB_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFORB_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.SFORB</span>$<span class='no'>bparms.ode</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Initial parameter(s) k_parent_free_sink not used in the model</span></div><div class='input'><span class='co'># }</span> - -<span class='co'># \dontrun{</span> -<span class='co'># Weighted fits, including IRLS (error_model = "obs")</span> -<span class='no'>SFO_SFO.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>f.noweight</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.noweight</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:50 2020 -#> Date of summary: Wed May 27 07:43:50 2020 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type analytical -#> -#> Fitted using 421 model solutions performed in 0.125 s -#> -#> Error model: Constant variance -#> -#> Error model algorithm: OLS -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Results: -#> -#> AIC BIC logLik -#> 204.4486 212.6365 -97.22429 -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.60000 1.57000 96.40000 102.8000 -#> log_k_parent -2.31600 0.04087 -2.39900 -2.2330 -#> log_k_m1 -5.24800 0.13320 -5.51800 -4.9770 -#> f_parent_ilr_1 0.04096 0.06312 -0.08746 0.1694 -#> sigma 3.12600 0.35850 2.39600 3.8550 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma -#> parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -3.190e-07 -#> log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 3.168e-07 -#> log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 -1.406e-07 -#> f_parent_ilr_1 -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 -1.587e-10 -#> sigma -3.190e-07 3.168e-07 -1.406e-07 -1.587e-10 1.000e+00 -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 -#> k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01 -#> k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03 -#> f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01 -#> sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 -#> -#> FOCUS Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.459 2 7 -#> m1 4.690 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5145 -#> parent_sink 0.4855 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.023 23.33 -#> m1 131.761 437.70 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.59848 -1.385e-01 -#> 0 parent 102.04 99.59848 2.442e+00 -#> 1 parent 93.50 90.23787 3.262e+00 -#> 1 parent 92.50 90.23787 2.262e+00 -#> 3 parent 63.23 74.07319 -1.084e+01 -#> 3 parent 68.99 74.07319 -5.083e+00 -#> 7 parent 52.32 49.91206 2.408e+00 -#> 7 parent 55.13 49.91206 5.218e+00 -#> 14 parent 27.27 25.01257 2.257e+00 -#> 14 parent 26.64 25.01257 1.627e+00 -#> 21 parent 11.50 12.53462 -1.035e+00 -#> 21 parent 11.64 12.53462 -8.946e-01 -#> 35 parent 2.85 3.14787 -2.979e-01 -#> 35 parent 2.91 3.14787 -2.379e-01 -#> 50 parent 0.69 0.71624 -2.624e-02 -#> 50 parent 0.63 0.71624 -8.624e-02 -#> 75 parent 0.05 0.06074 -1.074e-02 -#> 75 parent 0.06 0.06074 -7.381e-04 -#> 1 m1 4.84 4.80296 3.704e-02 -#> 1 m1 5.64 4.80296 8.370e-01 -#> 3 m1 12.91 13.02400 -1.140e-01 -#> 3 m1 12.96 13.02400 -6.400e-02 -#> 7 m1 22.97 25.04476 -2.075e+00 -#> 7 m1 24.47 25.04476 -5.748e-01 -#> 14 m1 41.69 36.69002 5.000e+00 -#> 14 m1 33.21 36.69002 -3.480e+00 -#> 21 m1 44.37 41.65310 2.717e+00 -#> 21 m1 46.44 41.65310 4.787e+00 -#> 35 m1 41.22 43.31312 -2.093e+00 -#> 35 m1 37.95 43.31312 -5.363e+00 -#> 50 m1 41.19 41.21831 -2.831e-02 -#> 50 m1 40.01 41.21831 -1.208e+00 -#> 75 m1 40.09 36.44703 3.643e+00 -#> 75 m1 33.85 36.44703 -2.597e+00 -#> 100 m1 31.04 31.98163 -9.416e-01 -#> 100 m1 33.13 31.98163 1.148e+00 -#> 120 m1 25.15 28.78984 -3.640e+00 -#> 120 m1 33.31 28.78984 4.520e+00</div><div class='input'><span class='no'>f.obs</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"obs"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.obs</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:51 2020 -#> Date of summary: Wed May 27 07:43:51 2020 + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit.FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0499</span></div><div class='input'><span class='co'># Again, we get a warning and try a more sophisticated error model</span> +<span class='no'>fit.FOMC_SFO.tc</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>) +<span class='co'># This model has a higher likelihood, but not significantly so</span> +<span class='fu'><a href='https://rdrr.io/pkg/lmtest/man/lrtest.html'>lrtest</a></span>(<span class='no'>fit.tc</span>, <span class='no'>fit.FOMC_SFO.tc</span>)</div><div class='output co'>#> Likelihood ratio test +#> +#> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 7 -64.829 +#> 2 6 -64.983 -1 0.3075 0.5792</div><div class='input'><span class='co'># Also, the missing standard error for log_beta and the t-tests for alpha</span> +<span class='co'># and beta indicate overparameterisation</span> +<span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.FOMC_SFO.tc</span>, <span class='kw'>data</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: NaNs produced</span></div><div class='output co'>#> <span class='warning'>Warning: diag(.) had 0 or NA entries; non-finite result is doubtful</span></div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 +#> R version used for fitting: 4.0.2 +#> Date of fit: Thu Oct 8 09:12:29 2020 +#> Date of summary: Thu Oct 8 09:12:29 2020 #> #> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type analytical -#> -#> Fitted using 978 model solutions performed in 0.413 s -#> -#> Error model: Variance unique to each observed variable -#> -#> Error model algorithm: d_3 -#> Direct fitting and three-step fitting yield approximately the same likelihood -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> sigma_parent 3.0000 error -#> sigma_m1 3.0000 error -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> sigma_parent 3.000000 0 Inf -#> sigma_m1 3.000000 0 Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Results: -#> -#> AIC BIC logLik -#> 205.8727 215.6982 -96.93634 -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.65000 1.70200 96.19000 103.1000 -#> log_k_parent -2.31300 0.04376 -2.40200 -2.2240 -#> log_k_m1 -5.25000 0.12430 -5.50400 -4.9970 -#> f_parent_ilr_1 0.03861 0.06171 -0.08708 0.1643 -#> sigma_parent 3.40100 0.56820 2.24400 4.5590 -#> sigma_m1 2.85500 0.45240 1.93400 3.7770 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_parent -#> parent_0 1.00000 0.51078 -0.19133 -0.59997 0.035670 -#> log_k_parent 0.51078 1.00000 -0.37458 -0.59239 0.069833 -#> log_k_m1 -0.19133 -0.37458 1.00000 0.74398 -0.026158 -#> f_parent_ilr_1 -0.59997 -0.59239 0.74398 1.00000 -0.041369 -#> sigma_parent 0.03567 0.06983 -0.02616 -0.04137 1.000000 -#> sigma_m1 -0.03385 -0.06627 0.02482 0.03926 -0.004628 -#> sigma_m1 -#> parent_0 -0.033847 -#> log_k_parent -0.066265 -#> log_k_m1 0.024823 -#> f_parent_ilr_1 0.039256 -#> sigma_parent -0.004628 -#> sigma_m1 1.000000 -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.650000 58.560 2.004e-34 96.190000 1.031e+02 -#> k_parent 0.098970 22.850 1.099e-21 0.090530 1.082e-01 -#> k_m1 0.005245 8.046 1.732e-09 0.004072 6.756e-03 -#> f_parent_to_m1 0.513600 23.560 4.352e-22 0.469300 5.578e-01 -#> sigma_parent 3.401000 5.985 5.662e-07 2.244000 4.559e+00 -#> sigma_m1 2.855000 6.311 2.215e-07 1.934000 3.777e+00 -#> -#> FOCUS Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.464 2 7 -#> m1 4.682 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5136 -#> parent_sink 0.4864 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.003 23.26 -#> m1 132.154 439.01 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.65417 -1.942e-01 -#> 0 parent 102.04 99.65417 2.386e+00 -#> 1 parent 93.50 90.26332 3.237e+00 -#> 1 parent 92.50 90.26332 2.237e+00 -#> 3 parent 63.23 74.05306 -1.082e+01 -#> 3 parent 68.99 74.05306 -5.063e+00 -#> 7 parent 52.32 49.84325 2.477e+00 -#> 7 parent 55.13 49.84325 5.287e+00 -#> 14 parent 27.27 24.92971 2.340e+00 -#> 14 parent 26.64 24.92971 1.710e+00 -#> 21 parent 11.50 12.46890 -9.689e-01 -#> 21 parent 11.64 12.46890 -8.289e-01 -#> 35 parent 2.85 3.11925 -2.692e-01 -#> 35 parent 2.91 3.11925 -2.092e-01 -#> 50 parent 0.69 0.70679 -1.679e-02 -#> 50 parent 0.63 0.70679 -7.679e-02 -#> 75 parent 0.05 0.05952 -9.523e-03 -#> 75 parent 0.06 0.05952 4.772e-04 -#> 1 m1 4.84 4.81075 2.925e-02 -#> 1 m1 5.64 4.81075 8.292e-01 -#> 3 m1 12.91 13.04196 -1.320e-01 -#> 3 m1 12.96 13.04196 -8.196e-02 -#> 7 m1 22.97 25.06847 -2.098e+00 -#> 7 m1 24.47 25.06847 -5.985e-01 -#> 14 m1 41.69 36.70308 4.987e+00 -#> 14 m1 33.21 36.70308 -3.493e+00 -#> 21 m1 44.37 41.65115 2.719e+00 -#> 21 m1 46.44 41.65115 4.789e+00 -#> 35 m1 41.22 43.29465 -2.075e+00 -#> 35 m1 37.95 43.29465 -5.345e+00 -#> 50 m1 41.19 41.19948 -9.479e-03 -#> 50 m1 40.01 41.19948 -1.189e+00 -#> 75 m1 40.09 36.44035 3.650e+00 -#> 75 m1 33.85 36.44035 -2.590e+00 -#> 100 m1 31.04 31.98773 -9.477e-01 -#> 100 m1 33.13 31.98773 1.142e+00 -#> 120 m1 25.15 28.80429 -3.654e+00 -#> 120 m1 33.31 28.80429 4.506e+00</div><div class='input'><span class='no'>f.tc</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>f.tc</span>)</div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:52 2020 -#> Date of summary: Wed May 27 07:43:52 2020 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 +#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent +#> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) * +#> parent - k_m1 * m1 #> -#> Model predictions using solution type analytical +#> Model predictions using solution type deSolve #> -#> Fitted using 2088 model solutions performed in 0.714 s +#> Fitted using 3611 model solutions performed in 2.61 s #> #> Error model: Two-component variance function #> #> Error model algorithm: d_3 -#> Direct fitting and three-step fitting yield approximately the same likelihood +#> Three-step fitting yielded a higher likelihood than direct fitting #> #> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> sigma_low 0.1000 error -#> rsd_high 0.1000 error +#> value type +#> parent_0 100.75 state +#> alpha 1.00 deparm +#> beta 10.00 deparm +#> k_m1 0.10 deparm +#> f_parent_to_m1 0.50 deparm +#> sigma_low 0.10 error +#> rsd_high 0.10 error #> #> Starting values for the transformed parameters actually optimised: #> value lower upper #> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf +#> log_k_m1 -2.302585 -Inf Inf #> f_parent_ilr_1 0.000000 -Inf Inf +#> log_alpha 0.000000 -Inf Inf +#> log_beta 2.302585 -Inf Inf #> sigma_low 0.100000 0 Inf #> rsd_high 0.100000 0 Inf #> @@ -932,98 +624,70 @@ Degradation Data. <em>Environments</em> 6(12) 124 #> #> Results: #> -#> AIC BIC logLik -#> 141.9656 151.7911 -64.98278 +#> AIC BIC logLik +#> 143.658 155.1211 -64.82902 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 100.70000 2.621000 95.400000 106.10000 -#> log_k_parent -2.29700 0.008862 -2.315000 -2.27900 -#> log_k_m1 -5.26600 0.091310 -5.452000 -5.08000 -#> f_parent_ilr_1 0.02374 0.055300 -0.088900 0.13640 -#> sigma_low 0.00305 0.004829 -0.006786 0.01289 -#> rsd_high 0.07928 0.009418 0.060100 0.09847 +#> Estimate Std. Error Lower Upper +#> parent_0 101.600000 2.6390000 96.240000 107.000000 +#> log_k_m1 -5.284000 0.0928900 -5.473000 -5.095000 +#> f_parent_ilr_1 0.001008 0.0541900 -0.109500 0.111500 +#> log_alpha 5.522000 0.0077300 5.506000 5.538000 +#> log_beta 7.806000 NaN NaN NaN +#> sigma_low 0.002488 0.0002431 0.001992 0.002984 +#> rsd_high 0.079210 0.0093280 0.060180 0.098230 #> #> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_low rsd_high -#> parent_0 1.00000 0.67644 -0.10215 -0.76822 0.14294 -0.08783 -#> log_k_parent 0.67644 1.00000 -0.15102 -0.59491 0.34611 -0.08125 -#> log_k_m1 -0.10215 -0.15102 1.00000 0.51808 -0.05236 0.01240 -#> f_parent_ilr_1 -0.76822 -0.59491 0.51808 1.00000 -0.13900 0.03248 -#> sigma_low 0.14294 0.34611 -0.05236 -0.13900 1.00000 -0.16546 -#> rsd_high -0.08783 -0.08125 0.01240 0.03248 -0.16546 1.00000 +#> parent_0 log_k_m1 f_parent_ilr_1 log_alpha log_beta sigma_low +#> parent_0 1.000000 -0.094697 -0.76654 0.70525 NaN 0.016099 +#> log_k_m1 -0.094697 1.000000 0.51404 -0.14347 NaN 0.001576 +#> f_parent_ilr_1 -0.766543 0.514038 1.00000 -0.61368 NaN 0.015465 +#> log_alpha 0.705247 -0.143468 -0.61368 1.00000 NaN 5.871780 +#> log_beta NaN NaN NaN NaN 1 NaN +#> sigma_low 0.016099 0.001576 0.01546 5.87178 NaN 1.000000 +#> rsd_high 0.006566 -0.011662 -0.05353 0.04845 NaN -0.652554 +#> rsd_high +#> parent_0 0.006566 +#> log_k_m1 -0.011662 +#> f_parent_ilr_1 -0.053525 +#> log_alpha 0.048451 +#> log_beta NaN +#> sigma_low -0.652554 +#> rsd_high 1.000000 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 1.007e+02 38.4300 1.180e-28 95.400000 1.061e+02 -#> k_parent 1.006e-01 112.8000 1.718e-43 0.098760 1.024e-01 -#> k_m1 5.167e-03 10.9500 1.171e-12 0.004290 6.223e-03 -#> f_parent_to_m1 5.084e-01 26.0100 2.146e-23 0.468600 5.481e-01 -#> sigma_low 3.050e-03 0.6314 2.661e-01 -0.006786 1.289e-02 -#> rsd_high 7.928e-02 8.4170 6.418e-10 0.060100 9.847e-02 +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 1.016e+02 32.7800 6.312e-26 9.624e+01 1.070e+02 +#> k_m1 5.072e-03 10.1200 1.216e-11 4.197e-03 6.130e-03 +#> f_parent_to_m1 5.004e-01 20.8300 4.318e-20 4.614e-01 5.394e-01 +#> alpha 2.502e+02 0.5624 2.889e-01 2.463e+02 2.542e+02 +#> beta 2.455e+03 0.5549 2.915e-01 NA NA +#> sigma_low 2.488e-03 0.4843 3.158e-01 1.992e-03 2.984e-03 +#> rsd_high 7.921e-02 8.4300 8.001e-10 6.018e-02 9.823e-02 #> #> FOCUS Chi2 error levels in percent: #> err.min n.optim df -#> All data 6.475 4 15 -#> parent 6.573 2 7 -#> m1 4.671 2 8 +#> All data 6.781 5 14 +#> parent 7.141 3 6 +#> m1 4.640 2 8 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.5084 -#> parent_sink 0.4916 +#> parent_m1 0.5004 +#> parent_sink 0.4996 #> #> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.893 22.9 -#> m1 134.156 445.7 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 100.73434 -1.274340 -#> 0 parent 102.04 100.73434 1.305660 -#> 1 parent 93.50 91.09751 2.402486 -#> 1 parent 92.50 91.09751 1.402486 -#> 3 parent 63.23 74.50141 -11.271410 -#> 3 parent 68.99 74.50141 -5.511410 -#> 7 parent 52.32 49.82880 2.491200 -#> 7 parent 55.13 49.82880 5.301200 -#> 14 parent 27.27 24.64809 2.621908 -#> 14 parent 26.64 24.64809 1.991908 -#> 21 parent 11.50 12.19232 -0.692315 -#> 21 parent 11.64 12.19232 -0.552315 -#> 35 parent 2.85 2.98327 -0.133266 -#> 35 parent 2.91 2.98327 -0.073266 -#> 50 parent 0.69 0.66013 0.029874 -#> 50 parent 0.63 0.66013 -0.030126 -#> 75 parent 0.05 0.05344 -0.003438 -#> 75 parent 0.06 0.05344 0.006562 -#> 1 m1 4.84 4.88645 -0.046451 -#> 1 m1 5.64 4.88645 0.753549 -#> 3 m1 12.91 13.22867 -0.318669 -#> 3 m1 12.96 13.22867 -0.268669 -#> 7 m1 22.97 25.36417 -2.394166 -#> 7 m1 24.47 25.36417 -0.894166 -#> 14 m1 41.69 37.00974 4.680263 -#> 14 m1 33.21 37.00974 -3.799737 -#> 21 m1 44.37 41.90133 2.468669 -#> 21 m1 46.44 41.90133 4.538669 -#> 35 m1 41.22 43.45691 -2.236913 -#> 35 m1 37.95 43.45691 -5.506913 -#> 50 m1 41.19 41.34199 -0.151985 -#> 50 m1 40.01 41.34199 -1.331985 -#> 75 m1 40.09 36.61471 3.475295 -#> 75 m1 33.85 36.61471 -2.764705 -#> 100 m1 31.04 32.20082 -1.160823 -#> 100 m1 33.13 32.20082 0.929177 -#> 120 m1 25.15 29.04130 -3.891304 -#> 120 m1 33.31 29.04130 4.268696</div><div class='input'># } - - -</div></pre> +#> DT50 DT90 DT50back +#> parent 6.812 22.7 6.834 +#> m1 136.661 454.0 NA</div><div class='input'> +<span class='co'># We can easily use starting parameters from the parent only fit (only for illustration)</span> +<span class='no'>fit.FOMC</span> <span class='kw'>=</span> <span class='fu'>mkinfit</span>(<span class='st'>"FOMC"</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>) +<span class='no'>fit.FOMC_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinfit</span>(<span class='no'>FOMC_SFO</span>, <span class='no'>FOCUS_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, + <span class='kw'>parms.ini</span> <span class='kw'>=</span> <span class='no'>fit.FOMC</span>$<span class='no'>bparms.ode</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>) +<span class='co'># }</span></div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> diff --git a/docs/dev/reference/mkinmod.html b/docs/dev/reference/mkinmod.html index 14b9eb1a..42529747 100644 --- a/docs/dev/reference/mkinmod.html +++ b/docs/dev/reference/mkinmod.html @@ -263,7 +263,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> Compilation argument: -#> /usr/lib/R/bin/R CMD SHLIB file15d25f6867c9.c 2> file15d25f6867c9.c.err.txt +#> /usr/lib/R/bin/R CMD SHLIB file306f74383fd2.c 2> file306f74383fd2.c.err.txt #> Program source: #> 1: #include <R.h> #> 2: @@ -301,7 +301,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> #> }) #> return(predicted) #> } -#> <environment: 0x5555576161e0></div><div class='input'> +#> <environment: 0x55555ad56ea0></div><div class='input'> <span class='co'># If we have several parallel metabolites</span> <span class='co'># (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)</span> <span class='no'>m_synth_DFOP_par</span> <span class='kw'><-</span> <span class='fu'>mkinmod</span>( @@ -312,8 +312,9 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media</p> <span class='no'>fit_DFOP_par_c</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>m_synth_DFOP_par</span>, <span class='no'>synthetic_data_for_UBA_2014</span><span class='kw'>[[</span><span class='fl'>12</span>]]$<span class='no'>data</span>, - <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='co'># }</span></div></pre> + <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.000174</span></div><div class='input'># } + +</div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> <nav id="toc" data-toggle="toc" class="sticky-top"> diff --git a/docs/dev/reference/mkinparplot-1.png b/docs/dev/reference/mkinparplot-1.png Binary files differindex 31800c09..6853a4ba 100644 --- a/docs/dev/reference/mkinparplot-1.png +++ b/docs/dev/reference/mkinparplot-1.png diff --git a/docs/dev/reference/mkinparplot.html b/docs/dev/reference/mkinparplot.html index 790f5e7e..027d8ae9 100644 --- a/docs/dev/reference/mkinparplot.html +++ b/docs/dev/reference/mkinparplot.html @@ -73,7 +73,7 @@ mkinfit." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 6d83e56f..15699c02 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -401,9 +401,9 @@ solver is used.</p></td> <span class='kw'>solution_type</span> <span class='kw'>=</span> <span class='st'>"analytical"</span>, <span class='kw'>use_compiled</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)[<span class='fl'>201</span>,]) }</div><div class='output co'>#> test relative elapsed #> 2 deSolve_compiled 1.0 0.005 -#> 4 analytical 1.0 0.005 +#> 4 analytical 1.8 0.009 #> 1 eigen 4.0 0.020 -#> 3 deSolve 45.6 0.228</div><div class='input'> +#> 3 deSolve 44.6 0.223</div><div class='input'> <span class='co'># \dontrun{</span> <span class='co'># Predict from a fitted model</span> <span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 11e0914e..5591d26f 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -243,7 +243,7 @@ combining the plot of the fit and the residual plot.</p></div> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> -<span class='no'>model</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='433' /></div><div class='input'> +<span class='no'>model</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>model</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='fu'>mkinresplot</span>(<span class='no'>fit</span>, <span class='st'>"m1"</span>)</div><div class='img'><img src='mkinresplot-1.png' alt='' width='700' height='433' /></div><div class='input'> </div></pre> </div> <div class="col-md-3 hidden-xs hidden-sm" id="pkgdown-sidebar"> diff --git a/docs/dev/reference/mkinsub.html b/docs/dev/reference/mkinsub.html index dc4faf0d..585e1840 100644 --- a/docs/dev/reference/mkinsub.html +++ b/docs/dev/reference/mkinsub.html @@ -73,7 +73,7 @@ mkinmod." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png Binary files differindex e80448ab..bce34531 100644 --- a/docs/dev/reference/mmkin-3.png +++ b/docs/dev/reference/mmkin-3.png diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png Binary files differindex 4c771bc9..56750342 100644 --- a/docs/dev/reference/mmkin-5.png +++ b/docs/dev/reference/mmkin-5.png diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html index 3daf16e1..a5d7ba42 100644 --- a/docs/dev/reference/mmkin.html +++ b/docs/dev/reference/mmkin.html @@ -196,8 +196,9 @@ for parallel execution.</p></td> <h2 class="hasAnchor" id="value"><a class="anchor" href="#value"></a>Value</h2> <p>A two-dimensional <code><a href='https://rdrr.io/r/base/array.html'>array</a></code> of <code><a href='mkinfit.html'>mkinfit</a></code> -objects that can be indexed using the model names for the first index (row index) -and the dataset names for the second index (column index).</p> +objects and/or try-errors that can be indexed using the model names for the +first index (row index) and the dataset names for the second index (column +index).</p> <h2 class="hasAnchor" id="see-also"><a class="anchor" href="#see-also"></a>See also</h2> <div class='dont-index'><p><code><a href='Extract.mmkin.html'>[.mmkin</a></code> for subsetting, <code><a href='plot.mmkin.html'>plot.mmkin</a></code> for @@ -218,19 +219,19 @@ plotting.</p></div> <span class='no'>time_default</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fits.0</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)) <span class='no'>time_1</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/system.time.html'>system.time</a></span>(<span class='no'>fits.4</span> <span class='kw'><-</span> <span class='fu'>mmkin</span>(<span class='no'>models</span>, <span class='no'>datasets</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> <span class='warning'>Warning: Optimisation did not converge:</span> -#> <span class='warning'>false convergence (8)</span></div><div class='input'> +#> <span class='warning'>false convergence (8)</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0117</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0174</span></div><div class='input'> <span class='no'>time_default</span></div><div class='output co'>#> user system elapsed -#> 4.499 0.456 1.983 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#> user system elapsed -#> 5.771 0.003 5.777 </div><div class='input'> +#> 4.500 0.399 1.311 </div><div class='input'><span class='no'>time_1</span></div><div class='output co'>#> user system elapsed +#> 5.154 0.008 5.165 </div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>fits.0</span><span class='kw'>[[</span><span class='st'>"SFO_lin"</span>, <span class='fl'>2</span>]])</div><div class='output co'>#> $ff #> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340479 0.2659521 0.7505687 0.2494313 +#> 0.7340478 0.2659522 0.7505691 0.2494309 #> #> $distimes #> DT50 DT90 #> parent 0.8777688 2.915885 -#> M1 2.3257457 7.725960 -#> M2 33.7200848 112.015697 +#> M1 2.3257466 7.725963 +#> M2 33.7200800 112.015681 #> </div><div class='input'> <span class='co'># plot.mkinfit handles rows or columns of mmkin result objects</span> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ])</div><div class='img'><img src='mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fits.0</span>[<span class='fl'>1</span>, ], <span class='kw'>obs_var</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"M1"</span>, <span class='st'>"M2"</span>))</div><div class='img'><img src='mmkin-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fits.0</span>[, <span class='fl'>1</span>])</div><div class='img'><img src='mmkin-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='co'># Use double brackets to extract a single mkinfit object, which will be plotted</span> diff --git a/docs/dev/reference/nafta.html b/docs/dev/reference/nafta.html index fe802c1b..690e4827 100644 --- a/docs/dev/reference/nafta.html +++ b/docs/dev/reference/nafta.html @@ -76,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -214,7 +214,7 @@ list element "data" contains the dataset used in the fits.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> - <span class='no'>nafta_evaluation</span> <span class='kw'><-</span> <span class='fu'>nafta</span>(<span class='no'>NAFTA_SOP_Appendix_D</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#> <span class='message'>The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></div><div class='output co'>#> <span class='message'>The representative half-life of the IORE model is longer than the one corresponding</span></div><div class='output co'>#> <span class='message'>to the terminal degradation rate found with the DFOP model.</span></div><div class='output co'>#> <span class='message'>The representative half-life obtained from the DFOP model may be used</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>nafta_evaluation</span>)</div><div class='output co'>#> Sums of squares: + <span class='no'>nafta_evaluation</span> <span class='kw'><-</span> <span class='fu'>nafta</span>(<span class='no'>NAFTA_SOP_Appendix_D</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00192</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00258</span></div><div class='output co'>#> <span class='message'>The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</span></div><div class='output co'>#> <span class='message'>The representative half-life of the IORE model is longer than the one corresponding</span></div><div class='output co'>#> <span class='message'>to the terminal degradation rate found with the DFOP model.</span></div><div class='output co'>#> <span class='message'>The representative half-life obtained from the DFOP model may be used</span></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>nafta_evaluation</span>)</div><div class='output co'>#> Sums of squares: #> SFO IORE DFOP #> 1378.6832 615.7730 517.8836 #> @@ -223,17 +223,17 @@ list element "data" contains the dataset used in the fits.</p> #> #> Parameters: #> $SFO -#> Estimate Pr(>t) Lower Upper -#> parent_0 83.7558 1.80e-14 77.18268 90.3288 -#> k_parent_sink 0.0017 7.43e-05 0.00112 0.0026 -#> sigma 8.7518 1.22e-05 5.64278 11.8608 +#> Estimate Pr(>t) Lower Upper +#> parent_0 83.7558 1.80e-14 77.18268 90.3288 +#> k_parent 0.0017 7.43e-05 0.00112 0.0026 +#> sigma 8.7518 1.22e-05 5.64278 11.8608 #> #> $IORE -#> Estimate Pr(>t) Lower Upper -#> parent_0 9.69e+01 NA 8.88e+01 1.05e+02 -#> k__iore_parent_sink 8.40e-14 NA 1.79e-18 3.94e-09 -#> N_parent 6.68e+00 NA 4.19e+00 9.17e+00 -#> sigma 5.85e+00 NA 3.76e+00 7.94e+00 +#> Estimate Pr(>t) Lower Upper +#> parent_0 9.69e+01 NA 8.88e+01 1.05e+02 +#> k__iore_parent 8.40e-14 NA 1.79e-18 3.94e-09 +#> N_parent 6.68e+00 NA 4.19e+00 9.17e+00 +#> sigma 5.85e+00 NA 3.76e+00 7.94e+00 #> #> $DFOP #> Estimate Pr(>t) Lower Upper diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png Binary files differindex 68ccb43f..8db1f999 100644 --- a/docs/dev/reference/nlme-1.png +++ b/docs/dev/reference/nlme-1.png diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html index 28a9f0a5..af5a151a 100644 --- a/docs/dev/reference/nlme.html +++ b/docs/dev/reference/nlme.html @@ -225,28 +225,28 @@ nlme for the case of a single grouping variable ds.</p> #> Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) #> Data: grouped_data #> AIC BIC logLik -#> 298.2781 307.7372 -144.1391 +#> 252.7798 262.1358 -121.3899 #> #> Random effects: #> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) #> Level: ds #> Structure: Diagonal -#> parent_0 log_k_parent_sink Residual -#> StdDev: 0.9374809 0.7098104 3.835429 +#> parent_0 log_k_parent_sink Residual +#> StdDev: 0.0006768135 0.6800777 2.489397 #> #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 -#> Value Std.Error DF t-value p-value -#> parent_0 101.76838 1.1445465 45 88.91589 0 -#> log_k_parent_sink -3.05444 0.4195622 45 -7.28008 0 +#> Value Std.Error DF t-value p-value +#> parent_0 101.74884 0.6456014 44 157.60321 0 +#> log_k_parent_sink -3.05575 0.4015811 44 -7.60929 0 #> Correlation: #> prnt_0 -#> log_k_parent_sink 0.034 +#> log_k_parent_sink 0.026 #> #> Standardized Within-Group Residuals: -#> Min Q1 Med Q3 Max -#> -2.61693660 -0.21853517 0.05740766 0.57209378 3.04598387 +#> Min Q1 Med Q3 Max +#> -2.1317488 -0.6878121 0.0828385 0.8592270 2.9529864 #> -#> Number of Observations: 49 +#> Number of Observations: 48 #> Number of Groups: 3 </div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='fu'><a href='https://rdrr.io/pkg/nlme/man/augPred.html'>augPred</a></span>(<span class='no'>m_nlme</span>, <span class='kw'>level</span> <span class='kw'>=</span> <span class='fl'>0</span>:<span class='fl'>1</span>), <span class='kw'>layout</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='fl'>3</span>, <span class='fl'>1</span>))</div><div class='img'><img src='nlme-1.png' alt='' width='700' height='433' /></div><div class='input'># augPred does not seem to work on fits with more than one state # variable diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html index c7db9c23..16df54af 100644 --- a/docs/dev/reference/nlme.mmkin.html +++ b/docs/dev/reference/nlme.mmkin.html @@ -262,45 +262,44 @@ with additional elements</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)], <span class='no'>name</span> <span class='kw'>==</span> <span class='st'>"parent"</span>)) -<span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='st'>"SFO"</span>, <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>) -<span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>nlme</span>) +<span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='st'>"SFO"</span>, <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0195</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.011</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>nlme</span>) <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>f</span><span class='kw'>[[</span><span class='fl'>1</span>]])</div><div class='output co'>#> $distimes #> DT50 DT90 #> parent 11.96183 39.73634 #> </div><div class='input'><span class='no'>f_nlme</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>) <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>f_nlme</span>)</div><div class='output co'>#> Nonlinear mixed-effects model fit by maximum likelihood -#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) +#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) #> Data: "Not shown" #> Log-likelihood: -307.5269 -#> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) -#> parent_0 log_k_parent_sink -#> 85.540979 -3.229602 +#> Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) +#> parent_0 log_k_parent +#> 85.541149 -3.229596 #> #> Random effects: -#> Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1) +#> Formula: list(parent_0 ~ 1, log_k_parent ~ 1) #> Level: ds #> Structure: Diagonal -#> parent_0 log_k_parent_sink Residual -#> StdDev: 1.308245 1.288586 6.304923 +#> parent_0 log_k_parent Residual +#> StdDev: 1.30857 1.288591 6.304906 #> #> Number of Observations: 90 #> Number of Groups: 5 </div><div class='input'><span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>f_nlme</span>)</div><div class='output co'>#> $distimes #> DT50 DT90 -#> parent 17.51556 58.18543 +#> parent 17.51545 58.18505 #> </div><div class='input'><span class='co'># \dontrun{</span> <span class='no'>f_nlme_2</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>, <span class='kw'>start</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='kw'>parent_0</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>log_k_parent_sink</span> <span class='kw'>=</span> <span class='fl'>0.1</span>)) <span class='fu'><a href='https://rdrr.io/r/stats/update.html'>update</a></span>(<span class='no'>f_nlme_2</span>, <span class='kw'>random</span> <span class='kw'>=</span> <span class='no'>parent_0</span> ~ <span class='fl'>1</span>)</div><div class='output co'>#> Nonlinear mixed-effects model fit by maximum likelihood -#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) +#> Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) #> Data: "Not shown" #> Log-likelihood: -404.3729 -#> Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) -#> parent_0 log_k_parent_sink -#> 75.933480 -3.555983 +#> Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) +#> parent_0 log_k_parent +#> 75.933480 -3.555983 #> #> Random effects: #> Formula: parent_0 ~ 1 | ds #> parent_0 Residual -#> StdDev: 0.002416802 21.63027 +#> StdDev: 0.002416792 21.63027 #> #> Number of Observations: 90 #> Number of Groups: 5 </div><div class='input'> <span class='co'># Test on some real data</span> @@ -332,88 +331,88 @@ with additional elements</p> <span class='no'>f_nlme_fomc_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f_2</span>[<span class='st'>"FOMC-SFO"</span>, ], <span class='kw'>control</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>pnlsMaxIter</span> <span class='kw'>=</span> <span class='fl'>100</span>, <span class='kw'>tolerance</span> <span class='kw'>=</span> <span class='fl'>1e-4</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> #> **Iteration 1 -#> LME step: Loglik: -394.1603, nlminb iterations: 2 +#> LME step: Loglik: -394.1603, nlminb iterations: 3 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 -#> -0.2079863 0.8563823 1.7454253 1.0917707 1.2756955 +#> -0.2079793 0.8563830 1.7454105 1.0917354 1.2756825 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 643.8814 -#> fixed effects: 94.17379 -5.473189 -0.6970234 -0.202509 2.103883 +#> PNLS step: RSS = 643.8803 +#> fixed effects: 94.17379 -5.473193 -0.6970236 -0.2025091 2.103883 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct -#> 0.7959873 0.1447512 +#> 0.7960134 0.1447728 #> #> **Iteration 2 #> LME step: Loglik: -396.3824, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 -#> -1.712406e-01 -2.278541e-05 1.842120e+00 1.073975e+00 1.322924e+00 +#> -1.712404e-01 -2.432655e-05 1.842120e+00 1.073975e+00 1.322925e+00 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 643.8025 -#> fixed effects: 94.17385 -5.473491 -0.6970406 -0.2025139 2.103871 +#> PNLS step: RSS = 643.8035 +#> fixed effects: 94.17385 -5.473487 -0.6970404 -0.2025137 2.103871 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): -#> fixed reStruct -#> 5.51758e-05 1.26861e-03 +#> fixed reStruct +#> 5.382757e-05 1.236667e-03 #> #> **Iteration 3 #> LME step: Loglik: -396.3825, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 -#> -0.1712500923 -0.0001515734 1.8420972550 1.0739796967 1.3229177241 +#> -0.1712499044 -0.0001499831 1.8420971364 1.0739799123 1.3229167796 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 643.7941 -#> fixed effects: 94.17386 -5.473523 -0.6970424 -0.2025146 2.103869 +#> PNLS step: RSS = 643.7948 +#> fixed effects: 94.17386 -5.473521 -0.6970422 -0.2025144 2.10387 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct -#> 5.792621e-06 1.335434e-04 +#> 6.072817e-06 1.400857e-04 #> #> **Iteration 4 #> LME step: Loglik: -396.3825, nlminb iterations: 7 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 -#> -0.1712517206 -0.0001651603 1.8420950864 1.0739800294 1.3229173529 +#> -0.1712529502 -0.0001641277 1.8420957542 1.0739797181 1.3229173076 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 643.7949 -#> fixed effects: 94.17386 -5.473521 -0.6970423 -0.2025145 2.10387 +#> PNLS step: RSS = 643.7936 +#> fixed effects: 94.17386 -5.473526 -0.6970426 -0.2025146 2.103869 #> iterations: 100 #> Convergence crit. (must all become <= tolerance = 0.0001): #> fixed reStruct -#> 4.025781e-07 9.628656e-06 </div><div class='input'> <span class='no'>f_nlme_dfop_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f_2</span>[<span class='st'>"DFOP-SFO"</span>, ], +#> 1.027451e-06 2.275704e-05 </div><div class='input'> <span class='no'>f_nlme_dfop_sfo</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f_2</span>[<span class='st'>"DFOP-SFO"</span>, ], <span class='kw'>control</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>pnlsMaxIter</span> <span class='kw'>=</span> <span class='fl'>120</span>, <span class='kw'>tolerance</span> <span class='kw'>=</span> <span class='fl'>5e-4</span>), <span class='kw'>verbose</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> #> **Iteration 1 -#> LME step: Loglik: -404.9583, nlminb iterations: 1 +#> LME step: Loglik: -404.9582, nlminb iterations: 1 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 -#> -0.4114357 0.9798641 1.6990035 0.7293314 0.3354323 1.7113047 +#> -0.4114355 0.9798697 1.6990037 0.7293315 0.3354323 1.7113046 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 630.3642 -#> fixed effects: 93.82269 -5.455991 -0.6788957 -1.862196 -4.199671 0.0553284 +#> PNLS step: RSS = 630.3644 +#> fixed effects: 93.82269 -5.455991 -0.6788957 -1.862196 -4.199671 0.05532828 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct -#> 0.7879730 0.5822574 +#> 0.7885368 0.5822683 #> #> **Iteration 2 #> LME step: Loglik: -407.7755, nlminb iterations: 11 #> reStruct parameters: #> ds1 ds2 ds3 ds4 ds5 ds6 -#> -0.371224105 0.003056163 1.789939431 0.724671132 0.301602942 1.754200482 +#> -0.371224133 0.003056179 1.789939402 0.724671158 0.301602977 1.754200729 #> Beginning PNLS step: .. completed fit_nlme() step. -#> PNLS step: RSS = 630.364 -#> fixed effects: 93.82269 -5.455991 -0.6788958 -1.862196 -4.199671 0.05532834 +#> PNLS step: RSS = 630.3633 +#> fixed effects: 93.82269 -5.455992 -0.6788958 -1.862196 -4.199671 0.05532831 #> iterations: 120 #> Convergence crit. (must all become <= tolerance = 0.0005): #> fixed reStruct -#> 9.814652e-07 1.059239e-05 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_2</span>[<span class='st'>"FOMC-SFO"</span>, <span class='fl'>3</span>:<span class='fl'>4</span>])</div><div class='img'><img src='nlme.mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_nlme_fomc_sfo</span>, <span class='fl'>3</span>:<span class='fl'>4</span>)</div><div class='img'><img src='nlme.mmkin-5.png' alt='' width='700' height='433' /></div><div class='input'> +#> 4.789774e-07 2.200661e-05 </div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_2</span>[<span class='st'>"FOMC-SFO"</span>, <span class='fl'>3</span>:<span class='fl'>4</span>])</div><div class='img'><img src='nlme.mmkin-4.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_nlme_fomc_sfo</span>, <span class='fl'>3</span>:<span class='fl'>4</span>)</div><div class='img'><img src='nlme.mmkin-5.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_2</span>[<span class='st'>"DFOP-SFO"</span>, <span class='fl'>3</span>:<span class='fl'>4</span>])</div><div class='img'><img src='nlme.mmkin-6.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f_nlme_dfop_sfo</span>, <span class='fl'>3</span>:<span class='fl'>4</span>)</div><div class='img'><img src='nlme.mmkin-7.png' alt='' width='700' height='433' /></div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span>(<span class='no'>f_nlme_dfop_sfo</span>, <span class='no'>f_nlme_fomc_sfo</span>, <span class='no'>f_nlme_sfo_sfo</span>)</div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 -#> f_nlme_fomc_sfo 2 11 818.5151 853.0089 -398.2576 1 vs 2 21.33957 <.0001 -#> f_nlme_sfo_sfo 3 9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66697 <.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span>(<span class='no'>f_nlme_dfop_sfo</span>, <span class='no'>f_nlme_sfo_sfo</span>) <span class='co'># if we ignore FOMC</span></div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value -#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9274 +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9273 +#> f_nlme_fomc_sfo 2 11 818.5149 853.0087 -398.2575 1 vs 2 21.33975 <.0001 +#> f_nlme_sfo_sfo 3 9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66716 <.0001</div><div class='input'> <span class='fu'><a href='https://rdrr.io/r/stats/anova.html'>anova</a></span>(<span class='no'>f_nlme_dfop_sfo</span>, <span class='no'>f_nlme_sfo_sfo</span>) <span class='co'># if we ignore FOMC</span></div><div class='output co'>#> Model df AIC BIC logLik Test L.Ratio p-value +#> f_nlme_dfop_sfo 1 13 843.8547 884.6201 -408.9273 #> f_nlme_sfo_sfo 2 9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274 <.0001</div><div class='input'> <span class='fu'><a href='endpoints.html'>endpoints</a></span>(<span class='no'>f_nlme_sfo_sfo</span>)</div><div class='output co'>#> $ff #> parent_sink parent_A1 A1_sink @@ -428,9 +427,9 @@ with additional elements</p> #> 0.2768574 0.7231426 #> #> $distimes -#> DT50 DT90 DT50_k1 DT50_k2 -#> parent 11.07091 104.6320 4.462384 46.20825 -#> A1 162.30518 539.1661 NA NA +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 11.07091 104.6320 31.49738 4.462384 46.20825 +#> A1 162.30536 539.1667 NA NA NA #> </div><div class='input'># } </div></pre> </div> diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html index a50ca352..bd35d3c1 100644 --- a/docs/dev/reference/parms.html +++ b/docs/dev/reference/parms.html @@ -188,23 +188,22 @@ such matrices is returned.</p> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='co'># mkinfit objects</span> <span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='st'>"SFO"</span>, <span class='no'>FOCUS_2006_C</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>) -<span class='fu'>parms</span>(<span class='no'>fit</span>)</div><div class='output co'>#> parent_0 k_parent_sink sigma -#> 82.4921598 0.3060633 4.6730124 </div><div class='input'><span class='fu'>parms</span>(<span class='no'>fit</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> parent_0 log_k_parent_sink sigma -#> 82.492160 -1.183963 4.673012 </div><div class='input'> +<span class='fu'>parms</span>(<span class='no'>fit</span>)</div><div class='output co'>#> parent_0 k_parent sigma +#> 82.4921598 0.3060633 4.6730124 </div><div class='input'><span class='fu'>parms</span>(<span class='no'>fit</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> parent_0 log_k_parent sigma +#> 82.492160 -1.183963 4.673012 </div><div class='input'> <span class='co'># mmkin objects</span> <span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)])) <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>ds</span>) <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/paste.html'>paste</a></span>(<span class='st'>"Dataset"</span>, <span class='fl'>6</span>:<span class='fl'>10</span>) <span class='co'># \dontrun{</span> -<span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>) -<span class='fu'>parms</span>(<span class='no'>fits</span>[<span class='st'>"SFO"</span>, ])</div><div class='output co'>#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 -#> k_parent_sink 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421 -#> sigma 5.15274487 7.040168584 3.6769645 6.4669234 6.50457673</div><div class='input'><span class='fu'>parms</span>(<span class='no'>fits</span>[, <span class='fl'>2</span>])</div><div class='output co'>#> $SFO -#> Dataset 7 -#> parent_0 82.666781678 -#> k_parent_sink 0.009647805 -#> sigma 7.040168584 +<span class='no'>fits</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"FOMC"</span>, <span class='st'>"DFOP"</span>), <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0195</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00408</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0492</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00985</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.00815</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.011</span></div><div class='input'><span class='fu'>parms</span>(<span class='no'>fits</span>[<span class='st'>"SFO"</span>, ])</div><div class='output co'>#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 +#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 +#> k_parent 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421 +#> sigma 5.15274487 7.040168584 3.6769645 6.4669234 6.50457673</div><div class='input'><span class='fu'>parms</span>(<span class='no'>fits</span>[, <span class='fl'>2</span>])</div><div class='output co'>#> $SFO +#> Dataset 7 +#> parent_0 82.666781678 +#> k_parent 0.009647805 +#> sigma 7.040168584 #> #> $FOMC #> Dataset 7 @@ -221,10 +220,10 @@ such matrices is returned.</p> #> g 0.526942415 #> sigma 2.221302196 #> </div><div class='input'><span class='fu'>parms</span>(<span class='no'>fits</span>)</div><div class='output co'>#> $SFO -#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 -#> k_parent_sink 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421 -#> sigma 5.15274487 7.040168584 3.6769645 6.4669234 6.50457673 +#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 +#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450 +#> k_parent 0.05794659 0.009647805 0.2102974 0.1232258 0.00720421 +#> sigma 5.15274487 7.040168584 3.6769645 6.4669234 6.50457673 #> #> $FOMC #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 @@ -241,15 +240,15 @@ such matrices is returned.</p> #> g 0.44845068 0.526942415 0.66091965 0.65322767 0.342652880 #> sigma 1.35690468 2.221302196 1.34169076 2.87159846 1.942067831 #> </div><div class='input'><span class='fu'>parms</span>(<span class='no'>fits</span>, <span class='kw'>transformed</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> $SFO -#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 88.522754 82.666782 86.854731 91.777931 82.148094 -#> log_k_parent_sink -2.848234 -4.641025 -1.559232 -2.093737 -4.933090 -#> sigma 5.152745 7.040169 3.676964 6.466923 6.504577 +#> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 +#> parent_0 88.522754 82.666782 86.854731 91.777931 82.148094 +#> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737 -4.933090 +#> sigma 5.152745 7.040169 3.676964 6.466923 6.504577 #> #> $FOMC #> Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10 -#> parent_0 95.5585751 92.6837649 90.7197870 98.38393896 94.848146 -#> log_alpha 0.2916741 -0.6996015 0.4941466 0.07181817 -1.271085 +#> parent_0 95.5585751 92.6837649 90.7197870 98.38393897 94.848146 +#> log_alpha 0.2916741 -0.6996015 0.4941466 0.07181816 -1.271085 #> log_beta 2.5675088 2.6493701 1.6108523 1.48095106 1.932278 #> sigma 1.8476712 1.9167519 1.0660627 3.14605557 1.622278 #> diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html index b9331f1a..ffbd1206 100644 --- a/docs/dev/reference/plot.mkinfit.html +++ b/docs/dev/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -338,7 +338,7 @@ latex is being used for the formatting of the chi2 error level, if <span class='co'># parent to sink included</span> <span class='co'># \dontrun{</span> <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='st'>"m1"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Parent"</span>), - <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>, <span class='kw'>standardized</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_err</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='433' /></div><div class='input'> + <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>, <span class='kw'>full</span> <span class='kw'>=</span> <span class='st'>"Metabolite M1"</span> ))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>error_model</span> <span class='kw'>=</span> <span class='st'>"tc"</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-2.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_res</span>(<span class='no'>fit</span>, <span class='kw'>standardized</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>)</div><div class='img'><img src='plot.mkinfit-3.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'>plot_err</span>(<span class='no'>fit</span>)</div><div class='img'><img src='plot.mkinfit-4.png' alt='' width='700' height='433' /></div><div class='input'> <span class='co'># Show the observed variables separately, with residuals</span> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>fit</span>, <span class='kw'>sep_obs</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>show_residuals</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>lpos</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"topright"</span>, <span class='st'>"bottomright"</span>), <span class='kw'>show_errmin</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='img'><img src='plot.mkinfit-5.png' alt='' width='700' height='433' /></div><div class='input'> diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png Binary files differindex 8cf969c9..24fa6ca7 100644 --- a/docs/dev/reference/plot.mmkin-1.png +++ b/docs/dev/reference/plot.mmkin-1.png diff --git a/docs/dev/reference/plot.mmkin-2.png b/docs/dev/reference/plot.mmkin-2.png Binary files differindex 45d67b55..377e50b5 100644 --- a/docs/dev/reference/plot.mmkin-2.png +++ b/docs/dev/reference/plot.mmkin-2.png diff --git a/docs/dev/reference/plot.mmkin-3.png b/docs/dev/reference/plot.mmkin-3.png Binary files differindex c58b371a..3ea7b38a 100644 --- a/docs/dev/reference/plot.mmkin-3.png +++ b/docs/dev/reference/plot.mmkin-3.png diff --git a/docs/dev/reference/plot.mmkin-4.png b/docs/dev/reference/plot.mmkin-4.png Binary files differindex 47cd7eec..017fbd1d 100644 --- a/docs/dev/reference/plot.mmkin-4.png +++ b/docs/dev/reference/plot.mmkin-4.png diff --git a/docs/dev/reference/plot.mmkin-5.png b/docs/dev/reference/plot.mmkin-5.png Binary files differindex 44037bb4..e7463916 100644 --- a/docs/dev/reference/plot.mmkin-5.png +++ b/docs/dev/reference/plot.mmkin-5.png diff --git a/docs/dev/reference/plot.mmkin.html b/docs/dev/reference/plot.mmkin.html index ca1ec266..f02e2ea6 100644 --- a/docs/dev/reference/plot.mmkin.html +++ b/docs/dev/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/plot.nlme.mmkin-2.png b/docs/dev/reference/plot.nlme.mmkin-2.png Binary files differindex c82d0271..265fd2e0 100644 --- a/docs/dev/reference/plot.nlme.mmkin-2.png +++ b/docs/dev/reference/plot.nlme.mmkin-2.png diff --git a/docs/dev/reference/plot.nlme.mmkin.html b/docs/dev/reference/plot.nlme.mmkin.html index fd40b975..7e6124a1 100644 --- a/docs/dev/reference/plot.nlme.mmkin.html +++ b/docs/dev/reference/plot.nlme.mmkin.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -238,8 +238,7 @@ than two rows of plots are shown.</p></td> <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'><span class='no'>ds</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/lapply.html'>lapply</a></span>(<span class='no'>experimental_data_for_UBA_2019</span>[<span class='fl'>6</span>:<span class='fl'>10</span>], <span class='kw'>function</span>(<span class='no'>x</span>) <span class='fu'><a href='https://rdrr.io/r/base/subset.html'>subset</a></span>(<span class='no'>x</span>$<span class='no'>data</span>[<span class='fu'><a href='https://rdrr.io/r/base/c.html'>c</a></span>(<span class='st'>"name"</span>, <span class='st'>"time"</span>, <span class='st'>"value"</span>)], <span class='no'>name</span> <span class='kw'>==</span> <span class='st'>"parent"</span>)) -<span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='st'>"SFO"</span>, <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>) -<span class='co'>#plot(f) # too many panels for pkgdown</span> +<span class='no'>f</span> <span class='kw'><-</span> <span class='fu'><a href='mmkin.html'>mmkin</a></span>(<span class='st'>"SFO"</span>, <span class='no'>ds</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>, <span class='kw'>cores</span> <span class='kw'>=</span> <span class='fl'>1</span>)</div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0195</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.011</span></div><div class='input'><span class='co'>#plot(f) # too many panels for pkgdown</span> <span class='fu'><a href='https://rdrr.io/r/base/plot.html'>plot</a></span>(<span class='no'>f</span>[, <span class='fl'>3</span>:<span class='fl'>4</span>])</div><div class='img'><img src='plot.nlme.mmkin-1.png' alt='' width='700' height='433' /></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/library.html'>library</a></span>(<span class='no'>nlme</span>) <span class='no'>f_nlme</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/pkg/nlme/man/nlme.html'>nlme</a></span>(<span class='no'>f</span>) diff --git a/docs/dev/reference/print.mkinds.html b/docs/dev/reference/print.mkinds.html index 0539c7da..a8c0d808 100644 --- a/docs/dev/reference/print.mkinds.html +++ b/docs/dev/reference/print.mkinds.html @@ -72,7 +72,7 @@ </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html index eac61a11..fd5c603e 100644 --- a/docs/dev/reference/sigma_twocomp.html +++ b/docs/dev/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ dependence of the measured value \(y\):" /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html index 99d7d7c4..f971fdf4 100644 --- a/docs/dev/reference/summary.mkinfit.html +++ b/docs/dev/reference/summary.mkinfit.html @@ -76,7 +76,7 @@ values." /> </button> <span class="navbar-brand"> <a class="navbar-link" href="../index.html">mkin</a> - <span class="version label label-danger" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> + <span class="version label label-info" data-toggle="tooltip" data-placement="bottom" title="In-development version">0.9.50.3</span> </span> </div> @@ -233,9 +233,9 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, <h2 class="hasAnchor" id="examples"><a class="anchor" href="#examples"></a>Examples</h2> <pre class="examples"><div class='input'> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='fu'><a href='mkinmod.html'>mkinmod</a></span>(<span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='mkinsub.html'>mkinsub</a></span>(<span class='st'>"SFO"</span>)), <span class='no'>FOCUS_2006_A</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>))</div><div class='output co'>#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 06:02:05 2020 -#> Date of summary: Wed May 27 06:02:05 2020 +#> R version used for fitting: 4.0.2 +#> Date of fit: Thu Oct 8 09:13:59 2020 +#> Date of summary: Thu Oct 8 09:13:59 2020 #> #> Equations: #> d_parent/dt = - k_parent * parent @@ -274,9 +274,9 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, #> #> Parameter correlation: #> parent_0 log_k_parent sigma -#> parent_0 1.000e+00 5.428e-01 1.648e-07 -#> log_k_parent 5.428e-01 1.000e+00 2.513e-07 -#> sigma 1.648e-07 2.513e-07 1.000e+00 +#> parent_0 1.000e+00 5.428e-01 1.642e-07 +#> log_k_parent 5.428e-01 1.000e+00 2.507e-07 +#> sigma 1.642e-07 2.507e-07 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html index b0994d33..58a1e9a1 100644 --- a/docs/dev/reference/transform_odeparms.html +++ b/docs/dev/reference/transform_odeparms.html @@ -226,7 +226,7 @@ This is no problem for the internal use in <code><a href='mkinfit.html'>mkinfit< <span class='no'>SFO_SFO</span> <span class='kw'><-</span> <span class='fu'><a href='mkinmod.html'>mkinmod</a></span>( <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>))</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'><span class='co'># Fit the model to the FOCUS example dataset D using defaults</span> -<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>) +<span class='no'>fit</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='no'>fit.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit</span>) <span class='co'># Transformed and backtransformed parameters</span> <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.s</span>$<span class='no'>par</span>, <span class='fl'>3</span>)</div><div class='output co'>#> Estimate Std. Error Lower Upper #> parent_0 99.598 1.5702 96.4038 102.793 @@ -241,7 +241,7 @@ This is no problem for the internal use in <code><a href='mkinfit.html'>mkinfit< #> sigma 3.12550 0.35852 8.72 2.24e-10 2.39609 3.8549</div><div class='input'> <span class='co'># \dontrun{</span> <span class='co'># Compare to the version without transforming rate parameters</span> -<span class='no'>fit.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='error'>Error in if (cost < cost.current) { assign("cost.current", cost, inherits = TRUE) if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"), " at call ", calls, ": ", cost.current, "\n", sep = "")}: missing value where TRUE/FALSE needed</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0 0.002 0.002</span></div><div class='input'><span class='no'>fit.2.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.2</span>)</div><div class='output co'>#> <span class='error'>Error in summary(fit.2): object 'fit.2' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.2.s</span>$<span class='no'>par</span>, <span class='fl'>3</span>)</div><div class='output co'>#> <span class='error'>Error in print(fit.2.s$par, 3): object 'fit.2.s' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.2.s</span>$<span class='no'>bpar</span>, <span class='fl'>3</span>)</div><div class='output co'>#> <span class='error'>Error in print(fit.2.s$bpar, 3): object 'fit.2.s' not found</span></div><div class='input'><span class='co'># }</span> +<span class='no'>fit.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>transform_rates</span> <span class='kw'>=</span> <span class='fl'>FALSE</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='error'>Error in if (cost < cost.current) { assign("cost.current", cost, inherits = TRUE) if (!quiet) cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"), " at call ", calls, ": ", signif(cost.current, 6), "\n", sep = "")}: missing value where TRUE/FALSE needed</span></div><div class='output co'>#> <span class='message'>Timing stopped at: 0.002 0 0.003</span></div><div class='input'><span class='no'>fit.2.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.2</span>)</div><div class='output co'>#> <span class='error'>Error in summary(fit.2): object 'fit.2' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.2.s</span>$<span class='no'>par</span>, <span class='fl'>3</span>)</div><div class='output co'>#> <span class='error'>Error in print(fit.2.s$par, 3): object 'fit.2.s' not found</span></div><div class='input'><span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.2.s</span>$<span class='no'>bpar</span>, <span class='fl'>3</span>)</div><div class='output co'>#> <span class='error'>Error in print(fit.2.s$bpar, 3): object 'fit.2.s' not found</span></div><div class='input'><span class='co'># }</span> <span class='no'>initials</span> <span class='kw'><-</span> <span class='no'>fit</span>$<span class='no'>start</span>$<span class='no'>value</span> <span class='fu'><a href='https://rdrr.io/r/base/names.html'>names</a></span>(<span class='no'>initials</span>) <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/colnames.html'>rownames</a></span>(<span class='no'>fit</span>$<span class='no'>start</span>) @@ -256,7 +256,7 @@ This is no problem for the internal use in <code><a href='mkinfit.html'>mkinfit< <span class='kw'>parent</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>, <span class='kw'>to</span> <span class='kw'>=</span> <span class='st'>"m1"</span>, <span class='kw'>sink</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>), <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> -<span class='no'>fit.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit.ff.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.ff</span>) +<span class='no'>fit.ff</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165</span></div><div class='input'><span class='no'>fit.ff.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.ff</span>) <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.ff.s</span>$<span class='no'>par</span>, <span class='fl'>3</span>)</div><div class='output co'>#> Estimate Std. Error Lower Upper #> parent_0 99.598 1.5702 96.4038 102.793 #> log_k_parent -2.316 0.0409 -2.3988 -2.233 @@ -277,7 +277,7 @@ This is no problem for the internal use in <code><a href='mkinfit.html'>mkinfit< <span class='kw'>m1</span> <span class='kw'>=</span> <span class='fu'><a href='https://rdrr.io/r/base/list.html'>list</a></span>(<span class='kw'>type</span> <span class='kw'>=</span> <span class='st'>"SFO"</span>), <span class='kw'>use_of_ff</span> <span class='kw'>=</span> <span class='st'>"max"</span>)</div><div class='output co'>#> <span class='message'>Successfully compiled differential equation model from auto-generated C code.</span></div><div class='input'> -<span class='no'>fit.ff.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='input'><span class='no'>fit.ff.2.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.ff.2</span>) +<span class='no'>fit.ff.2</span> <span class='kw'><-</span> <span class='fu'><a href='mkinfit.html'>mkinfit</a></span>(<span class='no'>SFO_SFO.ff.2</span>, <span class='no'>FOCUS_2006_D</span>, <span class='kw'>quiet</span> <span class='kw'>=</span> <span class='fl'>TRUE</span>)</div><div class='output co'>#> <span class='warning'>Warning: Observations with value of zero were removed from the data</span></div><div class='output co'>#> <span class='warning'>Warning: Shapiro-Wilk test for standardized residuals: p = 0.0242</span></div><div class='input'><span class='no'>fit.ff.2.s</span> <span class='kw'><-</span> <span class='fu'><a href='https://rdrr.io/r/base/summary.html'>summary</a></span>(<span class='no'>fit.ff.2</span>) <span class='fu'><a href='https://rdrr.io/r/base/print.html'>print</a></span>(<span class='no'>fit.ff.2.s</span>$<span class='no'>par</span>, <span class='fl'>3</span>)</div><div class='output co'>#> Estimate Std. Error Lower Upper #> parent_0 84.79 3.012 78.67 90.91 #> log_k_parent -2.76 0.082 -2.92 -2.59 diff --git a/docs/dev/sitemap.xml b/docs/dev/sitemap.xml index e284abf6..81368436 100644 --- a/docs/dev/sitemap.xml +++ b/docs/dev/sitemap.xml @@ -172,9 +172,6 @@ <loc>https://pkgdown.jrwb.de/mkin/reference/residuals.mkinfit.html</loc> </url> <url> - <loc>https://pkgdown.jrwb.de/mkin/reference/saemix.html</loc> - </url> - <url> <loc>https://pkgdown.jrwb.de/mkin/reference/schaefer07_complex_case.html</loc> </url> <url> diff --git a/man/saemix.Rd b/man/saemix.Rd deleted file mode 100644 index eedbf537..00000000 --- a/man/saemix.Rd +++ /dev/null @@ -1,54 +0,0 @@ -% Generated by roxygen2: do not edit by hand -% Please edit documentation in R/saemix.R -\name{saemix_model} -\alias{saemix_model} -\alias{saemix_data} -\title{Create saemix models from mmkin row objects} -\usage{ -saemix_model(object, cores = parallel::detectCores()) - -saemix_data(object, ...) -} -\arguments{ -\item{object}{An mmkin row object containing several fits of the same model to different datasets} - -\item{cores}{The number of cores to be used for multicore processing. -On Windows machines, cores > 1 is currently not supported.} - -\item{\dots}{Further parameters passed to \link[saemix:saemixData]{saemix::saemixData}} -} -\value{ -An \link[saemix:SaemixModel-class]{saemix::SaemixModel} object. - -An \link[saemix:SaemixData-class]{saemix::SaemixData} object. -} -\description{ -This function sets up a nonlinear mixed effects model for an mmkin row -object for use with the saemix package. An mmkin row object is essentially a -list of mkinfit objects that have been obtained by fitting the same model to -a list of datasets. -} -\details{ -Starting values for the fixed effects (population mean parameters, argument psi0 of -\code{\link[saemix:saemixModel]{saemix::saemixModel()}} are the mean values of the parameters found using -mmkin. Starting variances of the random effects (argument omega.init) are the -variances of the deviations of the parameters from these mean values. -} -\examples{ -ds <- lapply(experimental_data_for_UBA_2019[6:10], - function(x) subset(x$data[c("name", "time", "value")])) -names(ds) <- paste("Dataset", 6:10) -sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"), - A1 = mkinsub("SFO")) -\dontrun{ -f_mmkin <- mmkin(list("SFO-SFO" = sfo_sfo), ds, quiet = TRUE) -library(saemix) -m_saemix <- saemix_model(f_mmkin) -d_saemix <- saemix_data(f_mmkin) -saemix_options <- list(seed = 123456, - save = FALSE, save.graphs = FALSE, displayProgress = FALSE, - nbiter.saemix = c(200, 80)) -f_saemix <- saemix(m_saemix, d_saemix, saemix_options) -plot(f_saemix, plot.type = "convergence") -} -} @@ -6,18 +6,18 @@ Testing mkin ✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.9 s] ✔ | 4 | Calculation of FOCUS chi2 error levels [0.4 s] ✔ | 7 | Fitting the SFORB model [3.3 s] -✔ | 5 | Analytical solutions for coupled models [3.2 s] +✔ | 5 | Analytical solutions for coupled models [3.1 s] ✔ | 5 | Calculation of Akaike weights -✔ | 10 | Confidence intervals and p-values [1.1 s] -✔ | 14 | Error model fitting [3.8 s] -✔ | 4 | Test fitting the decline of metabolites from their maximum [0.3 s] +✔ | 10 | Confidence intervals and p-values [1.0 s] +✔ | 14 | Error model fitting [3.6 s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.2 s] ✔ | 1 | Fitting the logistic model [0.2 s] ✔ | 1 | Test dataset class mkinds used in gmkin ✔ | 1 | mkinfit features [0.2 s] ✔ | 12 | Special cases of mkinfit calls [0.6 s] ✔ | 8 | mkinmod model generation and printing [0.2 s] ✔ | 3 | Model predictions with mkinpredict [0.4 s] -✔ | 14 2 | Evaluations according to 2015 NAFTA guidance [1.1 s] +✔ | 14 2 | Evaluations according to 2015 NAFTA guidance [1.0 s] ──────────────────────────────────────────────────────────────────────────────── test_nafta.R:25: skip: Test data from Appendix B are correctly evaluated Reason: getRversion() < "4.1.0" is TRUE @@ -26,7 +26,7 @@ test_nafta.R:53: skip: Test data from Appendix D are correctly evaluated Reason: getRversion() < "4.1.0" is TRUE ──────────────────────────────────────────────────────────────────────────────── ✔ | 9 | Nonlinear mixed-effects models [7.7 s] -✔ | 0 1 | Plotting [0.7 s] +✔ | 0 1 | Plotting [0.6 s] ──────────────────────────────────────────────────────────────────────────────── test_plot.R:24: skip: Plotting mkinfit and mmkin objects is reproducible Reason: getRversion() < "4.1.0" is TRUE @@ -35,12 +35,12 @@ Reason: getRversion() < "4.1.0" is TRUE ✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.5 s] ✔ | 4 | Summary [0.1 s] ✔ | 1 | Summaries of old mkinfit objects -✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.2 s] -✔ | 9 | Hypothesis tests [6.8 s] -✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.4 s] +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.1 s] +✔ | 9 | Hypothesis tests [6.6 s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.5 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 37.1 s +Duration: 36.5 s OK: 146 Failed: 0 diff --git a/test_dev.log b/test_dev.log index b68a7e56..b5f8fd8e 100644 --- a/test_dev.log +++ b/test_dev.log @@ -1,36 +1,37 @@ Loading mkin Testing mkin ✔ | OK F W S | Context -
⠏ | 0 | AIC calculation
✔ | 4 | AIC calculation -
⠏ | 0 | Export dataset for reading into CAKE
✔ | 2 | Export dataset for reading into CAKE -
⠏ | 0 | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠋ | 1 | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠹ | 3 | Results for FOCUS D established in expertise for UBA (Ranke 2014)
⠏ | 10 | Results for FOCUS D established in expertise for UBA (Ranke 2014)
✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8 s] -
⠏ | 0 | Calculation of FOCUS chi2 error levels
⠙ | 2 | Calculation of FOCUS chi2 error levels
⠸ | 4 | Calculation of FOCUS chi2 error levels
✔ | 4 | Calculation of FOCUS chi2 error levels [0.4 s] -
⠏ | 0 | Fitting the SFORB model
⠋ | 1 | Fitting the SFORB model
⠼ | 5 | Fitting the SFORB model
✔ | 7 | Fitting the SFORB model [3.0 s] -
⠏ | 0 | Analytical solutions for coupled models
⠋ | 1 | Analytical solutions for coupled models
⠙ | 2 | Analytical solutions for coupled models
⠹ | 3 | Analytical solutions for coupled models
⠸ | 4 | Analytical solutions for coupled models
⠼ | 5 | Analytical solutions for coupled models
✔ | 5 | Analytical solutions for coupled models [2.9 s] -
⠏ | 0 | Calculation of Akaike weights
✔ | 5 | Calculation of Akaike weights -
⠏ | 0 | Confidence intervals and p-values
⠙ | 2 | Confidence intervals and p-values
⠏ | 10 | Confidence intervals and p-values
✔ | 10 | Confidence intervals and p-values [0.9 s] -
⠏ | 0 | Error model fitting
⠹ | 3 | Error model fitting
⠸ | 4 | Error model fitting
⠼ | 5 | Error model fitting
⠴ | 6 | Error model fitting
⠧ | 8 | Error model fitting
⠏ | 10 | Error model fitting
⠙ | 12 | Error model fitting
⠸ | 13 1 | Error model fitting
⠸ | 13 1 | Error model fitting
⠸ | 13 1 | Error model fitting
⠸ | 13 1 | Error model fitting
⠸ | 14 | Error model fitting
✔ | 14 | Error model fitting [3.7 s] -
⠏ | 0 | Test fitting the decline of metabolites from their maximum
⠹ | 3 | Test fitting the decline of metabolites from their maximum
⠸ | 4 | Test fitting the decline of metabolites from their maximum
✔ | 4 | Test fitting the decline of metabolites from their maximum [0.2 s] -
⠏ | 0 | Fitting the logistic model
⠋ | 1 | Fitting the logistic model
✔ | 1 | Fitting the logistic model [0.2 s] -
⠏ | 0 | Test dataset class mkinds used in gmkin
✔ | 1 | Test dataset class mkinds used in gmkin -
⠏ | 0 | Special cases of mkinfit calls
⠇ | 9 | Special cases of mkinfit calls
⠏ | 10 | Special cases of mkinfit calls
⠋ | 11 | Special cases of mkinfit calls
⠙ | 12 | Special cases of mkinfit calls
✔ | 12 | Special cases of mkinfit calls [0.6 s] -
⠏ | 0 | mkinmod model generation and printing
⠧ | 8 | mkinmod model generation and printing
✔ | 8 | mkinmod model generation and printing [0.2 s] -
⠏ | 0 | Model predictions with mkinpredict
⠋ | 1 | Model predictions with mkinpredict
✔ | 3 | Model predictions with mkinpredict [0.3 s] -
⠏ | 0 | Evaluations according to 2015 NAFTA guidance
⠙ | 2 | Evaluations according to 2015 NAFTA guidance
⠇ | 9 | Evaluations according to 2015 NAFTA guidance
⠏ | 10 | Evaluations according to 2015 NAFTA guidance
⠴ | 16 | Evaluations according to 2015 NAFTA guidance
✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.4 s] -
⠏ | 0 | Nonlinear mixed-effects models
⠋ | 1 | Nonlinear mixed-effects models
⠙ | 2 | Nonlinear mixed-effects models
⠸ | 4 | Nonlinear mixed-effects models
⠼ | 5 | Nonlinear mixed-effects models
⠴ | 6 | Nonlinear mixed-effects models
⠧ | 8 | Nonlinear mixed-effects models
⠇ | 9 | Nonlinear mixed-effects models
✔ | 9 | Nonlinear mixed-effects models [7.0 s] -
⠏ | 0 | Plotting
⠋ | 1 | Plotting
⠸ | 4 | Plotting
⠦ | 7 | Plotting
⠇ | 9 | Plotting
⠙ | 12 | Plotting
✔ | 14 | Plotting [1.3 s] -
⠏ | 0 | Residuals extracted from mkinfit models
✔ | 4 | Residuals extracted from mkinfit models -
⠏ | 0 | Complex test case from Schaefer et al. (2007) Piacenza paper
⠋ | 1 | Complex test case from Schaefer et al. (2007) Piacenza paper
✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.3 s] -
⠏ | 0 | Summary
✔ | 4 | Summary -
⠏ | 0 | Summaries of old mkinfit objects
✔ | 1 | Summaries of old mkinfit objects -
⠏ | 0 | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠋ | 1 | Results for synthetic data established in expertise for UBA (Ranke 2014)
⠹ | 3 | Results for synthetic data established in expertise for UBA (Ranke 2014)
✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.0 s] -
⠏ | 0 | Hypothesis tests
⠼ | 5 | Hypothesis tests
⠴ | 6 | Hypothesis tests
⠧ | 8 | Hypothesis tests
⠇ | 9 | Hypothesis tests
✔ | 9 | Hypothesis tests [7.1 s] -
⠏ | 0 | Calculation of maximum time weighted average concentrations (TWAs)
⠋ | 1 | Calculation of maximum time weighted average concentrations (TWAs)
⠙ | 2 | Calculation of maximum time weighted average concentrations (TWAs)
⠹ | 3 | Calculation of maximum time weighted average concentrations (TWAs)
⠸ | 4 | Calculation of maximum time weighted average concentrations (TWAs)
✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2 s] +✔ | 4 | AIC calculation +✔ | 2 | Export dataset for reading into CAKE +✔ | 14 | Results for FOCUS D established in expertise for UBA (Ranke 2014) [0.8 s] +✔ | 4 | Calculation of FOCUS chi2 error levels [0.4 s] +✔ | 7 | Fitting the SFORB model [3.0 s] +✔ | 5 | Analytical solutions for coupled models [2.9 s] +✔ | 5 | Calculation of Akaike weights +✔ | 10 | Confidence intervals and p-values [0.9 s] +✔ | 14 | Error model fitting [3.4 s] +✔ | 4 | Test fitting the decline of metabolites from their maximum [0.2 s] +✔ | 1 | Fitting the logistic model [0.2 s] +✔ | 1 | Test dataset class mkinds used in gmkin +✔ | 1 | mkinfit features [0.2 s] +✔ | 12 | Special cases of mkinfit calls [0.6 s] +✔ | 8 | mkinmod model generation and printing [0.2 s] +✔ | 3 | Model predictions with mkinpredict [0.3 s] +✔ | 16 | Evaluations according to 2015 NAFTA guidance [1.3 s] +✔ | 9 | Nonlinear mixed-effects models [7.0 s] +✔ | 14 | Plotting [1.3 s] +✔ | 4 | Residuals extracted from mkinfit models +✔ | 2 | Complex test case from Schaefer et al. (2007) Piacenza paper [1.3 s] +✔ | 4 | Summary +✔ | 1 | Summaries of old mkinfit objects +✔ | 4 | Results for synthetic data established in expertise for UBA (Ranke 2014) [2.0 s] +✔ | 9 | Hypothesis tests [7.1 s] +✔ | 4 | Calculation of maximum time weighted average concentrations (TWAs) [2.2 s] ══ Results ═════════════════════════════════════════════════════════════════════ -Duration: 35.7 s +Duration: 35.6 s -OK: 161 +OK: 162 Failed: 0 Warnings: 0 Skipped: 0 diff --git a/tests/testthat/FOCUS_2006_D.csf b/tests/testthat/FOCUS_2006_D.csf index a340a0a5..115d28f4 100644 --- a/tests/testthat/FOCUS_2006_D.csf +++ b/tests/testthat/FOCUS_2006_D.csf @@ -5,7 +5,7 @@ Description: MeasurementUnits: % AR TimeUnits: days Comments: Created using mkin::CAKE_export -Date: 2020-07-17 +Date: 2020-10-08 Optimiser: IRLS [Data] diff --git a/tests/testthat/summary_DFOP_FOCUS_C.txt b/tests/testthat/summary_DFOP_FOCUS_C.txt index ab64a588..b9ba7c17 100644 --- a/tests/testthat/summary_DFOP_FOCUS_C.txt +++ b/tests/testthat/summary_DFOP_FOCUS_C.txt @@ -66,8 +66,8 @@ All data 2.661 4 5 parent 2.661 4 5 Estimated disappearance times: - DT50 DT90 DT50_k1 DT50_k2 -parent 1.887 21.25 1.508 38.83 + DT50 DT90 DT50back DT50_k1 DT50_k2 +parent 1.887 21.25 6.397 1.508 38.83 Data: time variable observed predicted residual diff --git a/vignettes/FOCUS_D.html b/vignettes/FOCUS_D.html index 16bc2084..ea6acdbe 100644 --- a/vignettes/FOCUS_D.html +++ b/vignettes/FOCUS_D.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-26" /> +<meta name="date" content="2020-10-08" /> <title>Example evaluation of FOCUS Example Dataset D</title> @@ -365,7 +365,7 @@ summary { <h1 class="title toc-ignore">Example evaluation of FOCUS Example Dataset D</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-26</h4> +<h4 class="date">2020-10-08</h4> </div> @@ -431,6 +431,8 @@ print(FOCUS_2006_D)</code></pre> <pre class="r"><code>fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)</code></pre> <pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Observations with value ## of zero were removed from the data</code></pre> +<pre><code>## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE): Shapiro-Wilk test for +## standardized residuals: p = 0.0165</code></pre> <p>A plot of the fit including a residual plot for both observed variables is obtained using the <code>plot_sep</code> method for <code>mkinfit</code> objects, which shows separate graphs for all compounds and their residuals.</p> <pre class="r"><code>plot_sep(fit, lpos = c("topright", "bottomright"))</code></pre> <p><img 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width="768" /></p> @@ -440,9 +442,9 @@ print(FOCUS_2006_D)</code></pre> <p>A comprehensive report of the results is obtained using the <code>summary</code> method for <code>mkinfit</code> objects.</p> <pre class="r"><code>summary(fit)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:07 2020 -## Date of summary: Tue May 26 17:01:07 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:19 2020 +## Date of summary: Thu Oct 8 09:06:19 2020 ## ## Equations: ## d_parent/dt = - k_parent * parent @@ -450,7 +452,7 @@ print(FOCUS_2006_D)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 421 model solutions performed in 0.149 s +## Fitted using 421 model solutions performed in 0.152 s ## ## Error model: Constant variance ## @@ -474,6 +476,11 @@ print(FOCUS_2006_D)</code></pre> ## value type ## m1_0 0 state ## +## +## Warning(s): +## Observations with value of zero were removed from the data +## Shapiro-Wilk test for standardized residuals: p = 0.0165 +## ## Results: ## ## AIC BIC logLik diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 7573ef58..c7722f37 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-26" /> +<meta name="date" content="2020-10-08" /> <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> @@ -1518,7 +1518,7 @@ div.tocify { <h1 class="title toc-ignore">Example evaluation of FOCUS Laboratory Data L1 to L3</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-26</h4> +<h4 class="date">2020-10-08</h4> </div> @@ -1538,30 +1538,30 @@ FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1)</code></pre> <pre class="r"><code>m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet = TRUE) summary(m.L1.SFO)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:08 2020 -## Date of summary: Tue May 26 17:01:08 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:20 2020 +## Date of summary: Thu Oct 8 09:06:20 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 133 model solutions performed in 0.031 s +## Fitted using 133 model solutions performed in 0.032 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 89.85 state -## k_parent_sink 0.10 deparm +## value type +## parent_0 89.85 state +## k_parent 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 89.850000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 89.850000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -1572,25 +1572,25 @@ summary(m.L1.SFO)</code></pre> ## 93.88778 96.5589 -43.94389 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 92.470 1.28200 89.740 95.200 -## log_k_parent_sink -2.347 0.03763 -2.428 -2.267 -## sigma 2.780 0.46330 1.792 3.767 +## Estimate Std. Error Lower Upper +## parent_0 92.470 1.28200 89.740 95.200 +## log_k_parent -2.347 0.03763 -2.428 -2.267 +## sigma 2.780 0.46330 1.792 3.767 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 6.186e-01 -1.516e-09 -## log_k_parent_sink 6.186e-01 1.000e+00 -3.124e-09 -## sigma -1.516e-09 -3.124e-09 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 6.186e-01 -1.516e-09 +## log_k_parent 6.186e-01 1.000e+00 -3.124e-09 +## sigma -1.516e-09 -3.124e-09 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 -## k_parent_sink 0.09561 26.57 2.487e-14 0.08824 0.1036 -## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 +## Estimate t value Pr(>t) Lower Upper +## parent_0 92.47000 72.13 8.824e-21 89.74000 95.2000 +## k_parent 0.09561 26.57 2.487e-14 0.08824 0.1036 +## sigma 2.78000 6.00 1.216e-05 1.79200 3.7670 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -1639,21 +1639,16 @@ summary(m.L1.SFO)</code></pre> <pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is ## doubtful</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 -## -## -## Warning: Optimisation did not converge: -## false convergence (8) -## +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 380 model solutions performed in 0.08 s +## Fitted using 380 model solutions performed in 0.088 s ## ## Error model: Constant variance ## @@ -1674,6 +1669,11 @@ summary(m.L1.SFO)</code></pre> ## Fixed parameter values: ## None ## +## +## Warning(s): +## Optimisation did not converge: +## false convergence (8) +## ## Results: ## ## AIC BIC logLik @@ -1744,16 +1744,16 @@ plot(m.L2.FOMC, show_residuals = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.FOMC, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 239 model solutions performed in 0.047 s +## Fitted using 239 model solutions performed in 0.05 s ## ## Error model: Constant variance ## @@ -1822,9 +1822,9 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, <p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>summary(m.L2.DFOP, data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:09 2020 -## Date of summary: Tue May 26 17:01:09 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:21 2020 +## Date of summary: Thu Oct 8 09:06:21 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1833,7 +1833,7 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## ## Model predictions using solution type analytical ## -## Fitted using 572 model solutions performed in 0.13 s +## Fitted using 572 model solutions performed in 0.136 s ## ## Error model: Constant variance ## @@ -1894,8 +1894,8 @@ plot(m.L2.DFOP, show_residuals = TRUE, show_errmin = TRUE, ## parent 2.53 4 2 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 0.5335 5.311 0.03009 2.058</code></pre> +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 0.5335 5.311 1.599 0.03009 2.058</code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the chi^2 error level criterion. However, the failure to calculate the covariance matrix indicates that the parameter estimates correlate excessively. Therefore, the FOMC model may be preferred for this dataset.</p> </div> </div> @@ -1922,9 +1922,9 @@ plot(mm.L3)</code></pre> <p>We can extract the summary and plot for <em>e.g.</em> the DFOP fit, using square brackets for indexing which will result in the use of the summary and plot functions working on mkinfit objects.</p> <pre class="r"><code>summary(mm.L3[["DFOP", 1]])</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -1933,7 +1933,7 @@ plot(mm.L3)</code></pre> ## ## Model predictions using solution type analytical ## -## Fitted using 373 model solutions performed in 0.083 s +## Fitted using 373 model solutions performed in 0.085 s ## ## Error model: Constant variance ## @@ -1994,8 +1994,8 @@ plot(mm.L3)</code></pre> ## parent 2.225 4 4 ## ## Estimated disappearance times: -## DT50 DT90 DT50_k1 DT50_k2 -## parent 7.464 123 1.343 50.37 +## DT50 DT90 DT50back DT50_k1 DT50_k2 +## parent 7.464 123 37.03 1.343 50.37 ## ## Data: ## time variable observed predicted residual @@ -2030,30 +2030,30 @@ plot(mm.L4)</code></pre> <p>The <span class="math inline"><em>χ</em><sup>2</sup></span> error level of 3.3% as well as the plot suggest that the SFO model fits very well. The error level at which the <span class="math inline"><em>χ</em><sup>2</sup></span> test passes is slightly lower for the FOMC model. However, the difference appears negligible.</p> <pre class="r"><code>summary(mm.L4[["SFO", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: -## d_parent/dt = - k_parent_sink * parent +## d_parent/dt = - k_parent * parent ## ## Model predictions using solution type analytical ## -## Fitted using 142 model solutions performed in 0.029 s +## Fitted using 142 model solutions performed in 0.03 s ## ## Error model: Constant variance ## ## Error model algorithm: OLS ## ## Starting values for parameters to be optimised: -## value type -## parent_0 96.6 state -## k_parent_sink 0.1 deparm +## value type +## parent_0 96.6 state +## k_parent 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 96.600000 -Inf Inf -## log_k_parent_sink -2.302585 -Inf Inf +## value lower upper +## parent_0 96.600000 -Inf Inf +## log_k_parent -2.302585 -Inf Inf ## ## Fixed parameter values: ## None @@ -2064,25 +2064,25 @@ plot(mm.L4)</code></pre> ## 47.12133 47.35966 -20.56067 ## ## Optimised, transformed parameters with symmetric confidence intervals: -## Estimate Std. Error Lower Upper -## parent_0 96.440 1.69900 92.070 100.800 -## log_k_parent_sink -5.030 0.07059 -5.211 -4.848 -## sigma 3.162 0.79050 1.130 5.194 +## Estimate Std. Error Lower Upper +## parent_0 96.440 1.69900 92.070 100.800 +## log_k_parent -5.030 0.07059 -5.211 -4.848 +## sigma 3.162 0.79050 1.130 5.194 ## ## Parameter correlation: -## parent_0 log_k_parent_sink sigma -## parent_0 1.000e+00 5.938e-01 3.387e-07 -## log_k_parent_sink 5.938e-01 1.000e+00 5.830e-07 -## sigma 3.387e-07 5.830e-07 1.000e+00 +## parent_0 log_k_parent sigma +## parent_0 1.000e+00 5.938e-01 3.387e-07 +## log_k_parent 5.938e-01 1.000e+00 5.830e-07 +## sigma 3.387e-07 5.830e-07 1.000e+00 ## ## Backtransformed parameters: ## Confidence intervals for internally transformed parameters are asymmetric. ## t-test (unrealistically) based on the assumption of normal distribution ## for estimators of untransformed parameters. -## Estimate t value Pr(>t) Lower Upper -## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 -## k_parent_sink 0.006541 14.17 1.578e-05 0.005455 7.842e-03 -## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 +## Estimate t value Pr(>t) Lower Upper +## parent_0 96.440000 56.77 1.604e-08 92.070000 1.008e+02 +## k_parent 0.006541 14.17 1.578e-05 0.005455 7.842e-03 +## sigma 3.162000 4.00 5.162e-03 1.130000 5.194e+00 ## ## FOCUS Chi2 error levels in percent: ## err.min n.optim df @@ -2094,16 +2094,16 @@ plot(mm.L4)</code></pre> ## parent 106 352</code></pre> <pre class="r"><code>summary(mm.L4[["FOMC", 1]], data = FALSE)</code></pre> <pre><code>## mkin version used for fitting: 0.9.50.3 -## R version used for fitting: 4.0.0 -## Date of fit: Tue May 26 17:01:10 2020 -## Date of summary: Tue May 26 17:01:10 2020 +## R version used for fitting: 4.0.2 +## Date of fit: Thu Oct 8 09:06:22 2020 +## Date of summary: Thu Oct 8 09:06:22 2020 ## ## Equations: ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent ## ## Model predictions using solution type analytical ## -## Fitted using 224 model solutions performed in 0.044 s +## Fitted using 224 model solutions performed in 0.046 s ## ## Error model: Constant variance ## diff --git a/vignettes/mkin.html b/vignettes/mkin.html index e14cb374..8d9989a2 100644 --- a/vignettes/mkin.html +++ b/vignettes/mkin.html @@ -11,7 +11,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-26" /> +<meta name="date" content="2020-10-08" /> <title>Introduction to mkin</title> @@ -1583,12 +1583,12 @@ div.tocify { <h1 class="title toc-ignore">Introduction to mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-26</h4> +<h4 class="date">2020-10-08</h4> </div> -<p><a href="http://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke">Privatdozent at the University of Bremen</a></p> +<p><a href="https://www.jrwb.de">Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany</a><br /> <a href="http://chem.uft.uni-bremen.de/ranke/">Privatdozent at the University of Bremen</a></p> <div id="abstract" class="section level1"> <h1>Abstract</h1> <p>In the regulatory evaluation of chemical substances like plant protection products (pesticides), biocides and other chemicals, degradation data play an important role. For the evaluation of pesticide degradation experiments, detailed guidance has been developed, based on nonlinear optimisation. The <code>R</code> add-on package <code>mkin</code> implements fitting some of the models recommended in this guidance from within R and calculates some statistical measures for data series within one or more compartments, for parent and metabolites.</p> @@ -1627,7 +1627,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <p>Many approaches are possible regarding the evaluation of chemical degradation data.</p> <p>The <code>mkin</code> package <span class="citation">(Ranke 2019)</span> implements the approach recommended in the kinetics report provided by the FOrum for Co-ordination of pesticide fate models and their USe <span class="citation">(FOCUS Work Group on Degradation Kinetics 2006, 2014)</span> for simple decline data series, data series with transformation products, commonly termed metabolites, and for data series for more than one compartment. It is also possible to include back reactions, so equilibrium reactions and equilibrium partitioning can be specified, although this oftentimes leads to an overparameterisation of the model.</p> <p>When the first <code>mkin</code> code was published in 2010, the most commonly used tools for fitting more complex kinetic degradation models to experimental data were KinGUI <span class="citation">(Schäfer et al. 2007)</span>, a MATLAB based tool with a graphical user interface that was specifically tailored to the task and included some output as proposed by the FOCUS Kinetics Workgroup, and ModelMaker, a general purpose compartment based tool providing infrastructure for fitting dynamic simulation models based on differential equations to data.</p> -<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> +<p>The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see <em>e.g.</em> <a href="https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770">the initial commit on 11 May 2010</a>), where the code is still occasionally updated.</p> <p>At that time, the R package <code>FME</code> (Flexible Modelling Environment) <span class="citation">(Soetaert and Petzoldt 2010)</span> was already available, and provided a good basis for developing a package specifically tailored to the task. The remaining challenge was to make it as easy as possible for the users (including the author of this vignette) to specify the system of differential equations and to include the output requested by the FOCUS guidance, such as the relative standard deviation that has to be assumed for the residuals, such that the <span class="math inline">\(\chi^2\)</span> goodness-of-fit test as defined by the FOCUS kinetics workgroup would pass using an significance level <span class="math inline">\(\alpha\)</span> of 0.05. This relative error, expressed as a percentage, is often termed <span class="math inline">\(\chi^2\)</span> error level or similar.</p> <p>Also, <code>mkin</code> introduced using analytical solutions for parent only kinetics for improved optimization speed. Later, Eigenvalue based solutions were introduced to <code>mkin</code> for the case of linear differential equations (<em>i.e.</em> where the FOMC or DFOP models were not used for the parent compound), greatly improving the optimization speed for these cases. This, however, has become somehow obsolete, as the use of compiled code described below gives even smaller execution times.</p> <p>The possibility to specify back-reactions and a biphasic model (SFORB) for metabolites were present in <code>mkin</code> from the very beginning.</p> @@ -1636,7 +1636,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <p>Soon after the publication of <code>mkin</code>, two derived tools were published, namely KinGUII (available from Bayer Crop Science) and CAKE (commissioned to Tessella by Syngenta), which added a graphical user interface (GUI), and added fitting by iteratively reweighted least squares (IRLS) and characterisation of likely parameter distributions by Markov Chain Monte Carlo (MCMC) sampling.</p> <p>CAKE focuses on a smooth use experience, sacrificing some flexibility in the model definition, originally allowing only two primary metabolites in parallel. The current version 3.3 of CAKE release in March 2016 uses a basic scheme for up to six metabolites in a flexible arrangement, but does not support back-reactions (non-instantaneous equilibria) or biphasic kinetics for metabolites.</p> <p>KinGUI offers an even more flexible widget for specifying complex kinetic models. Back-reactions (non-instantaneous equilibria) were supported early on, but until 2014, only simple first-order models could be specified for transformation products. Starting with KinGUII version 2.1, biphasic modelling of metabolites was also available in KinGUII.</p> -<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> +<p>A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named <code>gmkin</code>. Please see its <a href="https://pkgdown.jrwb.de/gmkin/">documentation page</a> and <a href="https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html">manual</a> for further information.</p> <p>A comparison of scope, usability and numerical results obtained with these tools has been recently been published by <span class="citation">Ranke, Wöltjen, and Meinecke (2018)</span>.</p> </div> <div id="recent-developments" class="section level2"> @@ -1697,7 +1697,7 @@ plot_sep(f_SFO_SFO_SFO, lpos = c("topright", "bottomright", <p>Schäfer, D., B. Mikolasch, P. Rainbird, and B. Harvey. 2007. “KinGUI: A New Kinetic Software Tool for Evaluations According to FOCUS Degradation Kinetics.” In <em>Proceedings of the Xiii Symposium Pesticide Chemistry</em>, edited by Del Re A. A. M., Capri E., Fragoulis G., and Trevisan M., 916–23. Piacenza.</p> </div> <div id="ref-soetaert2010"> -<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="http://www.jstatsoft.org/v33/i03/" class="uri">http://www.jstatsoft.org/v33/i03/</a>.</p> +<p>Soetaert, Karline, and Thomas Petzoldt. 2010. “Inverse Modelling, Sensitivity and Monte Carlo Analysis in R Using Package FME.” <em>Journal of Statistical Software</em> 33 (3): 1–28. <a href="https://www.jstatsoft.org/v33/i03/" class="uri">https://www.jstatsoft.org/v33/i03/</a>.</p> </div> </div> </div> diff --git a/vignettes/mkin.rmd b/vignettes/mkin.rmd index acca0e44..a672f2a6 100644 --- a/vignettes/mkin.rmd +++ b/vignettes/mkin.rmd @@ -15,8 +15,8 @@ vignette: > %\VignetteEncoding{UTF-8} --- -[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](http://www.jrwb.de)<br /> -[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke) +[Wissenschaftlicher Berater, Kronacher Str. 12, 79639 Grenzach-Wyhlen, Germany](https://www.jrwb.de)<br /> +[Privatdozent at the University of Bremen](http://chem.uft.uni-bremen.de/ranke/) ```{r, include = FALSE} require(knitr) @@ -88,7 +88,7 @@ models based on differential equations to data. The code was first uploaded to the BerliOS platform. When this was taken down, the version control history was imported into the R-Forge site (see *e.g.* -[the initial commit on 11 May 2010](http://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)), +[the initial commit on 11 May 2010](https://cgit.jrwb.de/mkin/commit/?id=30cbb4092f6d2d3beff5800603374a0d009ad770)), where the code is still occasionally updated. At that time, the R package `FME` (Flexible Modelling Environment) @@ -135,7 +135,7 @@ of metabolites was also available in KinGUII. A further graphical user interface (GUI) that has recently been brought to a decent degree of maturity is the browser based GUI named `gmkin`. Please see its -[documentation page](https://pkgdown.jrwb.de/gmkin) and +[documentation page](https://pkgdown.jrwb.de/gmkin/) and [manual](https://pkgdown.jrwb.de/gmkin/articles/gmkin_manual.html) for further information. diff --git a/vignettes/references.bib b/vignettes/references.bib index a18922c9..69ef74a7 100644 --- a/vignettes/references.bib +++ b/vignettes/references.bib @@ -76,7 +76,7 @@ volume = {33}, pages = {1--28}, number = {3}, - url = {http://www.jstatsoft.org/v33/i03/} + url = {https://www.jstatsoft.org/v33/i03/} } @Inproceedings{ ranke2012, diff --git a/vignettes/twa.html b/vignettes/twa.html index 80272eef..663625bf 100644 --- a/vignettes/twa.html +++ b/vignettes/twa.html @@ -12,7 +12,7 @@ <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2020-05-26" /> +<meta name="date" content="2020-10-08" /> <title>Calculation of time weighted average concentrations with mkin</title> @@ -215,7 +215,7 @@ code > span.er { color: #a61717; background-color: #e3d2d2; } <h1 class="title toc-ignore">Calculation of time weighted average concentrations with mkin</h1> <h4 class="author">Johannes Ranke</h4> -<h4 class="date">2020-05-26</h4> +<h4 class="date">2020-10-08</h4> diff --git a/vignettes/web_only/NAFTA_examples.html b/vignettes/web_only/NAFTA_examples.html index f1b3fa03..dda93242 100644 --- a/vignettes/web_only/NAFTA_examples.html +++ b/vignettes/web_only/NAFTA_examples.html @@ -1,17 +1,17 @@ <!DOCTYPE html> -<html xmlns="http://www.w3.org/1999/xhtml"> +<html> <head> <meta charset="utf-8" /> -<meta http-equiv="Content-Type" content="text/html; charset=utf-8" /> <meta name="generator" content="pandoc" /> +<meta http-equiv="X-UA-Compatible" content="IE=EDGE" /> <meta name="author" content="Johannes Ranke" /> -<meta name="date" content="2019-04-10" /> +<meta name="date" content="2020-10-08" /> <title>Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</title> @@ -69,8 +69,6 @@ overflow: auto; margin-left: 2%; position: fixed; border: 1px solid #ccc; -webkit-border-radius: 6px; -moz-border-radius: 6px; border-radius: 6px; } @@ -98,10 +96,15 @@ font-size: 12px; .tocify-subheader .tocify-subheader { text-indent: 30px; } - .tocify-subheader .tocify-subheader .tocify-subheader { text-indent: 40px; } +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 50px; +} +.tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader .tocify-subheader { +text-indent: 60px; +} .tocify .tocify-item > a, .tocify .nav-list .nav-header { margin: 0px; @@ -504,13 +507,13 @@ float: none; item.append($("<a/>", { - "text": self.text() + "html": self.html() })); } else { - item.text(self.text()); + item.html(self.html()); } @@ -1327,9 +1330,7 @@ h6 { </style> -</head> -<body> <style type="text/css"> .main-container { @@ -1343,7 +1344,6 @@ code { } img { max-width:100%; - height: auto; } .tabbed-pane { padding-top: 12px; @@ -1361,8 +1361,6 @@ summary { -<div class="container-fluid main-container"> - <!-- tabsets --> <style type="text/css"> @@ -1407,6 +1405,7 @@ summary { border: none; display: inline-block; border-radius: 4px; + background-color: transparent; } .tabset-dropdown > .nav-tabs.nav-tabs-open > li { @@ -1419,49 +1418,10 @@ summary { } </style> -<script> -$(document).ready(function () { - window.buildTabsets("TOC"); -}); - -$(document).ready(function () { - $('.tabset-dropdown > .nav-tabs > li').click(function () { - $(this).parent().toggleClass('nav-tabs-open') - }); -}); -</script> - <!-- code folding --> -<script> -$(document).ready(function () { - - // move toc-ignore selectors from section div to header - $('div.section.toc-ignore') - .removeClass('toc-ignore') - .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); - - // establish options - var options = { - selectors: "h1,h2,h3", - theme: "bootstrap3", - context: '.toc-content', - hashGenerator: function (text) { - return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); - }, - ignoreSelector: ".toc-ignore", - scrollTo: 0 - }; - options.showAndHide = false; - options.smoothScroll = true; - - // tocify - var toc = $("#TOC").tocify(options).data("toc-tocify"); -}); -</script> - <style type="text/css"> #TOC { @@ -1474,6 +1434,12 @@ $(document).ready(function () { } } +@media print { +.toc-content { + /* see https://github.com/w3c/csswg-drafts/issues/4434 */ + float: right; +} +} .toc-content { padding-left: 30px; @@ -1509,8 +1475,6 @@ div.tocify { .tocify-subheader .tocify-item { font-size: 0.90em; - padding-left: 25px; - text-indent: 0; } .tocify .list-group-item { @@ -1526,6 +1490,16 @@ div.tocify { </style> + + +</head> + +<body> + + +<div class="container-fluid main-container"> + + <!-- setup 3col/9col grid for toc_float and main content --> <div class="row-fluid"> <div class="col-xs-12 col-sm-4 col-md-3"> @@ -1543,8 +1517,8 @@ div.tocify { <h1 class="title toc-ignore">Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance</h1> -<h4 class="author"><em>Johannes Ranke</em></h4> -<h4 class="date"><em>2019-04-10</em></h4> +<h4 class="author">Johannes Ranke</h4> +<h4 class="date">2020-10-08</h4> </div> @@ -1563,7 +1537,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p5a)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dB1gURxuALUlQVBRpB0izIIpYwNgVC7GhYokVxBJLfo2xRMXYUCzYNUZjlIAthqhYUzQxiqbYo0YTNRpLbFGx0EHKzb97d7Rj73ZuZ27L+b3Pw3K3+zE7+/He7ezu7GwZBAAypozUFQAAY4CggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDWyFPRmp55rpa4DIA9kKeiME6iN1HUA5IEsBU3NvxcidR0AeSCNoL82d/aakYfKVLezs1uB8pf62bc5XHz5geAHxgtI5BCYax6gdCQRNN3p+7zkLutRmTTN27FBd/KOuycULT88LZ+nBH5Ba5YrX15T5Gl/p/Bc5vfSUN2S6B+F1hsQH0kEveaYh9Dvu3SCXnFMZaZHa+YVLh/ZvV9osfD8JO1P0Tt+QbNVau2LLPdLWZ3iEDpnE4putvBbip4OpLglgLmRRNB8vzZx99iVawRdN4qdqp3vMNOdLr/n1Czc26/xrDUl71SfxhHsj3qBl+dHmndII6NmYee9CAUc174uIegVzyFNl7Lfw3v7I5SZjlKbRoeiKT+hlhkTL4u3ofKAr0GlR0xE8XdSN5ykaYO++qJf1fbXNCnzQlOjNPNaHmUmGy9Mjuug++5Dx/wepw1cfsr6GmJ/DjVKzuq0hX2F2LRpF8aFoX+88rWvNan0KMOSi450/OdO483MjOW9Ahz7p6Dw+HjmG7RlkyV/vy/FBksJX4OqiN2+7BQE1fBfi3d0KVs1TjPD6yozyUazap0tCJntERjoO+BUe4TYn2mrEPp6GPsKsWnTLnzp+GrRPF1gqVRuY7480Vyf22l9Z+0YjuK1rYawh+bfNnlhrEFV1HJiSb7BTkFQtCuSmZyz1aXstFsWMz2vytEsmzegMCx6PkJ5OaeCGUGZn6mrmT8MY18hNm3ahajX4Ua3dIElUnmG2ZEnDGZerB+P0PaQEJWHvXU35u0vc9UzGrb/V5TNlAmGG1Sa9pKmeZTKJGgfk+acD1wbvxdxiGmmR8Sop6gcBmQnhmiXSYVEB0m/qZ9F9ijY6Qzs+5/6rPcWzetMty8Lw877PM/pu65A0G/8U7M7xxYKql2IvgpsXxBYYhe/u9GL7K5b86/l3Pa+lRoczSzRfIOqQ1LvB6GE+SJvsLQYbFCx7SVt8+izfvm/DmPSvLFj5nMfnaBXAzNzmh5PDNEukwppdvEHm9g7hT4uEDQn0sexxX7tkiUDPywKW1/HdUzhN6h6Xk2vSbmFgmoXorSKmwsCS3yDqqc41fo4P6vMHRTn6TI6G+kE3fk5QtObBD4SYRtlBXeDim0vaZtHl2rMOK5m0jyIaZsu1gmKkg4vtDuUGKJdJhUyu5L03O56n7/WS10Ly8Jwg4r9tOuaSi839W6lPhU8eA9CyzSCjo85XW/5qRBGUO0yqWovM0GnDEM7K+2SuhaWheEGFSuotnkUNUX9vNKLU8GbgrJS/CIS6+c894xZNAE99DiYGKJdJlXtZSZoBvPB5ruKBJiIwQaVpr2kaR7dbeHs8Ql7kDTevcmiFdnhdbrN23W7Sf0eywISQ7TLpEJmggJASUBQQNaAoICsAUEBWQOCArKGV9Czs0aEzzwnRlUAoDR8gi7zjoyNne+7QpTKAIA+fIJ6prDTTF+92f9tfI3ZlEr93wD5NASfoLU1HX+eNNSbHeMz5vWlxg9EMnIB+TQEn6DxTmEzZ4a7fK03e9Nowv+IkulirD+6MCCfhuA9SHoSF7Ug9on+3IKEXurTYPANwTVTKCAoXcgE5T6K1yX0vipy9Qx3yToSSAQIShciQQ0cxesS+km9mn1r1P6KpHYKBASlC5GgpY7iU16wrB6heRPm8mqzXdnKe0mrqCxAULoQCap/FJ/uZstipdK8G2n7rfvct7qq/iGupJIAQelCJKiBo/ihPppfX/hXfONNu1o1N5LUT3EQCWq0Tf96Yo6jeJ2gad51qg1+O8N2pvDaKRASQY236V9PyATlRicoetnyzaHXN1WOFlaKQiER1MCVORDUEISCoi9bdnbv2YD+pRU5QyKogStzIKgh+ARt4KFFb3ahoK+qNJrXtof6lF/1pn8KrqHCIBEUrsyVhkjQJL+Y+yx6swsFRb95en2S//cbLWf6WZVqqVooRAdJem36jOAgFq/65NWSLVmfRBk9zUO2i1/JOZpmkaAob61DxICaCKkdpvGVZSHQPM2kPnGEJag2vSLlxl8VKjuUW2okwKxtUJb/hlZgvwAavC4Db5rhPGiJfFoYdVsjtKG8kZvJzS4oQu+Xa/nnhTfihZWlOEgE5WvTWyAVdzOT8mcNB4ggqNq/XNmyHdGDicHTkwz8hQVBIihvm97ycJyE0I2yaYYDRBAUoRNRfVWr3Od+O6lehrASFQTRLp63TW9xrCvXc6S1sccKiSIow8U61U8gFMw5dq9FAW1Q0/i6YZ2PjC0XS1AU2d/z3dvj1wkrUUGAoHQRTdAjDR4vrF71V2ElKggQlC6iCYoinALt31ZtyBVWpmIAQekinqCH6lfw//Vix/rfCitUKYCgdBFN0Guqoxn7VA/RN/U7/i6sWGUAgtJFNEGXT2UmwzYjlLvRNfSOsIKVAAhKF3Pc1cmZ0KXsA3ZGxrEv0+fbTbLYU/YgKF3McVcnZ0L/dP4t77BKd5Hk8Xj7hen4lVQSIChd6N7VqYU7oQd83miUWPjuoOsb/hbZRxQEpQvVuzp1GEposaeV/KvanFD/jQ0W+EgEEJQuZryr0xhr3l817rNAH9890j0EykyAoHShe1fnw1ssvevyrnauw7D1fVQbDgX4W9ppURCULkSC5ifsVW/tF1nYRSndpyZLFUfe1c6qdBddeWsTUu9t0MKybqoDQelCJOiEgNa9uu4dOkJvNkZC5/S0dXfs8inzKv/rem2O8sYrBxCULkSCqtJe2VxHKS56szESurdV5r20uic1r/N3+LT9ifcvlAIIShciQV2TUq3OoSc19GZjJFQ9qF64V+Gji/O+rNuGs6uuAgFB6UJ2ol7l9lFAdLsIvdlYCT217VKxd3k76rf4ziKO6EFQupAdxV+4jE5M36p/NlNQQvN3NfLfYwHnRc0p6LkdV6gXLnfE627Hi/pAM98vFd9d1HyCqsO8h7hPol66zJGRoAxH2nt9liX0j+UBiaAJKD+u/2BDe6T9zXNQhu7A8vVBXoIidLKnc3Sy8D+XHhJBy6PoJtsSuuqPV6nL59woZjJhrfDiFYncBEXoylC7aQ9JCpAWMkED7iKUVTjSTXqVMhocNO9i+zOTwIOkFVQY8hMUoX8nVR/5F1kR0kEiaDn14H8QeuGkN1uXz/R6I2L6t8wRXrwikaOgCD1f5Bx8jLQQaSARtJbKsyO63Ej/PvGCfKauGPWp3Fvor37+iW5HX3kKilB2TD3/HUr8tiA6is+6cgSd2a1/Prggn48+6tLh/b2yPlt817tl+xrnaZbIK2iEDhMeaUznxLL62441lijvMWDmO830wmN89YaqgP9RL58ifVYilNCEZom8gsbExLxXL3ppg8X4ZZILuqZK2erxCF0cVn3cddKyRMZ8gn4+bMpc1O0r1T3qK6CHC3N4m18pk2KJOLv4xsz3WLIJHwtiQX8s9/GFUeWvMq/+m6vqdkjWOzV9zCfovHm996Px69ofp74CerzNVO6uA80ScQR1YT4RWR74ZRILGtyUmXho92XZm5t4r00hLFBE+AQNKQC/SF0+f/T7eGyS54/2cr4hNsF9w2bflTRLxBF0XMeEPUET8MskFjSIHY/Pu7Cb6S8Dqn9wjbBI0eATNLEA/CIL8jnVqfJbTV0+EVw1MTgxath+qgXiCJqzcdDgTSYcURML+mX5hNz15Ypd1HswxzlorzIu02Ps4rOPHzp0MAC/yMJ8/vvj1jiltclJwRE0b/mAnHWcepgwcINJjC1f5s15Jea8im9bI+oRabkigCHoyKZ2Ie7L8Yssymf+sa/vCKmUgsERdPJA37y+YzmWmzJwg2nk3M8rNe+P9237HZH9AROGoLVyFp5+0BO/yMJ8prd4e6CTvB99+nx7LN3r1DiC1kwLRDn63eZZTBq4gQKpGxrVXvbUTIVTAkNQp8xDK5AJzz4qzOcCpln+r/1zYRUThcuqgcMcqN4cgSOo+5NA9MybY7mJAzfQ4NSIagN/kvPXKIagkwKTfCb44xdZmM/e+5hJoJxPM3XeyhwncakiGBxBVzf0mOfzGcdy4QM3EJC8rnGtRfLt7oQhqPoqOr3oDn6RhfmcuIDZX7ncFVQvcXD+j5lYi32iHh2dHc09pqf+wA0PcAduIOPc2Oo998v0Qj2GoDEsu/CLLBT0jmra521Gps8LGnxGWOXMTtuDzG6eqzUoGBxBEw3dKpQTm5YyMyQ6u+B9wcAN+v3F6JO+pZ3qI1kOPoYhaERExHjP+fhFFu2RHswduyOva/N3OjtQ7ZBBj+OOM+a57qBZIo6gAV7zbnMuH9op892+24PH6M8W5S7EGx+7NvtMfp1JMC91PuuOX2TJfN6s3GHrnCoDTKqUeNxYMOcC1QKxdvE3FzVqv4VjuVMWckhDqQLui6dC3qFB1fp/K7Pz95iC5tTBL7JkPve9xeyxhnmZVCkFg9cf9NXhwIocy5v8gfzvoCf6Pop4H/fLz1upJl8UbXUYYAg6kKFeOH6RJfP505unUfbb+qf2LBYcQfeGOfb6iutxiqfd+/esN77OZr3Z4g40cHNuzQZL/hVzjUbBEPQQQ2I2b1ghJfOZbOMQoPKKMrliCgVH0M5xLw0EZO1dPmd9qfapWILeHddlKnvaXv3LWLt2G2Vy/ppPUPIO4NscAusF0jyTI2swBE3SH9qGD5EEfVJjweEp9bVjP77aP6Bqr6/l8KhaPkEpdAB/tP+kGqVu/VSxNxaaAoageY1N7OsmkqCfsmcPuu4reJuytWu10IOvRFm1ETB28TQ6gN9z6/+BS4wJ9VIqOLv43pXaBwcH45cpkqCz5+9a/N3Y4n0nnn7Wzm7kD9Ie1mMISqMD+HvMN/DtapJ/HM0PjqCnNOCXKZKg31UK/Lixtd5wWg9Wt3AYfURCRzEENdwBHL/7YovTzMRHMd24hUPWH5QbkQTdVsuxtb1rYqn5/65o7jD6B6muhGIIarADuAndF4euQehhVbnfJU8Bov6gBhBJ0I8XP/r1+bj1XIvurmhhN+IbE87k0INXUN8bwRo4FpXqvvjrEZYgV2byCKH0I0W/tzi+P9drbOn5Fve7PUl/UAOI9Q3aHaHcxomcy87sOPpJYLVBu7jO3poXXkF3JxtsMul3X8zoFsTiXJGZxCF0MqjY7zWr528K4phvab8bkPQHNYBIgua0CZr1dn+uvqHqgT5D3KahJ5u62fSIFbmDM96lTjVnJwJJui/KHKL+oAYQK6G5uxZyDxu+q17dt5rUOMu8So4fUK3tipvi1EcDhqB7h6lbVeIcRlG/+6IWENQQvP1BuZE8oSNtfnt10HqJ9k3292Nd6s/4tfQ9TuYBQ1D3XxN7PzOht4fk+ZQSzMHDuHdJBpA8ob0aMDV2mFb4Xn1udiOHYbtTxVg3hqCq/IhNav1n9xhB8nxKCVZnEYO7JG4kT+ic6u9t6utYsoPg3XVdqgStvmH2dWMI2rOfW/LkVvhFSp5PKcG6aU5pu6Q/VP97b6zqP/3Z6ftHu9aeeNi8Jw8xBE357BKKMqH/leT5lBIcQQ3vksw1cAMp++u99favnEsuLm5rE7zOyFHTqS/J7iTBEFSmFz7kCY6gBndJ5hu4wZy83DXSpfb4g5ynSPP71Q91M7X3VgkwBKV/4eN+7FeGekQqHRxBDe6SxB64gR5/LAuq0n7xuVJ3A8YH/vbl2ZqmnLLQB0NQ6hc+vnUc0c/ZQq/L4wj6clrztlFcfS0lGLiBHhnfT6pvN2DTnRIzp/ox36CNNhEUi3OaifaFD48zCG0yYSwdJYEjaL/BZ88NGcaxXPFXPh5sDlXVGruraMTNEU55KNXKhIsSpcAQlPaFj+RqzORfd/wClQTWALZMgz7XgytA/8rHfe3ADVTHPjEz6sure1RtPOUb7UnSifajNoY4f25qIbc71x2oG2QX51In7QsfDjcQSuhkQokKAkfQdg8Z9VoYCupW9FK8gRvokntyUacqLWYcTkfrBi0d81lzU0e/uvtm/XBnXe9hXkFvn8xkvvSumTAOLa+gX7gvmO74G36BSgLnKR+DnMaMcfiYY3koi3VoqN5s5ezii5F9PDKwcqsprsM2hrQ39bLou7XZRwdoT2bwCbq6rLvDd242NdrhF8+fzzMzF97CL09R8Aq6QUc8x/JlNtHx8fbx+osUKShLVmJkGyvHoL2mjlfStBcqHLKcT1DnC+h4me0mPXdcsfmkAc5Nc7ePXjXQP/1s03jkUWquohOa/cuiLlX9xu24b8LffMDs3h+8sVvzmk9Q5oBGXc20r2ip8vnwnCi9F4zDL+huL+eWnqqt3BGp4cNVpWYqWlCW3HOr+jh6hH52GfOLLtfVyrN8G+1rPkHtmB8T2+jS5FP9vmNTR679prjwCvq9B/vQzHPeBwzE7Cj9PBXFC6rheuyIulW7zj+C9S0SO/473Ss+QW2TkpIcmB8THibDn8+7m7ZSH0Zte9sMdNWpVIcGseEVtNXPmpdnWuKXaRmCsiQdiGhbueH7W0x4tAafoFYF4BfJm8+DTqMGqmgP4zCaveu+l6HvJdHgFdRe217KN2G3ZDmCsuSc+WSwh133+YfxvqPM96S5kqyt6xxecAba7RxCX5gwcAEWMyOZif9JvjBzwyuor3bE6UdCBv23HP7b/3EHm7pDPz3Ne6eoSIJubnzp4eQO2tfmuZJ0w2nt0TEtJR/cklfQ6HfYRlhWH0EjAlsUeVdixzaxfnvc5ivGDsJFEpRdjbqgy6sj0wTZFcT9x7+P7LPZpJNahVwZ2nqm9H2keAXNH+M4Kmqc20AxnzQnYzJPrgnzqdJm0ra/DFhKImgCyo/rP3irvk9c+ezAHrq66x58vKVG5BQH7qFfEitWqGY9RFBtoqq4ulF9oowgMM6DXl43ddVpU8q0ZEE1pCQuH1i7cpsPt1wuvQckEbQ8im6yLaHrTL3ZXPlc0+Fp3orCR9mci4y+w11k/aaZ6PNyxocHuBJzgOPrZ1+jJ+hn1TOeKpsdzJvmTMLiBdXw8ujywXUrNRv7+ZkSPRHJBA1gWvxZtfVmc+Uzf3qVSh0xLm9WYbsOvmH00Uor3EZ3aFj6edLvs11mgr/lX4V5AUGJSPv105H+FesPXlq4gyURtJx68D8IvdA/YcKdTzXWyD6ePRH6uZyxk65PHB4jNDay1Pxp7G3brU/grMScgKDk5FzaOnlSwRsSQWupPDuiy40+Knj/ap5mMGZfggP0LRW8WlZsbSwisT0z2de71PyLzgnXIn0lGd+qOCAoZYiO4rOuHEFndheOlpKzcgmLvwdBkTsa1/zQ6MWwe6p0hGZOLb0g8R2f4ab0SDAPIChlSE8zcdxwauZ8fhCwfEwN2T5bEgSlDKmgAaVnmTufB6atlMkTKDgAQSmjQEFlDZmgch24QUJIBd1Yehbk0xB8gipz4AbzIlZnkdcFIkGVO3CD+QBB6UIkqKIHbjATIChdiARV/MANZgAEpQvZQZIFDNxAGxCULkSCps8N3cP8Cit8X0+ZAzfQBASlC5GgYd1i/LcgpH97DSSULmbO57VOVu4ko1GZFyJBnVLQU8+HIGhxFCfoK+/Psv+st9+cqyCB7DTTM4TieqlB0GIoTtDzTZnJtqHmXAUJRIKutJuF1IODyuvNBkHpAoIagvco/uIBhPISJurNBUHpYuZdfN112Zd9JL//3RDQWYQyihMUXX+ngpfJI6KKBghKGeUJKm9AUMqAoHQBQSkDgtIFBKUMkaDQv7YUIChlSASF/rWlAUEpQyIo9K8tDQhKGRJBoX9taUBQypAICv1rSwOCUoboIEm/f60WyKchQFDTgdNMdAFBKUNT0PS2ASx2zvSKVBwgKGVIBG3goaVwxqXzLNP7k1dLsYCglCERNMkv5j6L3uxNo4lqpGxAUMoQ7eJXcg65DYIaAgQ1HTMcJIGghoCxmUzHDMMvgqCGgLGZTMcMo9uBoIYweWymezBwAwhKF6pjM+kGbqjmRaNmCsUMwy+CoIYQODYTJJQukE9DmDw2kxZIKF0gn4YQeJoJEkoXyKchhAoa/kLHzdt4XLeQuFsvXnQ0g6Am5fP6LYx64mwQTszf/1Aq6MYLAxjNp0BBv7Wz1VKtTFk8LCWurK2twwVhSaOWT6y60iqI3sqq2nJjNJ8CBS0kwxovLq0yXlyKDV7cy2p4cS9s8eKe2eHFPXXAixMKVj777uGPSeiHUVCFLP6YUTH8Mcc6YKzMRdCDmkBQLSCoIUDQEoCgGEEgKD4gKF1AUD1AUC0gqCFA0BKAoBhBICg++QvoxuUtxIvLXUQ3Lmcx3TihYOVpxz/8MTd3YBQUpeaP2fcHf8xDDInREkEPpicVFADMCggKyBoQFJA1ICgga0BQQNaAoICsAUEBWQOCArIGBAVkDaGgO729lvMGrfJUhadjhS4NxSnytL9TeC5G3DJX5wn5vHFJDVDBSo2GauLwN0UY/CXj1QAjkThZ5M8gVvZIUkcm6BPHu8nepYYd0eNi9aTkbpE4oedsQjGKzHK/lNUpjj/uoiopLSCBL26Vn0fBdhgN1cThb4owMDYKqwYYicTJIn8GsbJHlDoyQePCEIqcxRN0gAmICcUITW0aHYpR5N7+CGWm88f94Zb6qvUevrgfYzwKtsNoqCYOe1MEwl8yVg1wEomTRf4MYmWPKHVkgi6YiVDsCP64p/57MELD4+NDMYpc3ivAsX8KRnlTKtj0UvPG3fco2A7joWwcwt0UgWCVzF8DnERiZZE/g1jZI0kdmaBR7PrC+aLUmz03Y4TuGI6YvPLHzfW5ndZ3Fn/ciYCz17ps441js6cNMh6qyTLmpggFo2SMGmAlEieLGBnEyh5J6sgEZdc1/2OeoLzQng9xQkNUHvbW3fjj1o9HaHsIf9z0KGZH1o83js2eNsh4KBuHuylC4S8ZpwZYicTJIkYGsbJHkjoyQR+7PMn05Wvz7umkxg1lPvj8cbe9b6UGR/PHbW38NGVoJG8cmz1tkPFQNs6ETREEf8mYNeBPJE4WMTKIlT2S1BGeZor38+U9azCpvJWVVRhWKJNXjLg4T5fR2fxx+dNdnIdn8MZp9j/aIKOhbJwJmyIM3pIxa4CRSIwsYmQQK3skqYMT9YCsAUEBWQOCArIGBAVkDQgKyBoQFJA1ICgga0BQQNaAoICsAUEBWQOCArIGBAVkDQgKyBoQFJA1ICgga0BQQNaAoICsAUEBWQOCArIGBAVkDQgKyBqZCnqzU8+1UtcBkAMyFXTGCdRG6joAckCmgqbm3wuRug6AHJBCUNdrKH+pn32bw8xrK1u7as6rmXpUt7OzG1UUcyD4gfFCEjkE5poHKBuJBB0bdCfvuHsCI2gSQhfK3kZl0kqEHJ6Wz1MIhqCacVMRml++fPlyTXQDqGqI/lFo1QGxkUbQK46pzO+jNfM0giL3k/qCjuzeL7TY2/wk7U/ROwxBNeOm6pj5lXYA1Zst/JaipwMpbAQgDtIIuk6zM1c732EFVf9sn1Ek6E6X33NqHi4IXeNZa0reqT6NI9gf9QIvz48075BGRs3CznsRCjiufV1SUM24qVquD9UNoDrlJ9QyY+JlETZSNuA0qPSIiSj+TtqGkzSCTo3SvGh5FFk5OFUr860uZSuYeRsvTI7rUPAQ3mN+j9MGLj9lfQ2xP4caJWd12sK+QmzatAvjwtA/Xvna15pUepRhyUWF46Yyn4Tut9hfT/333GzZZMnf74u6tVKD0aAqYrcvOwVBV43TvPC6yqYsb5sbKkpZNppV62xB5GyPwEDfAafaI8T+TFuF0NfD2FeITZt24UvHV4vm6QL1Ulko6OFBSDeAKkuYoOeWKxaeBlVRy4kl+QY7BUFPu2Uxv8+rcjQpy7dJLp6yeQMKX0bPRygv51QwIyjzM5XZOe0KY18hNm3ahajX4Ua3dIGGBB32VcEAqgy/zFXPaNj+X3NuoKww2qDStJc0zaPUEJXHPibNOR+4Nn4v4hDTTI+IUU9ROQzITgzRLpMGiY7iB/b9T33We4t2p4NqXCsmaKbbl4WR532e5/RdVyDoN/6p2Z1jCwXVLkRfBbYvCOTYxedfy0HZ9i8KBlBl/k8hqfeDUMJ8EbdXWow2qNj2krZ59Fm//F+HMWne2DHzuY9O0KuBmTlNjyeGaJdJg0SC5kT6OLbYj3SCBn6Cyjg4OTk1Z5cuGfhhUej6Oq5jCr9B1fNqek3KLRRUuxClVdxcEMjxDZpV5g46yDYKdAOoop2fIzS9SeAjMTZUFhhtULHtJW3z6FKNGcfVTJoHMU3VxTpBUdLhhXaHEkO0y6RBdleSnttd7/PXeqlrYUEYbVCxn3ZdU+nlpt6t1KeCB+9BaJlG0PExp+stPxXCCKpdJk31ZSfolGFoZ6VdUtfCgjDaoGIF1TaPoqaon1d6cSp4U1BWil9EYv2c554xiyaghx4HE0O0y6SpvuwEzchhPuRSV8KSMNqg0rSXNM2juy2cPT5hD5LGuzdZtCI7vE63ebtuN6nfY1lAYoh2mTTITlAAKA4ICsgaEBSQNSAoIGtAUEDW8Ap6dtaI8Jlmee4fAPDDJ+gy78jY2Pm+K0SpDADowyeoZwo7zfTVm30t4nXmP+r/hmtSb5KkGMsnn6C1Nd1+njTUm72p2ZLXF+/DHIkiA/JpCH4cIvAAACAASURBVD5B453CZs4Md/laP6GjCf8jSqaLGQSFfBqA9yDpSVzUgtgn+nMLEvp7D59+1wTXTKGAoHQhE5T7KF6X0H9Vi9fPdXsmvG6KhFjQbqXmgKCGEHgUr0vomgYevVzq7SCpnQIhETSUxTo0VG82CGoIk4/iU16wrB6heTPUOdNBVcaO/i5P1pAIuswmOj7ePj6+cMYf51kiBhj5G0uHSFD9o/h0V1sWK5Xm3ahq31dzsurpdJe4kkqCaBd/tmk88ih6m946gMXOmbRSCoZIUANH8UN9NL82NbN+s+xbDr5fkNRPcZC1QVPDh6tKzdTl8/XEHEfxuoSm1K5nM9hqk81s4bVTIKQHSTtK38cLghpCYGeRgoQ+b/bG8ESbiguFlaJQzHCaCQQ1BKGgKDawha3vW6/XcTwIShciQRt4aNGbXZjQ9PrDN73r3PXlxI4fZwquocIAQelCJGiSX8x9Fr3ZRQlNXTFmXXoXK5tm1s45gquoLEBQupDt4ldyjqWpl9Cx5deh9IrLTKuXYgFB6WLWNqiWdi1Vh1Gjd4WVpThAULqIIOiE6r85HLOGb1DBgKCGoCNoWtWq3mUdc9Dv8X8KK09RgKB0EUFQlPph+641H4TXHuQ+RViBSgIEpYsYgrKs9vDPQenep4SVqCBAULqIJShq7fKSaY5a/hPiQFC6iCZorI//c9T+oLASFQQIShfRBE338a3Zp7nln64HQekimqAoeYTKQf+akwUCgtJFNEHzurSc6FjV8gfXBkHpIpqgOzuoUY6zy01hRSoHEJQuogk6h32axIdhrr8LK1MxgKB0Mcdtx5wJ3R6MUH7zw/scvzOlesoDBKWLOW475kzoq+Y9FnTolo9Ou3yqeZ98xdAT95QNCEoXurcda+FOaM7W2TvZByDc8pmYi1B09QZ2m/CrqRxAULpQve1YB09CU3u+8+KE9xN0x/Uv/vopDhCULma87dggeVNqjxvVtVbvARswKqg0JBF038j3LbWXA9XbjvPiNrK08OT7ux2V3trxz4aKsXh1VBRSCBrtF/uJy7fU1ysLiATNT9ir3tovMqPgfdaHY1jquPGudly5VsfnV7bEIR0kEFRt+xChw22or1cWEAk6IaB1r657h47Qm43RZlo42rPSwCGWeJQkgaApVZnJXQ/q65UFRIKq0l7ZXEcpLnqzMQQ9535zo23VW7xxykOKXbzPIfY50NTXKwa5HcuX9TR2sEwkqGtSqtU59KSG3myco84v7FWOHu9a4OChJIKmzw3dw/wK05vNm8/fnDu3qqvMjjh9bH667W9nJIDsRL3K7aOA6HYRerOxTovkP0ZZU10t77ISiaBh3WL82ccO683mz2fy4USFdmS02cB8i5a9bTiA7Cj+wmV0YvpW/ecP45+3O1Fz+EvcWIVAIqhTCnrq+bBI0Pyt2rMiNWlUTJ5UYY5D8svdMBwgXn9QTtInuO4XthK5QiKoJ9PkieulLhQ0a7z2rIh+G8qCCLa9+LK1rZEAiQVF6Gefvg+FrUaekAi60m4WUg8OKq83mzufty9mC1+TbHjVomwZlwtGAiQXFGVHOqzJFbYiOUJ0FH/xAEJ5CRP15nLlM6NbjUbuvxGsSj4Y/+dLLyhCfwc1+kXYmmSISKeZZg7LQ4fc9Vv/FogcBEVop/sQZZ4lKY1Igrb+lZnUxrg/QX3+WAr1GomIPARFGXPs5lhG/1CRBO29kzmGqs5/CiS5pW971RHqVRIPmQiK0L1Ql42W0BQVSdAfa2w7EhzO/7eTBoweMMNFwU0BXkELHjprwjO3BfZf/L1TvT1qQX8pJ8S61Hn03aAVGIfxvnZrv25bVcG3KvIKGhMT81696KUNFuOXKbiD7eGmTQ9pXhypY9NAoXfXyazDsst4hP4r+5heZcQGZxff+AXTlmmCX6bwhKoTGrT8AaFL5YPXtnqT/oPXxUBmgta3W7vLsr9BGVwymQa5B36ZJAnN/9q3+Tfv1mdeOX0kvBQJkZmgTBu0v2W3QRnGdUzYEzQBv0yye2jydzeu7JvHfPaHkJQiGXyChhSAXyRJPpNb+HZU/ST87yUHR9CcjYMGbzKhtwzpTV7q98q5rztWXpkj4fEJmlgAfpFE+VSfP5pK8OeSgyNo3vIBOetMOAVEfhdiYLnyZeorcxwnjF189vFDhw4G4BcJd3UaQifo5IG+eX3HcgWYMLKIafy97cQH1UPPEJcjPhiCjmxqF+K+HL9IENQQOkFrpgWiHK4uX6aMLGI6L1fWbLYli0pRIoIhaK2chacf9MQvUi+fd3rb+8SZWi2xSP9mzwuqBeII6v4kED3z5lhu0sgiAsj/trvD5GuUChMJDEGdMg+tQPXxiyyZz1y/Jc/Oesu0gf63e5cQp19plogj6OqGHvN8PuNYLmxkEZO4/bEqcJuSHvSJIeikwCSfCf74RZbM52U/ZrI91NR6iUOPDQgd0v/CIgLrWvzR2dGc13UEjixiGjl7e1Qfc5JmiWYFQ1D1VXR60R38Ikvm8/fGkR0GLpTpPZzOzJGt2prmFwqOoIkGz/PqjSyS0T2IxdmZVu10PIiuW3fhXcqFmgkMQWNYduEXWVLQbOuWR5e8ucjkiokCex3wbxXNEnEEDfCax33bXU5sWsrMkOiiPgs/H2EJqk2xgjpOj7dvu0EJdyljCBoRETHecz5+kSUFvenauqJd71ElQ+TSyeZ75yVram6kWSLWLv7mokbtt3AsH9op892+24PH6M82y2mRVwcHVe2+NdkcRdME81Lns+74RZbM56mW6BXa37v4rKcDrKtOfIVfoBn5/aMJiVQLxOsP+upwYEWO5U5ZyCENpQoZuEEQ6fG9q/bcKu8blTEFzamDX2TJfKarTqPs4DXFZ/WcnPG0x2z8ApUEjqB7wxx7fcXV373JH8j/Dnqi76M5TyynfNnbputGGXcfwxB0IEM9jM7GBejl85Czvyq0+IW9rCrMu79MOG+lJHAE7Rxn4EvrtHv/nvXG19msN9vMVz7Sdg22bb2MPT2adUt+42lgCHqIIdGEe4b185n5e8kbuLKrMLv3Kw3wC1QSGIIm6Q9tU0TW3uVz1pc6gDL/pblXP/zPrc7k/1Wr6bDD3KsyFT5BCe5QONy4UkvOO437jX12950o/AKVBIageY1NvJgjzrXjC6Mq2PSNcpRbX1w+QYXfoXBN9dPtIdabOA7YXwyzdZ5pCTd0cYCzi+9dqX1wcDB+mSJ1boic/3T7kAquUw7L6kITxi5e4B0Ky6f+6zyxWS3OXjuWC46gpzTglymSoKtCBzUd2Tl6YbvKHaPP5omySgwwBBV4h8LSiMnz0MgNzv8KrJkykWd/UCzOlBsS3/kN5ngh7ZuJfrYhay7L4mw1hqCG71Aw2n3xT+d2+35Q3Q88TlpFRUHWH5QbkQRdPaif37B3ErRvHseP8bbvu+ai5N+kGIIavEOBp/vifvtyjY49sFfC9TR6EPUHNYBIgs5lrxaOjima8fCr9+tX6774Z0n7kPIK6nsjWAPHIv3ui9mzNQf8vlWZyRGErk9W1WhQfR26HqF7/zr8bkrSH9QAIgl6wvsxuuGiN/Zp0r7JzSq1/HDU6M0SHdXyCro72WCbXr/7Yu6aJSz+dszkNEL/Llk8ckAC+7vg/WvwuwVJf1ADiHWLwlLbevZbOeZnHHZ0qVjRafVJKb5K8S51qjm7nYvSfVFhEPUHNYBoCU27yn09ZlND27q2jj0DrJuO2/ynyK1SDEH3DlO3qrSWa4le90UdIKghigYP4/7EG0DyhA6xf4yuW89HmSfXhHpXaTf5y7/EsxRDUPdfE3s/88IvUvJ8SglWZxGDn3huJE9oX698lFl1pvZN8tFlA2pXbvXBF7+L0iENQ1BVfsQmtf7DpYwgeT6lBOumOaV94lfUbDyqjteeYnOSE1cN9avYMHzFkVI7UMpgCNqzn1vy5Fb4RUqeTynBEVRxn/h7qklz/ler1IAa2ee/+LB9ddU7H20+b76roxiCpnx2CUWZcD1I8nxKCY6ghj/xZhu4gZDLA/xHGrqH6f73S8MaV6zdZ1b8Ja5DrJ+3XCRaNYagMr0yJ09wBDX4iTfvwA3mJPfa7nn9fSvW6T1j69ni37T5vRoO85pCUjKGoDK9MidPcAR9Oa1526gMjuXmHrjB3ORc27MoNKByjU7/W3PoluZAf0fHC/su1y61TzABDEHldmXur9hvaF3VyFvawHsG1fYTjqD9Bp89N2QYx3IRBm4Qg39//HRckHsF3z7TYwb51ert4k/ymHCc00zyujK3qsbIwMaURsBb3Pbitb76N1ESgTWALfP5yvXgWG5RVz4yL+1ePEJVrlKjnuXDjt4VPOQrhqDyujL31P4xQmPm0Sms/p/s4+tpnnXGEbTdQ+bQogVXgP6Vj9QXLAPrUqyguEx0GDLd36Z1YA0r727jV+69hPmIobQx7WfplMa51Cnwytz57t59r5rwd3gktmcm+3rzxmHhdQehV1VoPqMR5ykfg5zGjHH42FBQt6KX6S62LG850KufyKwNW/nBF61+QCjr6rdrJ/Xyq2TX9N1p67/7i6sFXsRLa6fAym7a17yC3j7JtNGSr32CXymdoHdV2/5eX4N6Z7t7zukIzZpKp7Dxw7LyZvagU5YWXkE36IjnWB7KYh2qP5CVMnfxGl56Tdg2qFWxQ4YnZ3Yueb9rvYoOTftOXrPv96ecfxXmyhhdYYPmNZ+gq8u6O3znZlOjHX6ldPlcw/ZxHkT/LsFxTVe+70rpgb5pgyrZdKX69Aucm+ZuH73KfXvvMpvo+Hj7eH13FSwoehY5dBXXUejjM7tXfNirsV1Fn6DhkV8c/qvEQUVAL2biPUzzmk9Q5wvoeJntJjVxdflcOAvtfrtK8wem/CkW+6csp/e9nEX5gYH8gu72cm7pqeLq1obQ2abxyKPUXCULykfG1R9iI0d2rl/ZxrfLiLkbv/2D/U4dpmK+O6xiNQF8glZDSF3NtKMIXT7Pemzz/MJxnL/kNw2ICq+g33scY6bnvA9wRqSGDy89lpklC1pI8p+Hvpg3OtjPzsqrTb+3KtatqBsxjU9QO+bHybRVFeRzk1U11S7U4A+T66pkeAVt9bPm5ZmWBmJ2lH6eymshaCFZt36JX/62d8GY83yC2iYlJTkwP0n4ayjM58ANTNOgxWkhtVQsvILaa/co+SZ86l8vQfXgE9SqAPwiC/O5vdkTtMtDccP2E8ErqK+208UjwWOqv2aY9Ulzc22qNjpPvXxZwyto9DvsAWtWH8EDrr5mmPdRiHmijo8qhyFHeQXNH+M4Kmqc20ARnzSnaGT2rE4CNjhZ+Uv/pCqM86CX101dZVLDHASlizT5/LHO3+oEV8lHtMa8ac4kQFC6SJPPD9Yhs2yNiYCglLEYQSetYiYdj0qx6uKAoJSxGEF/c/v52fqaxnvJiAAIShkSQdPnhrK3oobpzZYon3sa2XWX/jmUIChlSAQN6xbjvwUh/XP4kE9DgKCmQyKoUwp66vkQBC0OCEoZEkE9nyEU10sNghYDBKUMiaAr7WYh9eCg8gXvM3pon31qwqAZFgcIShmio/iLBxDKS5hY8FZ93GzPPlUMZILKdWQRCSE9zcTxWB3IpyH4BFXuyCLmg1TQgNKzIJ+G4BNU6SOLmAMQlC5EglrIyCJUIRWU42HrkE9D8AlqUSOLUMJiLnXKBLKDJL2RRfI2b2RpYcJgtxYHCEoXIkH1rx1nfTCGpY4JI7dZHJYjaPrGOd9IsuISEAkK145LYzGCJnsPigp4T4o1l4BIULh2XBqLETR6DLNL9PpTilUXh+w0E1w7LoXFCDoijpn03cMbZ2aIBNW/dqwDBKULVz7V8x0r9LxHfVXFWMkcWqTW+Nucq8CB7Che79qxDhCULlz5XN/q38z5zamvqhgZAUGTak835xqwgM4ilBFJ0KCfmEkN+iPdFSMnYbUMRtkBQSkjkqDvHGEmtEb1lDMgKGVEEnRj85tps1tTX5X8AEEpI9ZBUnSNKv1egy9QEJQ2FnOaSSaAoJRRoqC0HuNlDkBQyihP0CutKjisNOsaSABBKaM4QbNrbs675bfbnKsgAQSljOIEPduMmXyp/ygh2QCCUkZxgv7uz0y2hJtzFSSAoJRRnKA59Ve8PFfne3OuggQQlDKKExTd6lnNZ4tZ10ACCEoZ5Qkqb0BQyoCgdAFBKUMsaLdScyCfhgBBTYdEUMt7ejQ5IChlSAS1wKdHEwODh1GGaBf/+j09mhcYPIwyZG3Qkk+PTvepyVKl9AOlXx+oDh6W0R0GXCU9SCrx9OgHt1gWDicrUtFQHTxMfUIz4Or43jRqplDMMD7optFkRSoacwweBgklgGP4RcinIUwdPEwHJJQAELQk5jjNBAklgGN8UMinIYQK2ihCy/RaLfDwwgtrLve4ehERXvQvdZqWz7pN+GOa1MUoCGej6zXijwmog1FQrSkR3BjNp0BB/12iY0H5bli8gxv3Bl5c0Jt04zrhxlktWbL0hbCkUcunUxP+mCZOGAWV68wfU6MBf0wzO4yVVfh4CTdG8ylQ0EIyrPHi0irjxaXY4MW9rIYX98IWL+6ZHV7cUwe8OKFg5RNnvK+EfhgFVcjijxkVwx9zrAPGylwE3UINgmoBQQ0BgpYABMUIAkHxAUHpAoLqAYJqAUENoXBBszFFycQUJR1TlDRMUVId8eKSMXtrvHDGixMKVj4HHOSPOTAQoyCbV/wx72PczPTLOxgrcy91uQcHUkHRc4ijCk75yXn8MXnJlFaWiiGxGufEm7DMEQsKAOYEBAVkDQgKyBoQFJA1ICgga0BQQNaAoICsAUEBWUMo6E5vr+W8Qas8VeHpWKFLQ3GKPO3vFJ6LEbfM1XlCPm9cUgNUsFKjoZo4/E0RBn/JeDXASCROFvkziJU9ktSRCfrE8W6yd6lRHfS4WD0puVskTug5m1CMIrPcL2V1iuOPu6hKSgtI4Itb5edRsB1GQzVx+JsiDIyNwqoBRiJxssifQazsEaWOTNC4MIQiZ/EEHWACYkIxQlObRodiFLm3P0KZ6fxxf7ilvmq9hy/uxxiPgu0wGqqJw94UgfCXjFUDnETiZJE/g1jZI0odmaALZiIUO4I/7qn/HozQ8Pj4UIwil/cKcOyfglHelAo2vdS8cfc9CrbDeCgbh3A3RSBYJfPXACeRWFnkzyBW9khSRyZoFLs+3rHP1Zs9N2OE7hiOmLzyx831uZ3WdxZ/3ImAs9e6bOONY7OnDTIeqsky5qYIBaNkjBpgJRInixgZxMoeSerIBGXXNf9jnqC80J4PcUJDVB721t3449aPR2h7CH/c9ChmR9aPN47NnjbIeCgbh7spQuEvGacGWInEySJGBrGyR5I6MkEfuzzJ9OVr8+7ppMYNZT74/HG3vW+lBkfzx21t/DRlaCRvHJs9bZDxUDbOhE0RBH/JmDXgTyROFjEyiJU9ktQRnmaK9/PlPWswqbyVlVUYViiTV4y4OE+X0dn8cfnTXZyHZ/DGafY/2iCjoWycCZsiDN6SMWuAkUiMLGJkECt7JKmDE/WArAFBAVkDggKyBgQFZA0ICsgaEBSQNSAoIGtAUEDWgKCArAFBAVkDggKyBgQFZA0ICsgaEBSQNSAoIGtAUEDWgKCArAFBAVkDggKyBgQFZA0ICsgaEBSQNbIV9GannmulrgMgPbIVdMYJ1EbqOgDSI7qguWWqV3ccmsKsubqdnd0KlL/Uz74N+0T78tXtqrS5URiXmn8vxHhJiRzLueYBSkYCQbNQytiuzJrTNO/HBt3JO+6ewAiahl6G9igKPBD8wHhJGIJqRk5FurFV1fNruEWptTOifxRaf0BcJBEUZTv+pRP0imMqMz1aM48VFB3yKow7PC2/+J/lJ2l/it5hCKoZORUVjLC6t8F/D9v8dLOF31L0FOdBloAckEZQFBKvE3TdKHaqdr7DCpo6tBvzZqfL7zk1D4/s3i+UXbTGs9aUvFN9GkewP+oFXp4fad4hjYyahZ33IhRwXPu6pKCakVNRwQir02cjtHbilJ9Qy4yJl0XcYinBbVDpExNR/J2UDSeJBB21WpMyLzQ1SjO35VFU3sHJuul15vXGC5PjOuh2xeiY3+O0gctPWV9D7M+hRslZnbawrxCbNu1CRr9/vPK1rzWp9CjDkosKR07VDpu6penzpHcG3GzZZMnf74u91VKB3aAqZLcvO33tBQ35SpeyVeM0c72uanbxGrLRrFpnC8JnewQG+g441R4h9mfaKoS+Hsa+QmzatAtfOr5aNE8XqJdKnaDaYVPzxjn5hGm+sMMEPblcieA0qIpaTizJmq/V113QV05/6lJ22o319bwqp0hQhOYNKHwZPR+hvJxTwYygzM/U1QjtCmNfITZt2oWo1+FGt3SB3IJqh03NzmB8j2be/jJXPaNh+3/Nu53ygL9BpWkvaZpHqSEqj31MmnM+cG38XsQhppkeEaOeonIYkJ0Yol0mBZIImj6+c+FOZ2Df/9RnvbegYoJmun1ZGH7e53lO33UFgn7jn5rdObZQUO1C9FVg+4JAjl18/rUc7bCpx+ukPva6y/x/QlLvB6GE+WJutVTwN6jY9pK2efRZv/xfhzFp3tgx87mPTtCrgZk5TY8nhmiXSYEEgjo6Og1JLhQ0J9LHscV+VFzQJQM/LIpfX8d1TOE3qHpeTa9JuYWCaheitIqbCwI5vkGzytzRDpuq/ljVgGl9oZ2fIzS9SeAj826nPOBvULHtJW3z6FKNGcfVTJoHMUlarBMUJR1eaHcoMUS7TApkeCXpud31Pn+tl7oWlgF/g4r9tOuaSi839W6lPhU8eA9CyzSCjo85XW/5qRBGUO0yKbZAhoJOGYZ2VtoldS0sA/4GFSuotnkUNUX9vNKLU8GbgrJS/CIS6+c894xZNAE99DiYGKJdJsUWyFDQjBym4Sh1JSwE/gaVpr2kaR7dbeHs8Ql7kDTevcmiFdnhdbrN23W7Sf0eywISQ7TLpECGggJAESAoIGtAUEDWgKCArAFBAVkDggKyhlfQs7NGhM80y4MpAYAfPkGXeUfGxs73XSFKZQBAHz5BPVPYaaav3uxdZV5jyp6i/m+AfBqCT9Daml5pTxrqzd40muj/oWy6HKZeJOTTEHyCxjuFzZwZ7vK13uyChObuXHBAml4uEgKC0oVIUPQkLmpB7BP9ubqE5rQNmt0i5HUzFASlC5mg3EfxuoTuaOJdvoH363YLLwhKFyJBDRzF6xI6tsrJG13KNeO5gd3SAEHpQiSo/lF8RtcgFg9vzbvBXv9VmlWlWa1U4koqCRCULkSCljqK/+0IS1BtzZulbt6Vy75Zucnr1b0YBKWLOY7ih/pofl1TNano+1Zipbkk9VMcIChdzHEUrxMUHa5etuk7HUFQUkBQQwjsLFIgKPq0WfXGVjbbhZWiUIgF7VZqDghqCFJBHzov+2qOdc/kiO7zXwkrSnmQCBrKYh0aqjcbBDUEn6ANPLTozS4UFP012H/E3z3erNyoYo1coTVUGCSCLrOJjo+3j48vnPHgFsvC4RTqpVSIBE3yi7nPoje7SFANH77ZIzu14kohtVMgRLv4s03jkUfR23SfmixVVKSVUjBku/iVnNeJ9ARt16F/l4xG75pWL8VC1gZNDR9e2ka9fL5emLUNqmWc/athba2jhZWlOEgPknaUHiwOBDUEHUFTq1Rv++abD3O2R+5+DYZcMMNpJhDUEHQERS/HtJ68oG5At3ltgy3fUBCULmIIqmFIxaso/+0fhJWoIEBQuogm6Jx+Tr+gD6UZwkdMQFC6iCZofKfvHROaHBVWooIAQekimqB5QW3C3/K1/P71IChdRBMU/dHGpU2dCXnCilQOIChdRBP0gfOGi0vdOnZLEVamYgBB6SKaoGv/x0ze/fJ/DW4JK1QpgKB0McdNc5wJXTCHmby/Aa1XHTOhesoDBKWLOW6a40zob7UeoBsu1xFKVFn0uSYQlC5Ub5rTwZ3Q1bZ17bewL+43G5yB7vyv0+T/sGupIEBQutC9aU6LgYRm/q3rtZwZ3vB0jYU/TfdOx6ujogBB6WLGm+aMsKFyx61zdwYn8FZPeYCgdDHnTXNGGPWG55xAD0t8IBcIShciQfMT9qq39ovMKHifXkU7ZJ4D72pnVOoZ8MtbGzErqSRAULoQCTohoHWvrnuHjtCbjZHQ2e3ta5ar+ylvnPIAQelCJKgq7ZXNdZTiojcbI6HxnR6fOl6h40veQMUBgtKFSFDXpFSrc+hJDb3ZGAnN69xqcuNBH7r/xBupNEBQupCdqFe5fRQQ3S5CbzZOQvO/WcXI+aPbhAz+WEUBgtKF7Cj+wmV0YvpW/fs4TEjoy6F1fsEOVgQgKF3E625ngP2uE9L4o5QDCEoXyQVFL4Z7HhK2HllCImj63NA9zK8wvdkgqCFEEZRpidYc8ljYmmQIiaBh3WL8tyBkpTcbBDWESIKijAjHDZZyQzKJoE4p6KnnwyJBM1oEsNg50aiYQpGFoAhdadvsrLCVyQ0SQT2fIRTXS130DfrXeZbu3hTqpVRkIihSb3UZVeqivhIhEXSl3SykHhxUXm822S7+1XOSv5YcXkEjdJjwQE5hCU35yGGFBYwiSnQUf/EAQnkJE/XmkgiaO9ba9u1rBFWSGl5BY2Ji3qsXvbTBYvwyhSb07x61CzvgKbarqMxOMy1uOaDnIP0e5UoCZxff+AVCyU3wyxSe0CON2vzG/p75ZtmKCr0xRGaC+jnEHgyq/C+9yogNjqAumQhleeCXSZDQ/M3ufa6iveU/expZ7nfBpUiJzAR1/Bih1LJ3qNVFdHAEHdcxYU/QBPwyiRr1WSuchrVrybyoNYqgFOngEzSkAPwiSfJZ2+uXv9+z/kd4AVKDI2jOxkGDN+Xgl0l4Yjl13luOzD6p1hiiUqSCT9DEAvCL5M1nGjMGUAAAEY9JREFU1twmzT/nHlVoamBT7z6uCh5xCEfQvOUDctaZ8BAE4isfX5atFDqu3AXCUqQBYxefffzQoYMB+EXy5nN0yIUT/us4F6V1qNWixs/465IdOIJOHuib13csV4AJAzeYxNy3ypRtocyTIxiCjmxqF+K+HL/Iwnz+HHeGa3lulXTmX+Fv4K//OiVmj8bMH75LplogjqA10wJRjn6vZBZTBm4wkczkRU59T5OXIzoYgtbKWXj6QU/8InX5zO/XcEQdru+JdJt8hG7WwS/QfPzj1bGr8ymaJeII6v4kED3jutxm0sANJpPxqVe7A4q7RI8hqFPmoRWoPn6RunzuaZ2HsupzdZ9tt1z9aqQJR7Hmo9enCH2jP04CETiCrm7oMc/nM47lpg3cYDq5X7/tvV5hvUUxBJ0UmOQzwdAemQNdPudGMZMJazkCbjd3qNpPFnlyeYSQulImxRKxrsUfnR3NeVJS8MAN+Pzyrt0URZ0kwRBUfRWdXnQHv0hdPjf3Yf60zbe6mb8M6fFJ0ZmVJ3QbfoJp/hOzm6fa+QpH0ESD+1m9gRvUJ4o/L54SdyMcux1Uzri3GILGsOzCL1InaGbDIWt7ttNl4phL7MHOI4VU0KwcdF31WR3u8wkCwRE0wGvebc7lObFpKTNDorML3mcEB7E469+HTEjWlpZu8/WftyhXMASNiIgY7zkfv8iCPVLGpxNiCrrT9IlnjLWVyfdmMc58MJbz6YOCwdrF31zUqP0WjuVDO2W+23d7sP4ZdTP0AL80vnpwgiL6OmFe6nzWHb/IonzemT4sVvsV2prts1DnhklVUyR4/UFfHQ6syLHcKQs5pKFUAffFm07G1vaOH543Q8GUwRQ0x4SzQoX5vOk0Z3NH7cO6I97LRz+50TnHceHT3fL97OMIujfMsddXXAeJTf5A/nfQE30fzXUPze15tXyj5d4xB0PQgQz1wvGLLMznh4sRyna9teuDqEfpQTVb1DghrIp6RHt80KW+bMd4wRG0c5yB6p9279+z3vg6m/Vmm+8mL/Vv4+zbrJP1HXYYgh5iSMzmDSukMJ999jGTwF4tP52suoOunaZzNuexQxJC4+dQKcsMYAiapD9ySBFZe5fPWV/qAMqsdyHmfBtm23GDZnDm7xpatzxpxlUJgk9QkjsUFg1Vo1vVHTMQWvCh4Arqk9iemezrTa08E8lYMdtoSxpD0LzGJl4VN/dtsln7hti2XX3nT+fEjD3OchtHnE9QkjsUMts17uM4s/lon7azewiuoD73nVIRmjGdWnmmccXKxqncIiMBOLv43pXaBwcH469UhPu4s78b5ejc+pwahW43+7pMA2MXL/wOBfWv+x48e2Pste8qm9CE5WNyo4XD3aVqNnm3Yz6z5Y0c7OEIekoD/krFGWggb4x/tbd8W8ltEFwMQcnuULiqUg1t6Rlqar2McHj2p5KdUK24m5mUN9IrSJb9QfE4Wn7ctsZlK3VYckFOPUowBDV8hwJO98Wzzf7Z9uOufoIrKC+cmDz8XdZIPwKy/qDciCTo2i5132jSIv77D30cBsVwX+qSAAxBDd6hwNt9kT1Jn+V+AD1vE0tWS9mwoVyX8IqBRgKI+oMaQCRB5yxAajSG3cXfiwtVeY3YJotTpLyC+t4I1sCxSL/74qu5mgN+32rM5BhC623LOQw8hk57VX6rOfv+RoR2vpl+PxhTP2iq2dcz37/uWGPL3ybpD2oAkQQ9Uv8Fulfjqu7d1c8GqDyHbvxLc/vNk5kDF6WKUolS8Aq6O9lgm16/+2LOqiUs/tWZyUl0xWl89KjK+xC6M2sR+x7dWbLEjL8fOI8f6Osdbfb18PxuTtIf1ABijcY2x76ZfYlqXdsUXrN6j0WJ9zxGTH/XX5rLd3iXOtUvuOYa7764hD0fPVK0fXvkdITy65dqEIsNUX9QA4g2XODTM6WPPh/t+ai1lVXlhvae33L8hfnBEHTvMHWrSlwdj40/d2r5VGYyjKvTjll4j/0o9N4n1uoMgTl4GPcn3gCSj2fZx+mT4T5lXcfHXjThZmlKYAjq/mti72de+EXq8nld9WP6HudHQitmKhu756EHqntirc4QWJ1FDH7iuZFc0HfdstDzyuGrwupX9B+x5piow7thCKrKj9ikNqHPbEE+f/Cv1Jr70m7G8TMmnAbEI7e3dy9Hk/se31u97Cp/lAlg3TQn7BMvHeu83XqovNhdfOaZTePaVHXtMjXurEj37GAI2rOfW/LkVvhF8ubzbI12jf3of7VeOPDA1D855fjBR6o9NCuBI6jQT7xkvPAcNu3dpkUHSXe/XTLU39qjy6TPjz0097oxBE357BKKMuGcGG8+GxxAaP4Q/ALNR/sEhM570CwRR1DDn3hzDdxAytPZg5fof2Pm3/5+xai2DlUCBs3ZdvKpob+8NfadqQYX4oAhqPArc7vf9ggr/a2WUpWZ3PHEL9B8aPruWIt9V6fBT7wZB24wGy/OfDl3cDM7m8b9pq7//mqW3tJHLtE/TvYlSTCGoIKvzB3xPHZ3tn+pGwjzqz1mlrXGL9B8dPwaodM1aZaII+jLac3bRnGNn2LegRvMysvzu5aO7VLXyqlZ/ymf7D1X0GlvZejIwImB3xAUjCGo4CtzI9gzPw3+KBUw3/+rjW6SnxBiOec46gOngzRLxBG03+Cz54YM41hu7oEbRODRqZ3LPwwJcLLybDNo8qqv+lRafHxatQ0EBeKcZhJ6ZW4Qewq/RemuP+r4gSPo3P5BzKMNa+kOY4A1gC3TXsr14FguwsANYpF965evVk4apCpfsXKtcv4TFsQcOPmPKcf9hTfFYAgq+Mrc9mZP0C4P/WaJZYMjaDvm0Pd+C64AvSsfr+ZoOze406yhqMwIdGhm2+B/a2eP6tnC07qim3/X0IlR63cevXjP6KD5Y8qXsVqlfYlzqVPwlbm5NlUbKeDOVprgPOVjkNOYMQ4fGwrqVvQyV9e5wYNa9cTmkN+1k3+4/K17l/Hv2e+3rZ79fr8OjWpYW6nqt+4xdMKclbF7fjr/T1LxK/2xb+xHS8pp24a8gt4+yRyDJV8zYQj+YnukPPkN1WBmeAXdoCOeY3koi3Wofu9uJe7iC5hp19Q+hnNJ1sM/fzmw9ZP5k0f06RhQ0+7NCo61Azr0GjImYsGq2n7fXkLu2i7IfIKuLuvu8J2bTY12+HWSKJ8PZw5dJ/398jg3zd0+epX7mvYym+j4ePt4fXeVLChKOp+CF5j5+Mb5o/u//HzJ7Eku9t1HIZepmtl8gjpfQMfLbDfpHgBp8vnQJWJbj+6SDx7OL+huL+eWnqqtnAFnm8Yjj1JzFS2oEPaVW/T3qPLao1c+QasxB93VTBsLTZp8RjIfuLz6krd4eQX93uMYMz3nfYAzIjV8uKrUzNdOULS4Ujm73dqXfILaMT8mjk8oTT413e36SH52lVfQVtoR+M+0NBCzo/TzVF4/QYvBJ6htUlKSA/OThF+kNPn8PDgPPVJJfhsNr6D22t1RvgmfehDUCFYF4BcpTT5ze/r0VpnQydJM8Arqe1fz8pHpY6q/nsjsSXMknN8n+fcnhqDR77B3n2X1ETDg6muJBQkqC3gFzR/jOCpqnNtA8Z40p2xIBE2fG8p29g3Tmw35NIT2POjldVNXmfTEIkioQMK6xfhvYZqperMhn4YoY2SZESChAnFKQU89H4KgxQFBKUMiqOczhOJ6qUHQYoCglCERdKXdLKQeHFS+cMYf51m6m/cph7d6VvfZbNY1kACCUoboKP7iAYTyEiYWvM1oE8Bi50yhXgbJqb/y+dk635lzFSSAoJQhPc10s/Qs8+bzAjuU7pZh5lwFCSAoZUgF5XiOvHnzebYZM/mS5oC4VAFBKaM4QbNrxeX945dgzlWQAIJShlRQjiHNzZzPK60rOK4y6xpIIBNUrgM3SIgSL3WKP8QaPkSCKnHgBnOjREHlDJGgCh64wWyAoHQhEtQCBm6gDghKFyJBLWjgBmqAoHQhO0jSG7ghvbUIVz5kDghKFyJBS/VfFOXasbwBQelCJCj0XywNCEoXIkGh/2JpQFC6kJ1mgv6LpQBB6UIkaKn+i1ogoXSBfBqC9yher/+iDkgoXSCfhoDOIqYDgtIFBKUMCEoXEJQyIChdQFDKgKB0AUEpA4LSBQSljFiCrqrlMOQx9VXJDxCUMsSCdis1hyufsQFXnkwLJFyVEgBBKUMiqAkPpej8A0Jq8R4eLx0gKGVIBDXhoRQd2YHZ3e4JX5dSAEEpQ7SLx38oxdp2/+UsaUqwKqUAglKGrA1a8qEU6S62LFYcw6/nz6xq3eUOyaoUAghKGdKDpBIPpUh5wbJ6BGek9A/ZEgMQlDJmGJtp02iyIhUNCEoZMwx9A4IaAgQ1HRCULjD0DWXMMDYTCGoIGPrGdMxwqRMENYTJQ9+kaY46B4KgVAFBDWHq0Dfpztrzdq40aqZQQFC6mGPoG0goXSCfhjB16BsdkFC6QD4NIfA0EySULpBPQwgVNPyFjoe3Xy8ev3jR0QyCvgb5fPTCAEbzKVDQfbYFlCmLh6XElWW2+aywpFHLJ1ZdaRVEb2VVbQ1gLJ8CBS0kwxovLq0yXlyKDV7cy2p4cS9s8eKe2eHFPXXAixMKVj777uGPSeiHUVCFLP6YUTH8Mcc6YKzM5SFGUClAUC0gqCFA0BKAoBhBICg+IChdQFA9QFAtIKghQNASgKAYQSAoPvnT8OLypuPF5UbQjcuZgRf36mO6cULByuem6/wx1zC8QtPy+WO+Os8fc28NxspmZ2IElYJUUAAwKyAoIGtAUEDWgKCArAFBAVkDggKyBgQFZA0ICsgaEBSQNYSC7vT2Ws4btMpTFZ6OFbo0FKfI0/5O4bkYcctcnSfk88YlNUAFKzUaqonD3xRh8JeMVwOMROJkkT+DWNkjSR2ZoE8c7yZ7lxp2RI+L1ZOSu0XihJ6zCcUoMsv9UlanOP64i6qktIAEvrhVfh4F22E0VBOHvynCwNgorBpgJBIni/wZxMoeUerIBI0LQyhyFk/QASYgJhQjNLVpdChGkXv7I5SZzh/3h1vqq9Z7+OJ+jPEo2A6joZo47E0RCH/JWDXASSROFvkziJU9otSRCbpgJkKx3ENbluCp/x6M0PD4+FCMIpf3CnDsn4JR3pQKNr3UvHH3PQq2w3goG4dwN0UgWCXz1wAnkVhZ5M8gVvZIUkcmaBS7vnC+KPVmz80YoTuGIyav/HFzfW6n9Z3FH3ci4Oy1Ltt449jsaYOMh2qyjLkpQsEoGaMGWInEySJGBrGyR5I6MkHZdc3n64CWF9rzIU5oiMrD3robf9z68QhtD+GPmx7F7Mj68cax2dMGGQ9l43A3RSj8JePUACuROFnEyCBW9khSRyboY5cnmb58bd49ndS4ocwHnz/utvet1OBo/ritjZ+mDI3kjWOzpw0yHsrGmbApguAvGbMG/InEySJGBrGyR5I6wtNM8X6+vGcNJpW3srIKwwpl8ooRF+fpMjqbPy5/uovz8AzeOM3+RxtkNJSNM2FThMFbMmYNMBKJkUWMDGJljyR1cKIekDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrlCNodzvbMnZ2Y6WuhsWgkHwqR1CEkioxk92+UlfDYlBEPhUnaPINqathMSgin4oT9FTwqeARbcZG9WrwEK3xrDUlT+paKRdF5FOJglZJzbWLQVM/Oeb3OG2gme7/fR1QRD6VKGgQQo3uok8jZ3sEBvoOkLpWykUR+VSioF2YhN5nEho9H6G8HKlrpVwUkU8lC3re53lO33VS10q5KCKfShYUra/jOkZun3gFoYh8KknQkuTLLZUKR6b5VK6gwGsBCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsgYEBWQNCArIGhAUkDUgKCBrQFBA1oCggKwBQQFZA4ICsub/rDDF3+r7dMAAAAAASUVORK5CYII=" 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" /><!-- --></p> <pre class="r"><code>print(p5a)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1574,23 +1548,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 95.8401 4.67e-21 92.245 99.4357 -## k_parent_sink 0.0102 3.92e-12 0.009 0.0117 -## sigma 4.8230 3.81e-06 3.214 6.4318 +## Estimate Pr(>t) Lower Upper +## parent_0 95.8401 4.67e-21 92.245 99.4357 +## k_parent 0.0102 3.92e-12 0.009 0.0117 +## sigma 4.8230 3.81e-06 3.214 6.4318 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.91e+01 1.02e+02 -## k__iore_parent_sink 1.54e-05 NA 4.08e-06 5.84e-05 -## N_parent 2.57e+00 NA 2.25e+00 2.89e+00 -## sigma 1.68e+00 NA 1.12e+00 2.24e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.01e+02 NA 9.91e+01 1.02e+02 +## k__iore_parent 1.54e-05 NA 4.08e-06 5.84e-05 +## N_parent 2.57e+00 NA 2.25e+00 2.89e+00 +## sigma 1.68e+00 NA 1.12e+00 2.24e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.99e+01 1.41e-26 98.8116 101.0810 ## k1 2.67e-02 5.05e-06 0.0243 0.0295 -## k2 3.41e-12 5.00e-01 0.0000 Inf +## k2 2.17e-12 5.00e-01 0.0000 Inf ## g 6.47e-01 3.67e-06 0.6248 0.6677 ## sigma 1.27e+00 8.91e-06 0.8395 1.6929 ## @@ -1599,10 +1573,10 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 67.7 2.25e+02 6.77e+01 ## IORE 58.2 1.07e+03 3.22e+02 -## DFOP 55.5 3.70e+11 2.03e+11 +## DFOP 55.5 5.83e+11 3.20e+11 ## ## Representative half-life: -## [1] 321.5119</code></pre> +## [1] 321.51</code></pre> </div> <div id="example-on-page-5-lower-panel" class="section level2"> <h2>Example on page 5, lower panel</h2> @@ -1610,7 +1584,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p5b)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p5b)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1621,23 +1595,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.497 2.32e-24 94.85271 98.14155 -## k_parent_sink 0.008 3.42e-14 0.00737 0.00869 -## sigma 2.295 1.22e-05 1.47976 3.11036 +## Estimate Pr(>t) Lower Upper +## parent_0 96.497 2.32e-24 94.85271 98.14155 +## k_parent 0.008 3.42e-14 0.00737 0.00869 +## sigma 2.295 1.22e-05 1.47976 3.11036 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01 -## k__iore_parent_sink 1.53e-04 6.50e-03 7.21e-05 3.26e-04 -## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00 -## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.85e+01 1.17e-28 9.79e+01 9.92e+01 +## k__iore_parent 1.53e-04 6.50e-03 7.21e-05 3.26e-04 +## N_parent 1.94e+00 5.88e-13 1.76e+00 2.12e+00 +## sigma 7.49e-01 1.63e-05 4.82e-01 1.02e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.84e+01 1.24e-27 97.8078 98.9187 ## k1 1.55e-02 4.10e-04 0.0143 0.0167 -## k2 1.09e-11 5.00e-01 0.0000 Inf +## k2 1.04e-11 5.00e-01 0.0000 Inf ## g 6.89e-01 2.92e-03 0.6626 0.7142 ## sigma 6.48e-01 2.38e-05 0.4147 0.8813 ## @@ -1646,10 +1620,10 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 86.6 2.88e+02 8.66e+01 ## IORE 85.5 7.17e+02 2.16e+02 -## DFOP 83.6 1.04e+11 6.34e+10 +## DFOP 83.6 1.09e+11 6.67e+10 ## ## Representative half-life: -## [1] 215.8655</code></pre> +## [1] 215.87</code></pre> </div> <div id="example-on-page-6" class="section level2"> <h2>Example on page 6</h2> @@ -1657,7 +1631,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p6)</code></pre> -<p><img src="data:image/png;base64,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" 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h/XAULQCcEpPuMC4FMSQY1hEHRlQ/e5Pt+wBTAGbshDHWggc7jncaHvlQkw50GvgTMLeQyjRgQ1RuG1+CNfxv7NNl+zOTN9Rnhs0XhLubN0Q7XUdxW+PvucJiv7Ft4QgsbRmLxzQHGIoMYwCJps7Cz6wJDs3j2/Dx3JmIxU0Be96p9DeLvkQAgaHR09xmMefEoiqDEMggZ6zr3LOl+dAxwyQQbffvEcbFfNyEFKICmQlzpfdoFPqRxBr31cre53eFNCbeJvLWzUZivLfP9/QMA98NyHMRm1oE961D+DlkFCIAXVeMGnlEpQzW+7H/F6w5ta36Sd8fgd60rAtQfNOxhcmWX+Gbc+3eqN8drCmIxe0B2OE7JQc0gEhKD9KOpFwaeUSNDUhs172ifweccJ+n4y37CdMRcOjKBJkaqw7aw3QMhJWjpr3V3mVAwFTYn0PIScRBIgBD1AkcxjJF+JBJ0wAYB/bfnsbSW3BtiH24URtEP8a145cRT0fPtqYYpsJsolqNgNwPHx0THqwY/P2AWZbt/lX/X6FetaQAiawhx7iQsMBV1Yrk7NstabFNgIj0tQ0RuAYyNyHQBppm5hVJKLLd532YB3LSAELWh8nV9ODAV9/2sARlZv3lJ5Y49AbOLRGoBnn7ta4nurfSDCyeN/HGZtDBrP803Yu+/AbOK7W7UJDQ2Fz4ku6KsyBQBcLlewSTVBaf2SIQQV3gD80dyxX7kFerZi/KwddXepynczB8HNqcN3Sr4NgxH0tA74nBh+QcsnAjCzGrV7Mdxpq+Q14gWEoMYbgHO0r72jnri6YiegHVd8XMB0p99BStPvBaysAkBqD2oEDIJ+Wr5Ns3Kr6GdnmzRX1JUlCEGNNgDnal87ZgF4qlI9ApeLN3BMDqYevosUsrbyB6k9qBFw7NT/0Kqd4TxTwWanwY/RE5oLTkF9b4bqYJlVon3tycM0Ic7UA1WCzGbzD/9i1exk5oLG+teH9dNP1KL+nf9Z0WuL+jcYpT2oEXAfdaZNtVvwhjtMHnAKujvN6C4Ts31tVqcQGsdK1MNmAP7wcA4J+fC9hFkVG+pfh+inn6jm3tyv+umi19j+DW6FN5+Qf31R2oMaAf9pkTu9Xb9TSIcQyEudrGdvuNrXpvu36KPu36XPYUbAk+E+Icxp6GiGWDt7l9ghNjdo7UHZEeO83almAb/hzyoCEIImDQItrdawzWG0rzVQVE/t5EpWdcy2R74oNAvs8pD6dwGpPagRRDmxrN1Vp+MFEfLiBkJQt1PJ3V96wqcsqmdcy5dgl5u5bncaQv8i+Eg9uBvs4GF8LiiIdOUjb61j/5uiZMYJhKCO2uhNgHlzKRMU1bNnIvUQaK6f0O67AMh35NeeCT9QjUWMbpLYEe3SXOYC+5Fy7/kJIWi3Xq5pX7SET1lUz0i63Zj3VUHrxZ99nvsvDe5kpoUZBarTnNFNEraBG2BJnWb7+RPRsuMAQtD0by6BmP/Bpyyq5yH3wzcnNWPZK9Q+EKOJd2Kw30R+zYREAEZQo5sknAM3wPJsgu0EOSsKIajwCx+JzWsPO7U2nrm/lezuYjUNPp+igBHU6CYJ68AN0Dz+vMZ4qfeMjAMhKNKFjx/Vn/VzvAG03wY3W2Q4XEp3+q10X+o0uknCO3ADPI+/qDGaR7ddswIhKMqFD60dtQu6pjdY22jDt+0+108r9Zc6X09p9lEM26Uc3AM3wPJ6eqv61QdLfQKEHZjTTAgXPh7Ru1pXfYBv3XoB7lb63dHzjaiH1RZ5Y0k4QXsNOHf+k0FsARwnlkUiv9Ww39c6jFP1kGPXOghBUS58FNjcBeDbcGA1DoBt5fW/GpqAqdf2OPFobqYkoAawpXbo8422X+z89mmmdRkd9phWzgh/1x3i027YhKw1Hq33SX2howQwlzpRLnysd505xuEfYDUwHywrb+goJs6lTnkAI2jrxwA8bM4yP4KmSkQEY7LYv6C/VJ1+NdGmE/WtSQiou0FmzUg4Bb37ZzYAaddXw6csXs+z85ZR/x/+TRxqNrRWVlNZQcDc5aO/euRIh+ks85dYxyYk2Ccwu6aKLeih9y6CrNrtdM+TuzrMkNUhPZegK8u5Ofzial2zNXxKtnr+UGvdhiC8N76WJ5yCrjfA2kH6XFACKLntF1vQ3/1UAQ6tBxpe3Rhj0/9PcRfIBy5BnS6AY2W+5/XTx1rPn/t0V1hfA2HAdJq7e+Qa6wDgFBlRgx1LTBRb0NeOv5y/ErT97evltQLj5TLkGJegNvQfvz1n5Qx9IwLcgu72dGrh4bjNWMyPJW9XIXpBj7i5VJv57oSCnzvbT5bHzb25BLWj/tT8UhJBjUEL+qv7UerxvPde+JziF5Tl2vPtKQ7td2O+FbQQuAStkZKS4kD9pcCnJIIagxa05Qnd07Mt4HNKVNCcH1urp94wvDizS6KWeVyCViwEPiVaPXNHtxxmbKCrrH9l36ubU1B7/f6SlsdmSbpv/H+T1a23ZgKQH+bTy2mBJKsgtzvNaWxtQxysMljnbbGrZyNNleDhFNT3vu7pE4UM+p+XFGoz9Pjmjz7vMcOJ54AoeJCboHOqUr8wdp+yzbrhdBM8q4t3tETscAoa+zH97cvpoZwRgZ8s8a36wdjESDujB3ZiIjdBu9MX6tu0Zpu1eQj1sGAGQnIzwCmodqRqeMxo137GTjSxIP1OfQs7dYtVlb6WYtFyE3RJpWygsZ7ENiuJHuV5HI9h9qQA4jzo5bWTV/BqliG9oMNsxox3LtdwqwQ3tZGboMC9UqMqKtafl4w6c0+vcZR5HxrYTnN8kF7QH5p/OThWtaqbdY8drAPviojsBAVLexi7yeSD4U0/+Rcpt/hYpqDaKNePHdYA8Cq+k3Wv7Wb9HZWfoMrGMgUF4L+Dho5LqZs7V+saz+O0OCJEULxYqqDv8vqHntXarjJTHxEiKF5Kg6AUb/YOcWg084wZmjcTQfFSSgSlKDg1tZ5qyG6xr+0RQfFSegSlub2mY7U2i40PfK9ZHdI1CW0RihL0fPwxsVLjAk1Qs48sgk7Wz2PqOA3azn6LmwltDu2qg9bBXEmCjqs9yC9Mdr26ioMkqBQji+Dg1rpw68aTD5Ro45Nf7RUAyWy9r+BRkKBnvLJAQfB27kApQRJUmpFFsKA5OaeVVes5J4qNZZhG90K7UQcpsYIE3TiCelg0RZzkuEASVKqRRTCReWBKUNWQmONvGz83bG3v1gztVn4ogiYCbXyfAduYXY3EqufhptSS+q4XJzkukASVamQRjKTtm9TEqvWXB/RXmz629naqPgspIYqgFUCs/3eJnZjti8SqZ0G7Lqsj6pn7WjBP0A6SGCOLZI8dQVOHx7hDciDj4MzWVo3G/Hg/u2rWlVsXGvHP8GzGgMWGPVo0QQPvA5DD3McQ7QufFzd2lcz9RBM0a3bEHuqfomGr8uM30DTjcRc1uZD3x7Luaqf3lpx8c5m5S81NikuLVgFN9S2GUAQtDwbcBuAVs/OCkrZI2EESNLJzXMBWAJjdaxRb0NuN/AMqWQduvcrz1MuCqp9v7V1Nf5dfFEFrO3q0A5cbFTXezHS0ofmAZy9QiwJJUHU6eOHx2HIEBU97VKnWe+mA2tWCJ22/AT8sQtsmAGjt9V4hHcXnXPkNnN399nVGKs1KvHdgVxZop5leAhAfZkGCAqDRaZl6aGEPt2qtJ2y7DNWRoKt7LkixitE9Rz3NxNK7fyMR1Ahcgi63mwnAgPYVGJOVLOg7pByK7etdOXD4muNcA7VvV7l1dah+WfccVdDAkpOIoMbgPIq/uBeAgkTmPcUtRFAdGX98M7KZlXvotB8vGb8/kXaAa3OVoVchERQvpauxiEC0t5JieterVK/37J1X2DW9uKdw04wq6EaWSURQIxBB3yX3UsLMnnUreXWP3nI61WiUCJc6iaDGIIKWJO/q7gWfBFZ1aDU0NvEfliH1iKB4IYIK49GRDZO61a3o2nbEot0X3m0ETQTFCxEUhfw7h9ZNDPerYt90YuEkIiheiKA4eHoqufApERQvRFDMEEHxQgTFDBEUL0RQzBBB8UIExQwRFC9EUMwQQfFCBMUMERQvRFDMEEHxQgTFDBEUL0RQzBBB8UIExQwRFC+lbmwmsSGC4qVUjs0kJkRQvOAdm+l/t2nCvTCsmFIhguIF69hMmT6eNNU9cKyZQiGC4kWMsZlIQfFC6mkMvmMzGSAFxQuppzEEnmYiBcULqacxhAo6MNXAzTtQ3PoPLu46XBhsHO7F3qE/clsRBOVVT5i1lVvMf6lGMVlPgYLut7XRU71MWTgg4zCngwU6Hf2Z7f8WVjRs9VRkTBUbY5isp0BBi8iqDBf37RC4uPaHoMJe2MGlWz0WLq7lSaiw+65w6YQDVc/y+dwx1lyD+VC4QNxltu417pigs9wxbY5wx7BBBNVBBDUGEbQ4RFCIICIoPERQvBBBGRBBdRBBjUEELQ4RFCKICApPQQxc3D974OK+hagZRd5CuHTnfoaLW/8UKuzNYrh0woGq51yImPkQEi9iGRmNyQqIW/OufcEds+kRdwwbqIISCKJCBCXIGiIoQdYQQQmyhghKkDVEUIKsIYISZA0RlCBriKAEWYMo6E5vz6WcQSs8HKOyoEIXR0CkPBOgjsqHSLfExWmcljMupQEoXKapUF0Y/AcRCnduuHXgLiRMFbkrCFM9tNKhCfpcdT/Nu8SwIwwu2qakdZ4DE3reOoI7ZY7bpZyQeO50Fx1TMgMTueJW+LkXfgxTobow+A8iFIhPBbUO3IWEqSJ3BWGqh1g6NEHjIwGYM5MjaC8VEBcBEZoRFBvBnTKpDwDZWdzp/nHNyPtwD1fc4Tj3wo9hKlQXBv1BBMOdG2odIAoJU0XuCsJUD7F0aILOnwHAZoh2Si8C9kCERiUkRHCnXBoWqOqTDpFuYiXrMO50D90LP4bJUDoM+oMIBio39zpAFBKqitwVhKkeWunQBI2hlxjFGbbFYwtE6I+DAVVXzrjZPncze87kTnc88Nz1jt9xxtHl0weZDNVXGe6DCAcmN/c6wBQSpooQFYSpHlrp0ASllzZvOkdQQUS3xzCh4Y7u9lU6c8atGwPA9+Hc6abGUBuyXpxxdPn0QSZD6TDYDyIc7tww6wBTSJgqQlQQpnpopUMT9Jnz82xfrr3ePSHQodQXnzPurvedjNBY7nTbGr9IHziHM44unz7IZCgdxuODCIQ7N+Q6cBYSpooQFYSpHlrpEE8zJfj5cp43mFChYsWKkVChVF254+I9nEfkcodppzo7DX7DGafbAOmDTIXSYTw+iFA4c0OuA3chIaoIUUGY6qGVjpyoJ8gaIihB1hBBCbKGCEqQNURQgqwhghJkDRGUIGuIoARZQwQlyBoiKEHWEEEJsoYISpA1RFCCrCGCEmQNEZQga4igBFlDBCXIGiIoQdYQQQmyhghKkDVEUIKsIYISZI08Bb0V0m2N1OtAkAXyFHTacdBK6nUgyAJ5CpqhfRAu9ToQZIG5BXW5DrSL/exbHaSeV6xhZ+O0kloHWzs7u+HFwvaGct85L5nFYbZpBCUjgaCj2t8rOOaWSAmaAsCFcndBmUxm1MEpWu5UUILqR/YFbwcApsce1hF7mN+aEyTB/IJeUWVQ/x6pVaATFLj9ySLo0C69IopP0abo/96+ghJUP7IveDt2MT328K3mfovBi37oH0YRQG6z3iUu+t1X0m6VzC/oWn1hnO7pBD1h/4ZF0HdY5VF7YsHpHo2j6T8w39Njku4V0BVON7NDEgCBx/TPmcXUj+wLisYu1o09PPF30OLN+MtifUSZAbfNMrDbl34s3YJO1t9wusURUNFBbVPmZ8P3eZl+/mfBeaD/+rfxR/2eZfZberrKdUD/HWiUlhOylX4G6MLpZ1Lu3fbU6p/rijkxjKbwht0vAug7gRsG99WNPXyrhf+iG5+a7zNLC8c26+1miSbtJv1YugVdMVr3xPMaXa6C71xBse/z+Uf/d7T5OzugX7oHB/v2Pd0GAPpvygoAdgyinwG6cPqZr1V5C+caAksWkx7ZFxQOA6wfe5gm8rFoH1FmmNxm6TZGum1PRrij+/+B06GasS6Nh0UfoHaAouPAREeHvrnJ4fp50mB+Qc+45lD//uWo0ZVLa53G2ODsaPjPO69i5wFQoDkdStWS+ptM7T/tiqSfAVpQ/UwQdrDRHUMgU1D9yL6gcHBf/djD1MuTs8G0hm3+J97nlA8mt1n0xki/7fmml/bUIKrIG9tlp/oYBL0WnK0JOpYcrp8nDRIcxffr+RSc896q3yMCNa8zBP187Luv/vJJ1fRcWyjo/oCM3A6biwTVzwTbg9sUBjI38fqRfbXXNUWD++p/QcMzHrYHifPE/7zSY3KbRW+M9NueSzWnHaOL3J/aVf2q8Bc05eACuwPJ4fp50iCBoJo5PqrmPwGDoMGrQRkHtVrdzBBwttHmYm9Y5+UysugXFMyt5Tkhv0hQ/UyQWXlLYSDzF1Q/sm9OmXtFg/vqBN25AYCp/sFPxPykcsHkNosupWE79HpT95bU6wHUHvsSnaBj4s7UW3o6nBJUP08a5HYlSdP4j0vH/pZ6LSwJk9ssWlD9tidmIki1enU6dFP7nHS/6OT6mlSPuIXjwGP3fcnh+nnSrL7cBI0dBcCorlKvhSVhcpul2xjptj33mzu5r6YPksa4+S9clhvl1Xnurrv+9bsuCUwO18+TBrkJSiAUgwhKkDVEUIKs4RT03MwhUTNEum8VgcAFl6BLvOds3jzPd5lZVoZAYMIlqEc6/Zjta4ZVIRBKwiVoHd3lwOcNzbAqBEJJuARNUEfOmBHlvIMx+drU0gz+K1DXpP5IkmKqnpwHSc/jY+Zvfs6curFJbOnF6wCSjGyQehpD4FH8RuPNsS2fDiIISuppBIFH8YUF/UH1vvPPCOumSIigeEESlHkUnx+/gaZnO92rP8uN+Ll3+Ruoa6gwiKB4QRKUeRSfPXYEjVdN3avuftSD62jUNVQYRFC8IAlq5Cg+sq7un5APqYd6ESXeZdkQQfGCJKiRo3iDoJsrHANJ5Q4JXzdFQgTFC5qg7BgEBdJU0tAAACAASURBVJ+ULVtuvLAUyoUIihcxBQXZH84VlkHBEEHxgiRoA3c9jMlFgoJHqkuC10yhEEHxgiRoil/cQxrG5LeC7rIv63tE8LopEiIoXtA28ctZx9gqEvRi9WrvV67O9NeyIYLiRdR90FHWdx7U+GCpsCwKRRpBs7AvVC6IKih9JnTz+0OFZVEoUgh6rnHl6kuwL1YeiCro5CqHM3aUUzWIuicskRKRQNAs113gfoNE7MuVBaIKetvGo4pbOeut82tnCMukQCQQ9AQ9ZP8WqcZHEhlRBQWnO3rW7+pfIeyjfcIyKRAJBD0TRD2sH4F9ubJAXEEphlbdGdnYKk5YJgUigaC59RY9TXY7in25skB0QQfZPsmuU+FbYZkUiBQHSXd6qPx3YV+sPBBd0OhO1T3s3vtKWCYFQs6D4kV0QZNr37xzytrLck/UMSCC4kV0QcFqG7XL/qgoYamUBxEUL+ILCvKfAJDdeL2RYEuDCIoXMwiq46bqrLBkSoMIihc0Qdm7HbMJCpJcerScncdn1ZQJERQvYnQ7ZhX0Tvn3W1T0gbiJocIhguIFa7djA6yCdqjb87MnFX7hs26KhAiKF6zdjg2wClp7eJrPevUMPuumSIigeBGx23FxOtcquKMqZ/k3ESaC4gVrt2PtscM0IbVZQh9WrOH33nvXBayishBT0Fs/lIrb3xUDSVBtYhLY1mvOm8LXWV1CaByd2IJTPuu0anPtEp3oLQ0RBW1btnLZPtizyxwkQccFfhjWKWngEMZk1k28nllBpu6ubQmgCJo1O4K++XIkY7JB0FXv3wBnyycJT69IkAR1zMyz/g+kOzMmmxBUGxWeD7tqygRF0MjOcQH0Ld8Ykw2CtmlNPfj2E55ekSAJ6pKSUfE8eF6TMdmEoCC3vYU2rC0ERVB1Onjh8fitoAXf60YL7N1R96qvNx0yAXUFFQbaiXpH10mBsa2jGZNNCQqymn8Jt2YKBUVQj5cAxIe9FfTNZ7rRAuu46F79W77T1y3fs7Rb2f/1WdR2U5dv0I7iL1wGx6duY+Y3KSh47rWKejzYpeWXFrk7iiLocruZAAxoX4ExubCeyd7WDS3tVrrJ6hVbAqaZCDBXY5F3eOCxERyuufNkvzBh6eUN0lH8xb3Udj2ROeAaRz0VTZedALysmms8QAJBwX9OO/tEulcLt7e0zRWNCKeZLFnQhvTgXTUfGA+QQlBwSe1a+9+0OZUuC8sva6QQ9Gq7ip4K7ZY4gtq8H69pYidUEkHBxQp1c8DPFSxx/HoJBM2utSH7Qp1fsS/XHDz3C+qo/t1EgDSCghbvV6vr4XJTWH5ZI4Ggf7SgHuIUOsKQ5sSvr0zNl0jQOF+75YsaWmLrUAkEPUUL+i3zep6FIJGg2vnVyza8LSy9vJFkE78p9x8vC21qK5GgFBedLXI0BykOkq60ed99E/bFygPpBAU33FYKyy9ryGkmvHAKGm2A2fHIBLAFvV93huXthRJB8cIpaFxc3LB6sYsb8Bi8BrqgL5oMt7i2TURQvMBs4hu/AiDNHz4nfEEzOoRa2gV5IiheYAR1zgYgh3mvGRPwKGjekKCn8ImVABGUH3m/70sxNR9G0NHtEve0Hwe/TF4FnetuWRc8uQQNLwQ+pSUL+rReyy4OB00EwAiq2dh/wCYNWwCPkUWM8oODMi/SGYFL0ORC4FNasqBDZwHwhzPitfiCpX01a9mOZviMLGKck07LecXLG4hNfO6xAwf2BcKnVJCgP3+5nt/tCPStmUx0VoUR9It+vgU9R7HM5zOyiAnuNxqcw+8dMgZC0KFBduFuPO4epRxBP/Wf17/WSz7vCN0OwAvU9qC1MoOBhtnxiIbPyCKmyOzbxESLQGUBIWhtzYIzj7rBp1SMoDdqZgEwZg6ft5xULYprNMtEAIygbs+DwUtvlvl8RhYxiXaJk6Xc0hNCUHX2gWWgPnxKxQj6c2fq4ccBvN7zz+dD95iaDyPoyobuc32+YQtgjCwC/nebJtyL1yrq+N1xgWVcVYIQdEJwis+4APiUihH0ruNragdmIdacUNfij3wZy9pZS7M5M31GeGzRHkSmjydNVZWAFXn4YecXAt4mO2DOg14DZxbyuPmeYgQFM2pP6OCH96ZtMIImG/ttGxiS3bvn96EjGZOFFVQzpeYxIe+TGRCCxtHwuG2MVIK+2vDVGZ5vOb18F+ZBimEEDfSce5d1vjoHOGSCDD4DN5jiV6c5rGdbFQWEoNHR0WM85sGnlEjQ+86R0e6S36oaahN/a2GjNltZ5vv/AwLugec+jMmCC/qkQ8s7At8qGyAvdb7sAp8STdBbLdVNBF2sG0HtTD6uLnVbCbj2oHkHgyuzzD/j1qdbvTFeWxiThRdUu9xe6a2YIQXV8DiQRBL0xQcNpzWpIOR2062OUw8NLyIsGwcwgiZFqsK2s36TcpKWzlp3lzkVpaCXG4cpu/UIhKD9KOpFwadEEnSiA/XgzhwwD4aR8wF4pIhf0A7xr3nlRCpo7kz1doS3Sw6EoAcokk1cPGGCVM9e9GWUj9oIeOcDl/6TXSXv9AAhaApzcDAuEHfqz/uGPcroVKVaRAFSGongElTEHgqsbHvvFnj8wRIhb329abH0Q0FBCFrQmOfA3ahHnbmz7Wvafru8cne0NNLAJaiYPRRYaVeuRrmmKAmkBWYT392qTWhoKHxO9NMil8oE/QsSmAO5KgKITbx4PRRYOb/kT6T3SwuMoKd1wOdEFzSt7Cr7acfLo6aRAghBReyhYHkgtQc1AoaC1ujypP/7rPdikDsQghrvoYCjAbiFgdQe1AgYCnqqSvly1t4hV5ETmR0IQY32UMDTANyyQGoPagQsBT17DWhWO4zl1fpVDnAK6nszVAfLLGYD8JyZU2nqW1MPhwG4PrUU/huI0h7UCPi+8SljHJbzOGEoBzgF3Z1mdJ+e2QBcszKWxt+WeqDi78eWwn+bIbUHZQfnJulqV0+TY+zLDshLnaxDDvJpAJ73x5EsnqumSJDagxoB7z7T0Sb+prqlyg0IQZMGgZZWa9jmMBuA62Gr5wOfwI9cShxOWSCwg4eZHGSUAeadeu3uuq1PYs0oJhCCup1K7v7SEz4lWz37fAVAIrMzmCUC1VjE6DeeHexHnfnx7h15nIeVFAhBHbXRmwDz9nwmKFbP9C+8/b/R99TVVpW6JYcZgOo0h+Ebj0juBrdOf2DPKgYQgnbr5Zr2RUv4lMXq2Tvy2qlGG0GzxQN6r66hqL1zYcAIavwbb8YTy7kb3EOU0PMTQtD0by6BGB431n63npnV8gA41hJ8UWHQmErtBayf0oAR1Og33rwnlvPivZv9n+xbOEEIinJlLsWOerjQGDRZNeSTTeQXVP+P0W88ppFFoCnYHeQTJ/NBSCAERboy13S5NrPnTOBK9kFBkaCvpzT7KOYNy3xcI4vw4Ehn9bwS52HkBISgSFfmbrewqTI4B/SbB8B2Hi2iFAuMoL0GnDv/ySCW+dhGFuHDv8NtosZ1G8WzjarZgDnNhHZlLjWbenjUsGFTd7H7C927KP3mCmoAW2p/KZ+1eRj8iWWcPK9duf4nKpnepg5CUCxX5vLP/5kNn0II2WFODV0kH6oARtDWjwF42NxYUOe3TzOtyuiwx7RyRrjkNd1PXaX5fXGXIhCYS52SXpmDZk5/DfjNWeqRCmDu8tFfPXKkw3SW+RE0VSIiGJPFLuivNQYfWl3Vw7bLT1JXjwVOQe/Sv3xp11fDp5RI0Db0Wb36VyRZ9ls4BV1vIIFl/hLr2IQE+wTmLLELeqTCdZDrFfJmS0unaf+Juyj+cAm6spybwy+u1jVbw6eUSNDe3wOQ6/BMkmW/BabT3N0j14z8VJ0LSgAld07FLujBBg6tazb7hHp2bZK65SZ+faLFhktQpwvgWJnveZ3AlEjQo85bfw/vK8mi34Fb0N2eTi08HLexR2REDXYsMVHsgqY4nEz+p53+3n+avb2q99svo009l6A29B+/yw2i1TN71ZCFJloBHe3V9iu2s4tmhVPQX92PUo/nvfcaifmx5O0qRP/G71I1VQ0u+k9OXf+hw2cn5HJRhUtQ+kqQml9Kseqpadn725G108RJjgtOQVue0D092wI+p/ibpIwzxYcMv7uwQc2JZ8VeKhRcgtZISUlxoP5M3hyoOGLV80BL6lsdsVac5LjgFNRe/0ul5fGtl2Sf6d9Zdd0n/fn2d1SqU8xcglYsBD6lWPXcOIJ6WDRVnOS44BTUV3+68YkCxlS/PKu+y5jfdPuj3zp+4HdCkpVQ0J3m/vJMA3ktd4uTHBecgsZ+TA/pnNND/gOu0lxb2MQ2clfaMY+duzc6SjKkuIIEBdNr9q39iVx23o3AKah2pGp4zGjXfjwOlKXtx/3wm47V3Bw9ezrWS5Ji8UoSFFxLkH50MA4gzoNeXjt5Ba+xyiUfaCAzoJqT16j3v5Zi2YoSVAHAdprjg/QF7VP9t6Pdy1fqtML8F+qIoHixTEEXdQ6w7eBzIHGEh9PArQ/NumhRBS1I3s2jq4hFYJmCvnAdvzUshD5BdnN9L7u6n+003xVlMQXNbBbY0yEee35ZY5mCguezo9YWjplTcGF51+r1Pkt4ZJYliynonKEA3LaVV9sDsbFQQZnk/7Ui3K5WVNxV0U+qiClo6D7qoaVyBrHAQSkRlEZ7ZX2Eh23ogqOidjUTU9DRsQC8UZt3n1pqSpGgOh4nftGiSqNR8VfF6r8spqA3VdM3fTiUR4dlC6C0CUqTd2bVJ3WqtZmyi+W2dg94NOJgBUXQrNkR9K2pmTc1elvP+9OHrelWyXqSjJoXik1pFFTHywPzw2vatp+64+Y7u6U3/J1rdEtHyosiaGTnuICtADDbkRSrZ5uZOU8/XiB8GUoDTVClj6n+dP+8cPdqrcbGndc3fmq+BuQNHYuUEkVQdTp44fHYpKCv6NFEzgUKX4bSQBLUMsZUT/19WWTDSvX7fbXvX2vqf/86jx7rLKAI6vESgPgwk4KmVad2nv9sJnwZSgNJUHMPfSMiuRe3Te7gVLZJjxkrGyMlQhF0ud1MAAa0r1D4OqtzCI3ju7c76Tw25b8PVyCsoJikfN6sB+abMiEJKsHQN+LSraJD5fJWNi2HL/v1rsDDfKSj+It7AShIHF/4UnvsMM3od3vVvIis4TZfpkOoaZqO+zNefQlrTiRBJRn6Rkz8egd+1HHoi2PrP//YrVLD3jO/P8v7sg3qaaZbJSdtHI6W0myca0Q9LJ6ANSfaQRJj6JtMH0+aqiocayYBr6tT+6A36+hfZP7146x+/lYOLQcv3Pk3R8+ykx/X7mUYigdVUJbjn7eC/ta56SQ+g7GbmUMfUw9bB2LNiSRoifN2D27ThHvhWDMJyK+WCsCR4t0DHydvmtLDr4pD84jZW08YuZz/n21Q3SYueonFFPSE887Tw9rKtwn8S/VZkNbSSA91gSAJCnPeTlFMbvXL9lpsY6iAxye2zPqkuaqiT+fRS3efZ3QmmVgl/tL8Kvrb3KMKupFlUqGgg6mZBbVkN5jKW/Y7+tqMw/sFQhIU4rydssj/pmOP/aYCsq7sWzWhe+MaVvW7fPrVDyf+p7+k06YV9eD0he65CJc6iwTtTndiacqrd4OZyb2C+9aAaKeZuM/bWShpl/d9PaVfS+f3XVr2m7TqI+sz+WcqrdTNEVPQr9tngsPOIo+6KDOQBGWetzNQCgQtRHP/xI+Lx7V8r9x7Kpt7uiliClowwsbLDeOInU8+bz1UpsOsFoF2FM84b2egFAlqILa6h2qn/qmYggKQegNjW6asutHJi53M045bMKW2sQhe3tzKMzwTV1Cs/NSRehi9XJzkuCCCYkZBgn47lHpYOE2c5LgggmJGQYJed74HUusfEic5LoigmFGQoGCDXaDtLJFy44IIihklCQrSzqF2IBAdIihmFCWoAiCCYoYIihciKGaIoHghgmKGCIoXIihmiKB4IYJihgiKFyIoZoigeCGCYoYIihciKGaIoHghgmKGCIoXIihmiKB4IYJihgiKl9I9eJgIEEHxQgYPwwwRFC9YBw9jG+yqtEEExQvWwcPYBrsqbRBB8SLG4GGkoHgh9TQG38HDDJCC4oXU0xgCTzORguKF1NMYQgVtOFXPlFrNoGjkAxdXJwAqrKkHXDq/enBxtQOhwprUpz+zhwiC8qonzIf3aMod49mEO6ZWEHcMTPXqDJtqDJP1FCjo/VgDMeU7QdHABS7OLggqrN37cOnqucHF2TSDCguuQn/mRanCioatnmU7cMdUCOGOqdiGO6ZKK+4Y6xbcMbZRscYwWU+BghaRVRku7tshcHHt4fpwv7CDS7ca8n4ekDcfvO8Kl044UPUsDzE4jjXEwNEuD7hj6l7jjgk6yx3T5gh3DBtEUB1EUGMQQYtDBIUIIoLCQwTFCxGUARFUBxHUGEoXNKc6XNy2EXBxnY5Chb1Sw6X7BvKGKa1PQ4U98oRLJxyoelaBuImSHcQdyd2fcMf43uSOaf43d0z7E9wxbKAKCiAHLM+DvH9rKuT4/JCLzcmATId3sQjALEGJMbD/sUyQBSUQxIQISpA1RFCCrCGCEmQNEZQga4igBFlDBCXIGiIoQdYgCrrT23MpZ9AKD8eoLKjQxREQKc8EqKPyIdItcXEap+WMS2kACpdpKlQXBv9BhMKdG24duAsJU0XuCsJUD610aII+V91P8y4xqgODi7YpaZ3nwISet47gTpnjdiknJJ473UXHlMzARK64FX7uhR/DVKguDP6DCAXiU0GtA3chYarIXUGY6iGWDk3Q+EgA5szkCNpLBcRFQIRmBMVGcKdM6gNAdhZ3un9cM/I+3MMVdzjOvfBjmArVhUF/EMFw54ZaB4hCwlSRu4Iw1UMsHZqg82cAsBmindKLgD0QoVEJCRHcKZeGBar6pEOkm1jJOow73UP3wo9hMpQOg/4ggoHKzb0OEIWEqiJ3BWGqh1Y6NEFj6CVGcYZt8dgCEfrjYEDVlTNuts/dzJ4zudMdDzx3veN3nHF0+fRBJkP1VYb7IMKByc29DjCFhKkiRAVhqodWOjRB6aXNm84RVBDR7TFMaLiju32Vzpxx68YA8H04d7qpMdSGrBdnHF0+fZDJUDoM9oMIhzs3zDrAFBKmihAVhKkeWunQBH3m/Dzbl2uvd08IdCj1xeeMu+t9JyM0ljvdtsYv0gfO4Yyjy6cPMhlKh/H4IALhzg25DpyFhKkiRAVhqodWOsTTTAl+vpznDSZUqFixYiRUKFVX7rh4D+cRudxh2qnOToPfcMbpNkD6IFOhdBiPDyIUztyQ68BdSIgqQlQQpnpopSMn6gmyhghKkDVEUIKsIYISZA0RlCBriKAEWUMEJcgaIihB1hBBCbKGCEqQNURQgqwhghJkDRGUIGuIoARZQwQlyBoiKEHWEEEJsoYISpA1RFCCrCGCEmQNEZQga4igBFlDBCXIGrkKeiuk2xqp14EgA+Qq6LTjoJXU60CQAWYVNL+Mra1qYDq1VFs7O7tlQLvYz77VQWpGBVu7aq2K3XMvQ/sgnDNfMksI2zSCcjGzoDkgfVQnaqn6G0mOan+v4JhbIiVoJngd0bVY7N7QR5z54ATVjfBLMa+ma0zhUL86Yg/zXH+C+TG7oCBXddUg6BUVfSfNI7UKaEHBgWI3aj04hXlvR22K/u/tKzhBdSP8UiQ1ePq41e/6oX5vNfdbDF70Q/5AsofHNqsYcdHvvpJyq2R+QUF4gkHQtcN1U53u0YJmDOz8bujQLr0iDE9XedSeWHC6R+No+g/M9/SYpHsFdIXTzeyQBEDgMf3zEsXUjfBLMfVLANaM1w/1O/F30OLN+MtiflZ5wGebpWe3L/1YqgUdvlL3ffYEk2N0U1scARUc1FWC/tMHfRacB/qvf/umo37PMvstPV3lOqD/DjRKywnZSj8DdOH0M+MjwW1Prf65rpgTw2gW6hMYRvjdGpSa8nFfoBvq91YL/0U3PjXTp5YSmG3W280STZruZ7VUCxq+3VCuFaN1Uz2v6cpVyPlH/3e0+Tvb9y/dg4N9+55uAwD9N2UFADsG0c8AXTj9zNeqvIVzDYEli2kQtGC02idyuH6oX5rIx6J8RnnBvc3SbYx0256McEf3/wOnQzVjXRoPiz5A7QBFx4GJjg59c5PD9fOkwPyC5qn/NZTrjCtdvb8cNcUEpQRs+M87r2LnUXJpTodStaT+Jq8EYFck/QzQgupngrCDje4YAo0KmvuGkj1WP9QvxcnZYFrDNv8T5XPKB+5tFr0x0m97vumlPTWIKvLGdtmpPgZBrwVna4KOJYfr50mB2QXNGtOhaI+oX8+n4Jz3VsAQ9POx7776yydV03NtoaD7AzJyO2wuElQ/E2wPblMYyL6J117XHPPKeOZ5Xz/UL0V4xsP2IHGeuB9Ycri3WfTGSL/tuVRz2jG6yP2pXdSvCn9BUw4usDuQHK6fJwVmFlSlUn+SViSoZo6PqvlPgCHo2Uabi71rnZfLyKJfUDC3lueE/CJB9TNBZuUthYHsv6A5Ze6B6Y4NEguH+gU7N1CHTf7BT0T6pHKBe5tFl9KwHXq9qXtL6vWAPQAs0Qk6Ju5MvaWnwylB9fOkQH5XkjSN/7h07G+p18JC4N5m0YLqtz0xE0Gq1avToZva56T7RSfX16R6xC0cBx6770sO18+T4hPIT9DYUQCMYjsBQuAP9zZLtzHSbXvuN3dyX00fJI1x81+4LDfKq/PcXXf963ddEpgcrp8nBfITlEB4ByIoQdYQQQmyhlPQczOHRM0Q6b5VBAIXXIIu8Z6zefM832VmWRkCgQmXoB7p9GO2rxlWhUAoCZegdXQXA583NMOqEAgl4RI0QR05Y0aU8w6zrAyBwITzIOl5fMz8zc+ZU3eWKcWU/RP7fwOppzEEHsVvHI70/6FsOhzAnpLU0xgCj+ILC5o9vt2MAoR1UyREULwgCWrkKN5Q0CzrGh9aqUuboURQvCAJyjyKz7TS7zeoda9G2wGQWzkWeRWVBYqg2v2/gvghX2sYk4mgxhB4FB9ZV/dPq7bUQ8MeCCunRFAEHVe/Y0+/Nd3HMiYTQY0h8CjeIOgYW/oXdJHwdVMkKIKq0zXv/QVe1yya8OA2zfwIE++xdNAEZccgaHZ1mxZVnMg+KDzqV4D6Pms8Cl9n+njSVFXjWDGFIqKgIHeS47TS5ieSoBManAQgOXgIY3JhPUslSII2cNfDmPy2oCNLwfgcDJCO4vdfAmDHeuZBEhHUGFyCpvjFPaRhTH5b0F+rWLUpZa3xRDjNRAQ1BucmfjnrCFtFBb1mbV22hu0zIeulWIigeBFzHxSMq3pll+MH0vSnkgpRBb2FPbfsEVXQdq0AmFh5qLAsCkVEQYeVK1s+2nSo5SGqoJ9XvQgOl63h3PWqsERKRDxBfyj/K9hWLhl7enkjqqD/Vbf7wL6cbfw619fCMikQ8QT9uAX1UDcSe3p5I6qg4GgLh1ptncr4fCjN4GdSIJ6gYY2oB/dR2NPLG3EFpYiy/ntTzUobhGVSIOIJeqzc+LNR5a9hTy9vxBfU5hboUe4bYZkUiIgHSRtrlFftwp5d5ogu6Kyuttae1cOEZVIgkpwHffB/Z7AvVSaILugFp+RH3zt6bhGWSnlIIehGVVi99jnYlysLRBcU7PWzCv7rmkqiIU7NjgSCPrO/Dwp6WejwGeILqueY+j9hyZSGBIIeak89JPTFvlxZYC5BQZzXM0nGODU3Egh6zTMPgNmTsC9XFqAJyt7tmL2g/mXLV/+Jx5opFCn2QXt2/G66+h725coCMbodsxZ0ZYUm7Xq+V6J7iMWBImjW7Ig91D/Mi0WcgmrWR0Zb6i1JxOh2zFpQvx65bUdbr+SzbooERdDIznEBWwGoyJxMmtsZQeDgYawF9e0L0v0rLeazbooErdMceOHx+K2gmU42NO/b41gxhSJit+PiLHh/1/8GlSWCmsLjJQDxYe/8gqal0vT1xrBeSgVrt+PMZgE0tiq22N7lK1ReZPkj4aEIutxuJgAD2ldgTCabeGNwCapNTALbes15UzThynmazl6s0Zm388Fl1c/811FRIB3FX9wLQEHieMZUIqgxuAQdF/hhWKekgXy6yf7hYOGXlEifJLwgCeqYmWf9H0h3Zkw2WdATDifg1kyhEEHxgiSoS0pGxfPgeU3GZNMF/UVl0bcyJILiBe1EvaPrpMDY1syOXBwFTVJfAOB29MgdWpNhCoUIihe0o/gLl8HxqduYonEVNFF96bLDrPVNPudePeVhQYLmH02S/obPZmss8i671F2HtKg3onqqsPyyxnIEfdW4aZi95C34JREU7K9gd/jfkVUvCssvayxH0InUFu6KndTtoKURFDi/fxJcKXdbWH5ZYzmCfkSfEPS7JMmy3yKRoG2d3mvl4GyJ4zlYjqAR3wCQbiP1bphEgi7/eJH1qFqWOHio5Qh60WHOt83GSbLod5BI0PxPq9pWmCssvbyxHEHBf5OGbJf8VKBEggLw5sl/7kuF5Zc1FiSoANLP4G44zSlotAEenQZhC/qwwQTL28iXakF3OjRRDcb7f8opaFxc3LB6sYsbfAWfE7qgr1r3k/osBnZKs6AvHK6AN23jsOaE2cQ3fgVAmj98TviC5vRp9RI+ryIozYIe6EA9bMM7/h6MoM7ZlErMOyWYgEdBtVO8LWzU4NIs6N++1DHVgolYc8IIOrpd4p72PM438CroBvVxHtHyh0vQ8EJY5jHvmpLVJYTG0Ylzqdl8V1MU8j8a8tsaFd7x92AE1WzsP2AT894oJuD3jf9NtZlPuNzhEjS5EJZ5zLumaI8dpgmpzbHM8wGVashi5Ju0L0OiMF96ghG0YGlfzdp8tgA+AzcY5brXBNbkygRiE5977MCBfYFsczjummKELNcd4K7vHoiVUyAwgn7Rz7egJ9vIvnwGcrUergAAEZBJREFUbjDBqw4fW86hEoSgQ4Pswt14nAPmqueJVtRD/CD4hEoCRtBamcFAw2w2T8Nn4AZT5E+uZTENmyAEra1ZcOZRN/iUXPU83YR6WD8CPqGSgBHU7XkweMnWN5vPwA2m2eHwHe/3yBMIQdXZB5aB+vApueqZ67Pk2Qn3I/AJlQSMoCsbus/1YRvEm8/ADRxc9v7UMs7ZQwg6ITjFZ1wAfErOet4Od2ik1AEHvm3qOznDxHyoa/FHvoxl7+jGGLghe/QImjouvFcTgPTegRbRPBTmPOg1cGYhj8HoFHMeVABbfI/9M6CPiQAYQZONNWnRbM5MnxEem1v4umDbBppmnrzXk2a1w05B75MXEILG0fDoS2HJgrb5DYA863TjATCCBnrOvcs6f2BIdu+e34eOZEwWWtDzdUZmCXunjIAQNDo6eozHPPiUlixo4DnqQfXUeADUJv7WwkZttrLMV+cAh0yQwa9fvAnSIupJ3cMAGchLnS+7wKe0ZEFn9noD1ppq5wHXHjTvYHBllvn+/4CAe+C5D2MyQkG/c1iq8BZ4kIJq2MevYgVN0OWVynwwGyWBqOQMrGofZOr2BTCCJkWqwrZnssw/49anW70xXsxbzKAU9G6rtsoeyxpC0H4U9aLgUyIJ+me52Oyvy+1HyCAymaZH3YYRtEO8sXvB5iQtnbXuLnMqUkHzY+2/RXi75EAIeoAiOZczrAikekbRP9VBoQgZpAVC0BS+9yhH3Ge63LjzAzCjjs/XSFmkgktQEXsosDKYFjSwq6D3akydoDQTEIIWNL7OLyfqTn1ejEOjDwb3LT8ZLY00cAkqZg8FNs6Wm/1qSbmDAt5Z8IWVVZN/ERaNBZhNfHerNqGhPDYS6Eedl8s0vgFWVEFNIwUQm3jxeiiw8XWVMpUWCnnjqrapYFNdqbt1wgh6Wgd8TnRBX5VZbjfvfDnUNFIAIaiIPRRw8vEh6sFL6hsEorUHZQdDQStNvt/NyhY5jQRACGq8hwKW9rW46JYEgLam1PdfQmoPagQMBf2ufI2qFdT9HyEnMjsQghrtoYCpfS0mdtU9ce/zYEkW/Q5I7UGNgKOgTxetysiabreYx9kYecApqO/NUB0ss5jta3NnTaWpX516OALAjanm/TdWVa3hWAmWW+zfIJT2oEbA942/2dVrL65cZoJT0N1pRvfpme1r85bH0vjXoB7+AOBubCn8tylKe1Aj4NwkHagfoqzL85CXOlnv/Iyxfa1i+GNI782mrm6jtQdlB2tBNevUgx5gzCc2EIImDQItrdawzWG0rzVgyYIeclq3o8UXJgJgBw/jc693zAVNm247RepRKuGBENTtVHL3lzzazFqyoJ0SAXhd1URnCqjGIka/8exgL+ijkfYLZHDVDQoIQR210ZsA8+ZSJrBkQRvRvSVdTGwioTrNSf+NvzFAvfwNd5gMgBC0Wy/XtC9awqcUUVAew3GIw6fU5v2wqYsWMIIa/8ab8cTy5V5OK5TQ3h5C0PRvLoEYHifARRP0SIP3PCTubPcyyPcjJ1ODH8EIavQbb94Ty5d6qmNNdF6RCRCCIl2ZSx1e02spngvkj9SHtOec/8KSSzAF55PZmhoXASOo0W88roEbYLkywG6m6eat0gMhKNKVubARDy415XFAYILvI6iHWTIfiB1G0NdTmn0Uw7YHiG/gBlhujawx+jYAef/xOatgViAERbkyl2FN/fSeai5gxUqyvR/1MH0+llyiASNorwHnzn/CNvSPFCeWn86077nMyav6eKnbgRkB5jQTwpW5VLoFzV88Rn0wwXOnhPRDjpK3+DQN1AC21Jc2n/VIi3FiOf9bfb94Hk3JBJC5pLzXtFMttom6EMFACIp0Za5VTP6rLpg2y2c/qhaIf7xdvMAI2voxAA+NblU6v32aM14/sgiPzZcQDte3tinv1FPchQgF5lInypW5/7WvbDUmj+9aKReYu3z0V48c6TCdZX4ETZWICMZksU8sH6xwE1y2ea/7ATn2UOYU9O6f2QCkXV8Nn5JRz2zdCYCCi+csYzArDjgFXW8ggWX+EuvYhAT7BOYssQX92Xr8ue+sO2zw91wgvwajXIKuLOfm8Iurdc3W8CnZ6vnEv16A5z+81kyZwHSau3vkmpErDueCEkDJHU6xBb1Ue0xgt6GTATg/0iY0UWYtRrkEdboAjpX5ntcRHls9B8wC4HvWUZotDG5Bd3s6tfBwNHJIkhE12LHERLEF1XbonbRQpbs7SObWYPuxZ8VdHD+4BLWh//jtm7DV0/U+9ctR1eQpbsuAU9Bf3Y9Sj+e9jTUc/rHk7SpEb9zwZnH46JuFL+7O86obI5972XAJakf9qY3MY97lwwBbPYOO3br60Eamp9pwwiloyxO6p2dbwOc0e+ub02NVTZZL3bvLAJegNVJSUhyovxSWecy7fBhgq+f6ivZOlcYIXUkFwSmovX5zpDX2rWdBguZh+YeG2bVYdr/o9R+d60fdNxEvHlyCViyEbSbjLh/an3bSfFSrZGRUrz7dB+O5oCRvOAX11f8/P8E4pro45P061C5w4VXd86vq9btnekvS+AnnnebeDOhN48bSksz9Hr0PqoTmXYhwChr7Md1UOKeHEgZczf99jGudSUc1YFZz20b29YWM94IMqqAse9Ns9Qz4A4AX1eV4IhgznIJqR6qGx4x27SfaneYw8/ecgBr9GjungesV10uxfFRBWU4dsdVzs0/SwVZ8R3VTIhDnQS+vnbziDJ+cUndReLTJsVyjmZssUdDXdwrbke7s0v4bC7pBn1FgO83xQWpBAZj3UZVq75dvvuqK+ReNKujGkpOK6lkwqrqHu2Xde5cTyxT0jnr57nG+3w2vpeq/gefYkaiIeTvutSGZ4KCzPO5sbC4sU1BwJbLVxBfUv/fio9zUfdZcMN/GUExBuydRD0147W4pHgsV9F3ubxvmY91hzkFj45jjRUxBB22iDlprST0gonkpBYLSvNgb3bpq/SEbLojez1ZMQY877/5rRJtScH3zHUqJoDSav78ZVN+qxdj4S2JaKqag4FDHgC+UM8gKFkqRoDoyji0fULdy4PB1p1h7MD9JSEJsISSqoKUQNEFlNSIwPBmn1g4PqlKr+5c7/i3eeeKgQ99Ormj3tCWC4gVJUHmNCMyT/P92zerpVcm3z5wdFwvP3LifBGAlWmcnIihekAQ198ANIpBzYfvMXr4V3TuMW3vo7gu6NfEdYfdqLoQIihckQc0/cINI5N/cv2xkW9eK5Rs3bj+iGdKZcCIoXpAEtbQRgbM/Lu/rXNbhA8cWn0zfcOCqoMZsKIJmzY7YQ/0TyZisnHrmbx4x+wnelGgHScwRgU8dpgmpjWHFpEBbY/sX09a01T48sS1mWHvvSraNuo5euO3odW5TL+wp7IKCImhk57iArQAw2zKLJujLaaHjsHZE6NV+wwQnZocANJAEZX7jszqG0DiW7EenDFIZ+6DP//5p9dSI1l6Vreu1i5yycvuxa+xXo7T9azZzMIz2gSKoOh288HhsUtACsP/TyZhGq8luMGb/LJdneJLRXKmVB8BUthEUhIMkqLm/8aLjcYI6iu9VcvqrK4e3xn7e76O61hVrNukyaMqyrb+cvf/2hOl2B88wVXV9L3UUQT1eAhAfZqKeS1UfeNRZO8/hpPBlvMO+EOphxCosuXT8Qo8y88Mn+BICREEhvvHK4rBDn45ud0xFZP/vzL7NCydEdPSvWamSS8N2fT+dsXxLkOPFlJdWMboAFEGX280EYED7CkUTHt6mCS8caGx7w9tvKlK/UgmdhC/jHeKGUQ9fYWz1/Ej1GGh7rcCXEKCeZuL6xiuOpzt+gj80yrr/96HtX88bP9DOql4nrf0k3USko/iLe6mNeOL4wpeZdT1pqqoMr/vuAI+dGl0AV+shLOMtV2o+ARmNfsaSS89Kx74N2+IdkQdJ0BLfeD0KFlQYC6uO29q7mr4LlJh9kvr/CIBzrUtg/kC0ZRSywqG1ytQtYPhzO+EE5rYsaEfxjG+8gVIn6EvXZh82bqFvgiJml4+kuhfThlToEVwH15BUT5NlMpqAcUpbYxFxeDErovAmJKL2SdpQ27rbuV0HZTYclagQQTEjZp+k0ggRFDPkUideiKCYIYLihQiKGSIoXoigmCGC4oUIihkiKF6IoJghguKFCIoZIiheiKCYIYLihQiKGSIoXoigmCGC4oUIihkiKF6IoJhBEVS7/1cQP+Rr5sg8pJ7GIILyB0XQcfU79vRb030sYzKppzGIoPxB6zSnee8v8Jp5s2hST2NY6thMYoIk6CuwCACNB2MyqacxLHpsJpFAEXRCg5MAJAcPKXyd6WxD8wGPm6hZHHjHZspIpelLBBXI/ksA7Fj/9iApTVfPFYPRV0uxYB2bKdNR/41nuTNaqUGETnMbh6OlVDRijM1ECooAS58kUk9j8B6bSQ8pKAJE0OKIcZqJFBQBlk5zpJ7GECrowFQDj+6UJp7RH7kt/kudll/PB6lGMVlPgYIm2RRSpiwckHGY08ECnU73mc8KKxq2eioyxsrGKKbqKVDQIrIqw8V9O4Q7hqb9IaiwF3Zw6VYzrykaoSXcYHL3XeHSCQeqnuUh7qpnDXFTM5cH3DF1r3HHBEF8Y9sc4Y5hgwiqgwhqDCJocYigEEFEUHiIoHghgjIgguogghqDCFocIihEEBEUnoLJcHF//QAXt+YuVFjuNLh0f+yGi1sGNxxn1iy4dMKBquckiBFkp0LcTvdLiKGm50GIvgjiLg0rBQ5TiioogSAqRFCCrCGCEmQNEZQga4igBFlDBCXIGiIoQdYQQQmyhghKkDWIgu709lzKGbTCwzEqCyp0cQREyjMB6qh8iHRLXJzGaTnjUhqAwmWaCtWFwX8QoXDnhlsH7kLCVJG7gjDVQysdmqDPVffTvEsMO8Lgom1KWuc5MKHnrSO4U+a4XcoJiedOd9ExJTMwkStuhZ974ccwFaoLg/8gQoH4VFDrwF1ImCpyVxCmeoilQxM0PhKAOTM5gvZSAXEREKEZQbER3CmT+gCQncWd7h/XjLwP93DFHY5zL/wYpkJ1YdAfRDDcuaHWAaKQMFXkriBM9RBLhybo/BkAbIZop/QiYA9EaFRCQgR3yqVhgao+6RDpJlayDuNO99C98GOYDKXDoD+IYKByc68DRCGhqshdQZjqoZUOTdAYeolRnGFbPLZAhP44GFB15Yyb7XM3s+dM7nTHA89d7/gdZxxdPn2QyVB9leE+iHBgcnOvA0whYaoIUUGY6qGVDk1QemnzpnMEFUR0ewwTGu7obl+lM2fcujEAfB/OnW5qDLUh68UZR5dPH2QylA6D/SDC4c4Nsw4whYSpIkQFYaqHVjo0QZ85P8/25drr3RMCHUp98Tnj7nrfyQiN5U63rfGL9IFzOOPo8umDTIbSYTw+iEC4c0OuA2chYaoIUUGY6qGVDvE0U4KfL+d5gwkVKlasGAkVStWVOy7ew3lELneYdqqz0+A3nHG6DZA+yFQoHcbjgwiFMzfkOnAXEqKKEBWEqR5a6ciJeoKsIYISZA0RlCBriKAEWUMEJcgaIihB1hBBCbKGCEqQNURQgqwhghJkDRGUIGuIoARZQwQlyBoiKEHWEEEJsoYISpA1RFCCrCGCEmSNcgTtYlejjJ3dKKlXw2JQSD2VIygAKVbUw25fqVfDYlBEPRUnaNpNqVfDYlBEPRUn6OnQ06FDWo2KCWvwGKzyqD2xQOq1Ui6KqKcSBa2WkW8XByavPur3LLOfaD2ALR9F1FOJgrYHoNF98PWcL92Dg337Sr1WykUR9VSioB2pgj6kCho7D4ACiBuqEdhRRD2VLOhfPqmanmulXivlooh6KllQsM7LZaTcvvEKQhH1VJKgxdHKrZQKR6b1VK6ghFIBEZQga4igBFlDBCXIGiIoQdYQQQmyhghKkDVEUIKsIYISZA0RlCBriKAEWUMEJcgaIihB1hBBCbKGCEqQNURQgqz5fxjpXZaUu6qhAAAAAElFTkSuQmCC" /><!-- --></p> <pre class="r"><code>print(p6)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1668,17 +1642,17 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7759 7.29e-24 92.3479 97.2039 -## k_parent_sink 0.0179 8.02e-16 0.0166 0.0194 -## sigma 3.0696 3.81e-06 2.0456 4.0936 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7759 7.29e-24 92.3478 97.2039 +## k_parent 0.0179 8.02e-16 0.0166 0.0194 +## sigma 3.0696 3.81e-06 2.0456 4.0936 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 97.12446 2.63e-26 95.62461 98.62431 -## k__iore_parent_sink 0.00252 1.95e-03 0.00134 0.00472 -## N_parent 1.49587 4.07e-13 1.33896 1.65279 -## sigma 1.59698 5.05e-06 1.06169 2.13227 +## Estimate Pr(>t) Lower Upper +## parent_0 97.12446 2.63e-26 95.62461 98.62431 +## k__iore_parent 0.00252 1.95e-03 0.00134 0.00472 +## N_parent 1.49587 4.07e-13 1.33896 1.65279 +## sigma 1.59698 5.05e-06 1.06169 2.13227 ## ## $DFOP ## Estimate Pr(>t) Lower Upper @@ -1696,7 +1670,7 @@ div.tocify { ## DFOP 34.1 8.42e+09 1.79e+10 ## ## Representative half-life: -## [1] 53.16582</code></pre> +## [1] 53.17</code></pre> </div> <div id="example-on-page-7" class="section level2"> <h2>Example on page 7</h2> @@ -1704,7 +1678,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p7)</code></pre> -<p><img src="data:image/png;base64,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" /><!-- --></p> <pre class="r"><code>print(p7)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1715,23 +1689,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.41796 4.80e-53 93.32245 99.51347 -## k_parent_sink 0.00735 7.64e-21 0.00641 0.00843 -## sigma 7.94557 1.83e-15 6.46713 9.42401 +## Estimate Pr(>t) Lower Upper +## parent_0 96.41796 4.80e-53 93.32245 99.51347 +## k_parent 0.00735 7.64e-21 0.00641 0.00843 +## sigma 7.94557 1.83e-15 6.46713 9.42401 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.92e+01 NA 9.55e+01 1.03e+02 -## k__iore_parent_sink 1.60e-05 NA 1.45e-07 1.77e-03 -## N_parent 2.45e+00 NA 1.35e+00 3.54e+00 -## sigma 7.42e+00 NA 6.04e+00 8.80e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.92e+01 NA 9.55e+01 1.03e+02 +## k__iore_parent 1.60e-05 NA 1.45e-07 1.77e-03 +## N_parent 2.45e+00 NA 1.35e+00 3.54e+00 +## sigma 7.42e+00 NA 6.04e+00 8.80e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.89e+01 9.44e-49 95.4640 102.2573 ## k1 1.81e-02 1.75e-01 0.0116 0.0281 -## k2 2.57e-10 5.00e-01 0.0000 Inf +## k2 2.30e-10 5.00e-01 0.0000 Inf ## g 6.06e-01 2.19e-01 0.4826 0.7178 ## sigma 7.40e+00 2.97e-15 6.0201 8.7754 ## @@ -1740,10 +1714,10 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 94.3 3.13e+02 9.43e+01 ## IORE 96.7 1.51e+03 4.55e+02 -## DFOP 96.4 5.32e+09 2.69e+09 +## DFOP 96.4 5.95e+09 3.01e+09 ## ## Representative half-life: -## [1] 454.5528</code></pre> +## [1] 454.55</code></pre> </div> </div> <div id="examples-where-the-representative-half-life-deviates-from-the-observed-dt50" class="section level1"> @@ -1751,16 +1725,11 @@ div.tocify { <div id="example-on-page-8" class="section level2"> <h2>Example on page 8</h2> <p>For this dataset, the IORE fit does not converge when the default starting values used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here.</p> -<pre class="r"><code>p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent_sink = 1e-3))</code></pre> -<pre><code>## Warning in summary.mkinfit(x): Could not estimate covariance matrix; -## singular system. - -## Warning in summary.mkinfit(x): Could not estimate covariance matrix; -## singular system.</code></pre> +<pre class="r"><code>p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent = 1e-3))</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p8)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p8)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1771,27 +1740,25 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.16549 NA NA NA -## k__iore_parent_sink 0.00100 NA NA NA -## k_parent_sink 0.00803 NA NA NA -## sigma 7.44786 NA NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 88.16549 6.53e-29 83.37344 92.95754 +## k_parent 0.00803 1.67e-13 0.00674 0.00957 +## sigma 7.44786 4.17e-10 5.66209 9.23363 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02 -## k__iore_parent_sink 6.14e-05 3.20e-02 2.12e-05 1.78e-04 -## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00 -## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.77e+01 7.03e-35 9.44e+01 1.01e+02 +## k__iore_parent 6.14e-05 3.20e-02 2.12e-05 1.78e-04 +## N_parent 2.27e+00 4.23e-18 2.00e+00 2.54e+00 +## sigma 3.52e+00 5.36e-10 2.67e+00 4.36e+00 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 95.70619 NA NA NA -## k__iore_parent_sink 0.00100 NA NA NA -## k1 0.02500 NA NA NA -## k2 0.00273 NA NA NA -## g 0.58835 NA NA NA -## sigma 3.90001 NA NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 95.70619 8.99e-32 91.87941 99.53298 +## k1 0.02500 5.25e-04 0.01422 0.04394 +## k2 0.00273 6.84e-03 0.00125 0.00597 +## g 0.58835 2.84e-06 0.36595 0.77970 +## sigma 3.90001 6.94e-10 2.96260 4.83741 ## ## ## DTx values: @@ -1801,7 +1768,7 @@ div.tocify { ## DFOP 55.6 517 253.0 ## ## Representative half-life: -## [1] 201.0316</code></pre> +## [1] 201.03</code></pre> </div> </div> <div id="examples-where-sfo-was-not-selected-for-an-abiotic-study" class="section level1"> @@ -1812,7 +1779,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p9a)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCVwU1R/ATVS8ExFYQDlTUUQEzDsxJVNR8UjJUNQ8Ks28xaPEM7w1/6YpidrhraWVWlZomXdlZpp5lpkHHogKuMC+/8zOgrrM7Huz83bmzfr7fj4uy8zjN29/fmHezLz5TQkEAAxTQusOAIAtQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpmBT0TJtOi7XuA8AGTAo6fi9qoXUfADZgUtCsgn/itO4DwAbaCLqvsXfg+HxUoqq7u/s8VDA7rFqLXY+u3xb7r+0A6SICiy0D9I4mgt7z2pGf+eL7qMRd87evxVzI3+O3+eH6XWMLMBEIBM2o9/AVoQUBhsR7wtuUb+zqNKAJmgh6yjMfoZ83WgT93TOLe/0uKL9o/asduic80rwgQ/j38DsCQReE+Re9IvRr1YzM9slnmoTNRtfjaX0MQAU0EbQgrEXaP/zGzYIuGci/mrwvcK8bfH42BhXt7RcFBI/KP9C1QRL/zzQ9MGC0+TtkltG8su1WhKL2CO8fF/SbVP+iV27MMAmh1IRR36Km94cfV+MjsgRuQGVFatKj32k9cNJmDPrgw+5PtzplTlkgGjPNvKzpd9zL8l9Gpj1vsrT6Puzq3fi5B8qfQvy/neGZOW1W8+8QnzZhZVpvdDawQHhvTqV/CZ487t0lf1T0ynM9csuZphGzTr+u4udkAtyA6iGbQvlXENTMlSYvWFK2YIh5QeBJ7iUXTQo+XNjkbf/o6NCeB1ohxP8buwCh9X35d4hPm7DytueDmVMsDa1S+bigplUBq8xvel925KdiEVsDqocjJ57Mv/hXEBRtTOZejrhZUnawRg73etRgNK+b0rOoWcpUhPKNB2I5Qbl/YxZyP9ibf4f4tAkrUedd4ecsDW0Jmp/QSRDzx8mm8fVb/e3AD8cc0gMq83jJPDzKijP4f8al2fimb4MBSTu5YXpSqmmUwaNnbnqcsE4rNDpI+sl0I7lj4U4nvtsV0+Faq83vs2t8UtTsaMhNY7clhYJ+EZmV23ZlkaDCSrQ2ulVhQ8ldfMEp45Y2wrjBFJd1KQZtnqraR2UByQEVP14ShkdLuxfs68uleXnr7JshFkFPRmcbG+5JjxPWaYU2u/jtEdW8Eq4WCmpMDvFs8rmwZlb8Ww+bvV/Td3DRX1DTlKDAEXlFggor0d1yqwobSv4FzSlxYYSLq6trb+4g7AOExkVE/+fgz8cc4gMqfrwkDI+OVR+/x8Sl+WVubPquRVCUsWuG+870OGGdVjB2Jemm+59d/3hf6144F9IDKv633TJUur2iSzPTgdheWxCaYxZ0aOrBOnMPxHGCCuu06j1jgo7qizZU2Kh1L5wL6QEVL6gwPJo2ynSzwq0DsSticu6EJaXXNd4MSJ05DF32354eJ6zTqveMCXqf+8XGXUUCZCI5oDKPl8zDo4tNvP3f4w+ShvpFzJyXm1iz/ZSN5yPqdpwTlR4nrNMKxgQFgMcBQQGmwQp6eFL/xIlH1OgKABQHJ+icWskrV04NnadKZwDAGpygAXf41+xQFboCAMXBCfqM+argtfoqdAUAioMTdJ1X74kTE33Wq9IZALAGe5B0LW3a9JXXrJdeWf4EsyLL/nxvRgVpPXqtsT7XC/mUws6j+NSQwU8u1b+Wq+VDXFBKxEeb202EfD7EZj7tPIpfMUj+/43T8KKt+egYXFDURYRynrFaDPmUQvZR/D/neGb0Fr471rVer78U9k93KBG0pKnXWYRueVktBkGlkHsUf69OEE8lg/m7S4YRE4f6aTaRQCOUCBpsCGiNjoePtloMgkph51F8nxDzl0X+z74Z6r9WSe90iBJBUc7vu9GhTUWz1+5VNs+wLuFDoV96RZGgEkfxFkHj/U3IWHWU/X3TJYoEFceSzycTZYKKY0noq25/o5MVJtkXQ7eAoHRxoKAf1XML8Hxmp30xdIsSQev5C1gtBkGlwAlqO6G5z3Ya36btkzbDWImgGWGpl3isFoOgUuAExSQ0d2XSp/nFfsjJUbSLny9aGQoElQK7i4eEFgPGoHRx4Bj0yQQEpQsIShkQVB55e3bctLUeBKUMCCqLa/WavOi120YDEJQyIKgsXn22QvmGBhuVH0BQyoCgsjC0uJU/w9X6PNAjgKCUAUFlUWUmQjeeslH1EgSlDAgqi/pu89IiymZKN3CooKbAWUb7QugXEFQWS+v1im/b0UYDx/4FvdCh/n77YugWEFQWpnk1fQfaOs/k6F38Bt/Xnqwpy4oEFb/Hy5kFxeLwMWjmUO+PtKtwqj5KBJW4x6swn7ufffq5A/ZH1ycqHCQdaRh9wr5AekSJoBKVWiz5PG3YlbnR8KTVf1YmKNkuKf99z9EK7hbXF0oElajUYsnnPP5epcTV9ofXJYoExeySHnKtf/VPn5D9vBJBbd/jNWs89zJgpf3hdQnd244FxAb1B6Ja/iarY3pF0UGS1T1e9zvE8Hh7m7877rNt93rDPwr7pzeo3nZsQfSos2C511Cb01acBJqnmUx7d/PEWCo5dHJ5qlR/etH1gQNvO7bm1pueS/Lk9E2XOO486Nf17xlvBP5MPTzbUL3tOPedJJ7QGhLNf48J/QblbZz3ncxO6gnH3TSXPIV7eXOJ/eF1iSJBCzZvNa3pnny/8Pu8hbN4Iv0lf+LzZ2JD/IK9+snrpJ5w3E1zyxO5l3Zb7Q+vSxQJOiyqeed2W/tYj4tsXfl40OGpJp/0KXuIsHv6w3E3zd0KGPf5G/WyFYTXI4oENdx9UPlPdMe6MovNS3NNWwz1mOc+gax3OsSB1+KvjotLvk09OuMoEtQ3I8v1CLpW3WqxTUFbRaFTHUv2dNqzojBZhC7KTtQbaoyOSmmZZLXYZkLXVOiYXL9s/We/J+uf7gBB6aLsKP6X42jvuGIlq20m1NTfLajKEtO64PbOeeIeBKWLBjPqz357lXt9sNjQ54J98ZkGBKWLhrd83J3iPuyqfVtgGBCULprek3R9hPtEZ5vODILSReOb5v4e6DHjrn1bYRQQlC6a39X5V4LXvPv4ZroBBKWL5oIidOIln0U59m2JQUBQujAgKELHuvgudhZFQVC6MCEoQr928VnoHFeZQVC6MCIop2hX77nOcLgEgtIFK2iSBesbj2xgZ0J/f9lzho0aKDpBiaASD5PVmaDp8zZRrCeDFTQ1NXVAnZTZ9d4lj2l3Qv/sW21isacu6Qxlz+oUfZisvgR9zbd+cAN6wzWSXXyDWwhlRpDHVJDQ829UHfa33T/NAjQfJmucj5sAzh7HyrdbPqTCLGrxSAT14X4fcvzJYyr6jb8yzj1Rz3UecILGFSKyzvphsg+mCLfQ+FHuoyOZVoUboUQ3phaPRNAhrTdviRlGHtMuQR/8W/gu813vjj/YEYENcIKmFyKyTuJhsrraxc+qlItQo+bU4pEIalz+cq8VouNeesWuOpZ8quyHhd/kLK/VZLNOn7BEsIvP3bNz5/YosTVWD5O1oCtBT1cIn9K50vvU4pEImj+3p1H0hmHiyiJYJrn+8GCsy5Wi7ws+axb0v3uywzAAgaCvNnSP85tLHlJXgqIZ7mHVO9C7vZxE0JHxofndXhNZL6OyCIYQ/o5Ft8dM39+92vh/JZozDIGgwcYZB//tRB5SX4Ki02tp1oQlETTobjQyWt94xCOnsohtwl7iXiote3zhueFVXyk2fmAdAkG9snfOQ3XJQ+pMULqQCOp3LRrdqCWyXl5lEVssKTX/eGyZYrcsZs4PaL5BX9VICAQdEZ0RMiySPCQIKoVF0IX1/aeELBVrYFVZ5J6PG08ZD/n9GFvuKR+x++jyt7aqPvO6/HiaQSCo6SQ6OPMCeUgQVIrCa/HfvZ0iWhLIuPLunYlxKblFC7Ju8cTXpthBhI4NdOt7mGpER0IgaCrPRvKQIKgUFkHTpZ753qdN9kvdPo4dbL2YdkJvzglstFon8/EIBE1KShoaMJU8JAgqhUXQqMAp50XXe+Ugj7soS1bhBvso+LK9x5gz1MM6AMJLnTc6kIcEQaUo3MWfmRneSqz0dMRvKPICumadP8ck9HyS1wub2X/sEqGgxprkIUFQKYrmgz7YFV1OZP1Bvx6d6gytucpqsaMSmru2pffEC46JTQ0CQeM56iSShwRBpbAIurW3Z+e1opOJc7bOfef9Yrt/Byb05AiPtpseOCw8BQgE3cmRnottVgQIKoVF0LZp8mqqOTShOZ8+7zX6DwduQCE4QdWbAO4cEAiaYV0cDIcDEvrYIfyZCT5NV9yhvg064ARVcwK4M0AgaH6DU/JiUk/oN76l3FY9uiD/i25V+nwvdfZLUwh28SpOANc/JLv4LhVaxcbGksekndB/XcOnP1/mp8cXZixq4D+ZwRNPBIKqOgFc75AIesAMeUzaCZ1UNR+hOsWn/xwbaWj+AWu1nQgElZ4ALn9+7T8jukx36qf4KZoPKgFtQQcHcS+tnxNZk/dlfJVun8k4IHY8BIJKTgCXP7/2ik/yZ/0asX922H4UzQeVgLagO0utytjhKnEfVmba8+4D97AzHMUKGvpXrBmRVcXm1+4THuTly738h9C93cW+Dupy7qyx8RzJ9fr/2krJfFAJqI+ZulQsVylC+k/4pTkNqo8+Snmb9oIVdFOm5JDJen7t/fbCoxDLcS9pCO2PKfbVr1q5YK+IUMn1+v9aT8l8UAnoD+r3LPrS9t/IP95+puY7TJwdJbvUaRIdOsufXzup3D/o25J7ybunmEOdw19V83mhyuaDiqPNUeeR0TXCpp/WYsuPQSDo1r6mZhUWi62xml9rwUY+xzSuHuMZtVZOB5Xxp9dHx6aEqFgvU9F8UAm0Oi1i2veWT4OZGjtKIKjfvvQuNwLJQ9rI5/QJZ3dfabuDPJZSprzNvbT5Wr0NEhYPE98lSaDhebuCH97yCZuqZeEHAkENBUkrTNZPP7OBjXye8Vr129RaKv5BG7mQe+m5Qb0NEk0WkdwliaPtieWCH0f4hUzU7JiJQNBO3WtkjmxGHtJWPo/E1Vd1SLij3vm/Dhkuq7dBopvmJHdJ9Ao30KCo4IHp8Phafm+la1L6gUDQO0uPoWkyKlAxdSWp9VOlS45TcXskgkrukugVblBO3sgKZWIfVn5AJ2Y0rNZ3q/q1HwgE1frChxK+DLt07mcvFesVkAgquUuiV7hBOePKtexW/vF7eS8teaFy7HIVd0c8BIJqfeFDCUyOQSV3SfQKNyjHfRhC+0taF8DNXP9K1ajkoyo+upZAUM0vfChgyiTupTVjR/G3xzZ+bprYkSK9wg3KKcf9VueXFJnelJc+prb3wK1qlRcnOc2k+YUP+/nLsOrXd+qo+DgBEkG79zp85JW+Yg2sTiwXfL6Rp2UQvf4RE1Ln3M1XS0kcF51Z1LZSzPyTCN1I/9PB/SAQVB8XPiQ40jVikJols4gK2HID+jx/qUbtH77N7t2Dx9+XTt9k8WOFyuWfniK9/t621/0DWj/9nF8Px5bSIbnUqZMLH0xAImhL7jjjUhOR9Qk85RMSrBZrk9BTSW99g2myr4JnySrVJ2CmPmXNHq7g4jZW0PP7uR1k5qn3yEOCoFJYnvLxstfgwR4TRNbPqZyybl21deusFjOb0C8rTT422bWiR6+VNs5tXyxfzstlgLzA6w2lalqmJ+EEXfiUn8dXNSpXb0kendl8qgFW0GUWrC00c7jhOuRfbCmzCf20LPfHs33EpZUve9QeulXi6m3Tukb0RUlZzwnfV6r9vHBXYSyOE9T7F7SnxMey5q8ym081ILlp7vx3J6XmbGcl9jMUW8hsQr+t2nNbSuVu3LuCX+a2q/zs+K9Fzk24p3AvFT6SE7dDfS6iW7L5PU7QKgiZqkgcyznHc5Loghd0U6B30wDDGqk2nxZ/XAWzCb3jPTDu1XqFdeUe7E1+ruJzyXusqpKFdEbocklZc0ubtuNeAoVT7zhB3bl/XhLrnOI5SZTBCrrDny/beaTWNvKY7CZ0T5B35eRHF9z/enzTiq2mPCrpdpfGL1ewvjhmm2muhwo2unxrfo8T1C0jI8OD+5chss76OUkW2M2nCmAFbSY8EeZQU/KYLCf0v+KDlbs7OUmfe/vrwpsjdz/7zBB5Z6LzI11LluklvMcJ6lqIyDrr5yRZYDmfDgcraDVhvFQgtVsSQYcJvbd7cnTFhiO2yDo2eoQ9ab9a3il50pwzPCeJNlhBQy+a3/7n/EX/c39Mia1Sq//KU4qu3Ct62rHVc5LuVSxhxo6S6k4DVtCUF/hdX07XJ6MicMHxpb0Dq3V6d4/d8/SUPo5bZDaBuvlk7JkVWEELBnsOnDakRryM4gA6FtTM5c2jmlaIHPqxXYV1lAoq8gQ6FfOZP65y+fZqztDHQnAe9PiSMQsOyompd0F5cvcv6OlXLXbaLrmldfQt6Nw21x5MpfegTQoQ3jQnC2cQ1Mzlz8Y/X7lWwnv7ZRzUKxV0efFFKuaz+aiwgNc9it34rCEgKIaCE6uGPFu+wcAPjpIVdlYqqAhy85n/5Ypf8a1EqV3z8IWBZW7a+dOOAAQl4cHhpQPCy0cOWnYY+zAc7QXNDipbsuwb9m2qVp2TGW+VlldQ27GAoMRkH1jyaoNy4f3e22ururP2gr5e8dcH77vYVxug6dCgan29ruAbqgYIKo/cIyveaFIhuPv07RL3DWsvaG3+ASLVpoutynrTL3iarbuxZ8TeMc1vKGtzDgYEtYP8U+uS2nm7tXrrwyPFDp+0F7RxNPd7VHaVaKRXdnzZaoaNn817o0Kllmdlbc7BgKB2c333vMSIcrVemrrlr0f+KGkv6GelOs8Krih2pSGvwjPhz9S3/SRVo1p3FxKiTFC2KotoQd6J9RM7B5WPKiq2ob2g6INqLvWOi63IdUlDptcr0eiTaigSlKXKIppy9/B3hW8ZEFSS+6XGGrNiK1OKpg6KBGWpsggrsCyo6el2FSp0jsQ3ZAhFgrJUWYQVWBYUTWq2bV1NWXezaI4iQVmqLMIKTAtasKJ9189pBVMHZQdJVpVF7ncQiv570+iZTmFaUB2iSNB7kxO2cF96Fy34QXhsivVNNU8SIChdFAnau31q5GqErG+vgYTSBfIpBU5QrzvoesBlEPRRQFC6KDvNdAOhtM4mEPQRQFC6KBJ0vvskZOoV42K1GBJKicInzcFBpwTYo/hftyGUv3m41VIQlBb74KATJotQBnbxdAFBKQOC0gUEpQwIShcQlDIgKF1AUMqAoHQBQSkDgtIFBKUMCEoXEJQyIChdQFDKgKB0AUEpA4LSBQSlDAhKFxCUMiAoXUBQyigRFJ6TVBwQlDJKBIXnJBUHBKWMMkHhOUnWgKCUUSIoPCepOFCbiTLwnCS6QG0mytB8TpIFyKcUsmsz/XOOp0stCh3TK3CaiS5UazPdqxPEUyWQRs90Ck1BHyQn8YT60QupOxxRm2nFIIWd0jNKBK3nL1D4vXH+LJ4XW1Dol16hWpvJAghqJxlhqZd4rBZDPqWw8zQTJNRe5n8jthTyKYW9gibesnDmPJ4zfxE0+pOgzbnTlAIRNfrzljit6R8kycgntuenz2Ia4LOI3Qa+E+cwDc5eJsunnYJ+6e4mUKXEU5QgCkTSiF6g8m6iePxiX9IKEXmCrYx8Uki48m1Q6GUpsnzaKWgR98sTNJoxiaCR+w18m9V9CQKFH8O3+bYNQaCOXxA0sgORh8kWgc9n5M+YBsMWYxqsj8c0uFQd14mAC5gGCZ9gGiwlfFAeCCoNCCoJCCqOcwgq8rTjIkBQK0BQaRwlqC1AUCtAUGlAUElAUHFAUBBUJgWij9y14sfv8G3Qu0Z8mxObCQItvoVv8++HBIHWXCBoRBl8Pv93E9Pgq2KTI604bX3h2pqc2bhOzL2PabDxJKbBL9tx2xBQKigAOBQQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGoWCbqgVOBfbKKikiwvmElBGPXwwcxtMrAUBhsR7mEBCG9uBTCMN3sMLyD4eTfAbxCQAm0hcFvEZxOUPmztLAwItkFJBr3lezKyFu7KWa7AuU1CMBWH+2GDmNphYv1bNyGyfbDuQ0AYT6It6NzPrfkH08WiC3yCm39hE4rKIzyA2f9jcCQ0ItOBRJmhab4SScTNBfg94peFs63qDj/NNqj82mLkNJtY27sdTE2wHEtpgAh37ERnb7SD6eDTBbxDTb2wicVnEZxCbP2zuhAYEWvAoE3T6RIRW9sc02t367IUGq2y3ueSPD8a3wce6HrkF2yuuDTbQrNJdTUQfjyb4DeL6jU0kPov4DOLyh80d34BEC6RU0Gl8LxIJGn7Uw/Z6Pm24YHwbXCzTqoBVuEDmNvhOHQ1LI/54tCDboK1+YxOJyyI+gwT5w+aOa2A7QhHKBOV7MHUCptGh4wht7mW7DZ82XDC+DSZWfkKny7hAQhtMoPXpCC3uT/TxaILfIC6Z2ERisojPIDZ/2NwJDUi0QEoFvepzLTsUdxSxKfxWbrs1ttvwacMF49tgYm1pY8L2SmiDCbQs2ni7y2Kij0cT/AZxycQmEpNFfAax+cPmTmhAogVSfJppXVgo9jyMaZRX8ATMaNi848EE49tgYo1wcXV17W07kNAGE8jYP8BnqJHo41EFu0FcMrGJxGQRn0Fs/rC5ExqQaIHgRD3AOCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0jAp6pk0n3NN+gCcCRgUdvxe10LoPAAswKmhWwT9xWvcBYAEtBPU9hQpmh1VrsYt77+rmXsV7IdePqu7u7gMfttkW+6/tIOkiAostA/SNRoK+FnMhf4/fZk7QDIR+eeo8KnH3sSa7xuJqTmAFtZRJ5Zjj6z2swDS1eo1ploqUKd8o6D2gKtoI+rtnFvf1u6B8s6DIb7+1oK926J7wyLcFGcK/h9/hBRXKpHJvfjVk3I3avLXelcstvj3TJGw2uo57mCrADtoIusS8Mzd5X+AFNf1Q7f5DQTf4/GwM2lXYdFFA8Kj8A10bJPH/TNMDA0abv0NmGc0r225FKGqP8P4xQYUyqdyb32pkPWi+ZdzbCC0ePupb1PT+8OPqfVbtIRlQWZGa9Oh32g6ctBF0zDTzm6bfIVcPryolvrSkbB63bPkvI9OeLywO/X3Y1bvxcw+UP4X4fzvDM3ParObfIT5twsq03uhsYIHw3pxK/xI8eUgok8o3HVW2cmfT6oY3M17oeaZpxKzTr2vwmbWDYED1kE2h/CsIumCI+U3gST5l+R/VQA9TlosmBR8ubPm2f3R0aM8DrRDi/41dgND6vvw7xKdNWHnb88HMKZaG1qkUyqTujTp86sWP8od4hfQ2/9HofdnxH5EhMAOqhyMnnsy/+FcQ9GCNHO7rUYPRnLKCypmPpmxKz6K3KVMRyjceiOUE5f6N4XZOG3vz7xCfNmEl6rwr/Jyl4WOpFMqkcm/GcX+ut3bPvc/5nsJ9++Nk0/j6rf5W44Mygc0BlXm8ZB4eZcUZ/D/j0mx807fBgKSd3DA9KdU0yuDRMzc9TlinDRodxcd3u2I6XGu1sNNB1U89Imh2jU+KWh4NuWnstqRQ0C8is3LbriwSVFiJ1ka3Kmz42C5eKJNacMq4psH1O32S99TMuhp4kft/isu6FIM2T1X7Q2uGzQEVP14ShkdLuxfs68uleXnr7JshFkFPRmcbG+5JjxPWaYNGghqTQzybfI4sgka/h0p4eHl5NebXzop/62HT92v6Di76C2qaEhQ4Iq9IUGElultuVWHDx/6CCmVSc0pcKBjn493vvmmCoR7/VJ4NH3B/UyOi/1Pps2qPzQEVP14ShkfHqo/fY+LS/DKXpHctgqKMXTPcd6bHCeu0gbkrSTfd/+z6x/ta98KJsDmg4n/bLUOl2yu6NDMdiO21BaE5ZkGHph6sM/dAHCeosE6b7jMn6Ki+aEOFjVr3womwOaDiBRWGR9NGmW5WuHUgdkVMzp2wpPS6xpsBqTOHocv+29PjhHXadJ85Qe8buV9yrTvhTNgcUJnHS+bh0cUm3v7v8QdJQ/0iZs7LTazZfsrG8xF1O86JSo8T1mkDc4ICwKOAoADTgKAA02AFPTypf+JENR9mBQCPgBN0Tq3klSunhs5TpTMAYA1O0IA7/Gt2qApdAYDi4AR9xnzR+lp9FboCAMXBCbrOq/fEiYk+660Wn0p6krlC/b/hlNYfSVNs5RN7kHQtbdr0ldesl65oNOvJpdYusUQpAvIphZ1H8SsGKfr/0DcvOkBQyKcEdh7FFybUtOO97xR0TZ+AoHRRJKj1Ufy9ZlE8hprm7/LbNXmrfoLEjzotIChdFAla7Cj+t6M8HQRBN4SFuNQP+l5pD3UGCEoXRYJKHMX3CTF/eb3Svhrvl5+mpHc6BASliyJBJY7iLYLGB6OFPTxG2d83XQKC0kWZoOJYBE3x7pRcxivNvhi6BQSliwMFPeWVlNSi3JN1Gy8IShtFgtbzF7BabBEU7Qwv1/Dpi/b3TZeAoHRRJGhGWOolHqvFhYLyvN3fzo7pFRCULsp28fNFK8E9KmiW9wnZfdI1IChdHDgGFZjfxb4gegUElUl+rs3VDhc0x+8n+6LoFBBUFnnDKpRvY+s4xeGCorQWWtWd0AQQVBYzOtzOn9nMRgPHC1rQ6FP7wugTxYK2L7bEmQVtfIh78ciQbuB4QdERnzv2xdElSgRN4CmfYD3BxpkFfW7vv6dz3TKlG6ggKOqXJN7OKVEi6JzKKevWVVu3rtDjH7cAACAASURBVGjBP+d4pvem0C9GWVSlWuDT0TYaqCHoVY8n6FSTol384YbrkP/Db++HBvFU8lbaKXYZ06hWjda1bDRQQ1C0olG+fZF0iLIxaFZiP0OxhcXy6URE/sy9eNuod6mKoKYX5tsXSYcoPUj6tHhNbWcWNGYnQtmV70s3UEVQdNbjrH2h9IcDTjM5s6Cbgz//oeOrNhqoIyha1DzPvli6Q21B7581Ut+gmnzWrmWKrWtJKglq6vCk1H1XWdAZTwd7baa+RYZQJqj4bcdiCb3mu19Wv3SLuoLuqnsN/eb1D/VNsoMjbjsWTei2II3KRKuMuoKO4XPfay31TbID1duOLYgndHTsE1G8W11Bp0/gXl7YQX2T7ED3tmMB8YTmRU+X0S/doq6gZwxpx6bUtnGWRvc48LZja/7z/UpOz3SKygdJR7uGD7C+o8GpoHrbce7wwTw1a4i3PurFP0k4/7NFP8rspJ6A86B0USRoweatpjXdk4v2MPlpy3maBEi03xh4DeU+6xfi+ZrsfuoGlQU1fbPCuUuwKxJ0WFTzzu229rG+L046ocmN7s0tN/aT7mV/Ju6g3lBXUGObRoOCnbo2hiJBDXcfVP4T3fGxWiydUNOANk1bcl/cJxJ3UG+oK+jKWBO6F3iM+ibZQZGgvhlZrkfQtepWi20kNK+TV0Pu9/5p572upK6gw/lnvPVdQ32T7KDsRL2hxuiolJbW85FtjZmya5fqOr1hudOE3dMf6gr6XiI38q+/j/om2UHZUfwvx9HecWusT8DbPOq861G2VpU5RH3TJeoKmlW7z8LnOzvzXYkqTRZ5hDthHZy5HI7KR/F3F4/81Klng6svKLrXOsGJ597BeVC6aCAouv9CT9vVJPQMCEoXLQRFD15uZeNGU30DgtJFE0FRwVvhzjqHEQSlizaCIjTf95B9wVlHiaD3Jids4b5Y3wYPgkrhQEHRF57OWRFHiaC926dGrkbI1WoxCCqFIwVFv9cc4YwH80oE9bqDrgdcfihowWcbeVoG0eiYTtFOUHS743P/2rcBllEiaMANhNI6m4oEze7Tg8ffl0bHdApW0MKHzlrfeGQD0l2SaZG3801hViLofPdJyNQrxsVqMezipeAFTU1NHVAnZXa9d8ljkid0r9/IHPK4ukDRUfyv2xDK3zzcaikIKoVlF9/gFkKZEeQxZST05kthv5EH1gNwmokuJIL6ZCOU408eU1ZCV3tOd6pjJRCULiSCDmm9eUvMMPKY8hL6b/tIZ5pfjxM0rhDykCCoFBZBjctf7rVCtAIQeWURW6wxjL77T/emE51iVg5O0PRCyEOCoFJYBM2f29O4RGw/LKeyiC0y+nq7BMWUd4r/B4JdfO6enTu3R5GHBEGlsAg6Mj40v5vYjZiyKovYJKJsm+M3XOgP39SHQNBXG7rH+c0lDwmCSmERNOhuNDJa33jEI6uyiE0CX1vi9Ua1ybJ/jj0IBA02zjj4byfykCCoFBZB/a5FoxtidcTlVRaxRftg060BJQbdk/2DzEEgqFf2znmoLnlIEFQKi6AL6/tPCVkq1sCqskjBVnuvHV8t7xZWqlGC93u6n8lMIOiI6IyQYZHkIUFQKQqvxX/3doromSDjyrt3JsY9LJCb3dfua8f3knp8jNDxuBpLdK4ogaCmk+jgzAvkIfUmKNXz2iSCpkvVTezTJvulbh/HDrZerCChRzpWX6TrYm0EgqbybCQPqS9B90eUqb6KXjgSQaMCp5wXXe+VgzzuoiwZhRsI+OUlw/SbSgJoC4GgSUlJQwNklK7QlaA3fbaZfg/YSy0e0S7+zMzwVqtF1kf8hiIvoGvW+VOa0FMDqg47oyyEdhBe6rzRgTykrgTd3vmn9z6fO5paPLL5oA92RZcTWX/Qr0enOkNrrrJarDyhVyZ6dt6tz3IEhIIaa5KH1JWgXwXUfivaT11Bt/b27Lz2rliDnK1z33m/2O6fRkKzV9QPWXxbeRzVIRA0nqNOInlIXQl6oNQHt/eUo/d0VhJB26bJM4VSQvf2qtL/JyqR1IRA0J0c6TJOVuhK0HXtYqo0eON1avEIBM2Q++tALaHX54bUnXuFUjCVwAnqwDsUmOBo7QcI9V1ALR6BoPkNTsmLSTOhP77q1nF99t3fdPMMG5ygDr1DgQVeaTqjR13RAaFdkOziu1RoFRsbSx6TbkLvffxi+VIVy42lGdOBEOziHXiHAgOYPpuUmk0vHImgB8yQx6Sd0JOl6z9TruRUfUwXJRDUoXcoOBuK5oNKQDuhowwIXahe2WPQjgd0AzsCAkGl71CgMwE8d+28H+T9BMMomg8qAW1B3+Qfa9P8+QvzW7i9vI71U08EgkreoUBnAnhWvbZ9a9M7EakxiuaDSkBb0IOlx+yfV/pD7t3V1E6Vnp/3J93wdMEKGvpXrBmRVdYTwHPfNh/whz7NvexG6M8koq8pz5StVr7cGuL2bH9tqGQ+qATUx0xJ5atWeMlyYen+9tf8Aodso3eYSBmsoJsyJcf01hPA8xbN4ol0514OIvT3LKKvLQKnzhpWKpm4PdtfmyiaDyoO/UH9he2Pnen6fd4LlVpO38/k7cpklzpNoqfNMBPAd4SVfZbgIX0No7lf43LLSbqhAxTNB5VAjaPO+ztHhT/daeEx5p6gTCDo1r6mZhUWi62xmgBuwZLPk4b07O0GfDmrtzy6Tw7zlHHWhWkIi4eJ/8ZLoNZpkesbXw+p2mXhUabOPxEI6rcvvcuNQPKQlnzO4U8F91uFbX7Cq/+gro2Y3L3YAdFkEcnfeHHUPG93Zd2QepXbTvuOmTEpgaCGgqQVJuvH89nAks/Z47iX/mn49odfajLyBnl4tiG6ac6+33jVuLF9XPOKEUM//kvdzYpDIGin7jUyRzYjD2nJ5wnvffk7DU797G0RSASV/o2nc2KZBg8OLIz3q9ZhylfX1d/2YxAIemfpMTTtb/KQhfncHlKqQbp9vdIvJIJK/sbTqixChWX1gl5fP+mFKgEvpXydoUUHBAgE1frKnK4gEVTyN55eZRHlLKpoqFmxHXc4d2bd6Oef9uuc/NkFDXpBJKjWV+Z0BYmgt8c2fm6a2K2W9CqLKMe7WwH6pKSll6azmyZ28K0SPSz1kNq1IAgE1frKnK4gEbR7r8NHXukrsp5eZRHllN3MvZR87CnKGd/O7xdZPrjLpPXH1ZtlQnKaSesrc3qCqIAtN17K8xdrYH1i+Z9zPF1kJJ8a/i2zTe+6FBcx/89NU3rULRfSbdLaX+5TLiogBoGgWl6Zu6236kIkgra8jNClJlKN2j98e69uEE8lL0qdk8P6imUrPS15J9qDExunxoeX837a5elhfzv0dlGSS52aXZn7J9qtYg9mThkTQfKUj5e9Bg/2mCCyPoGnfEKC1WJtdklfdW71P8zfxyzfzs+Fl6taLvylCSv3/ifZ7MoJBSMCrKDn92cjlHnqPfKQ9PL5wnRTbr83aUVTBaygyyysE1k/p3LKunXV1lmvYnbMtNer+XuvVU24+/P6GX2beVYI7zb2g2/OWUttbFyqVPnN8gJfG9rmHcvxGU7QhU/5eXxVo3L1luTRqeUzpyL3Yc8FU4qmDiQ3zZ3/7qRoAXCEDjdch/yLLWVW0LWuudxfkcK6cnd+3pgyqHWAa1CbgTM+/anoD2pixX1XEkrLKmF2o2LFuq4Bguk4Qb1/QXtKfCxrjkthPv/q23ywogtJ+ZWzEPrV+qQL2+AF3RTo3TTAsEa8RVZiP0OxhcwK+nmFJZkHPFo/vtB45pvl419uYihbu+2g6R/9cNEwkVvo+rmcuK96ZKMrZVaa3+MErYKQqYq86S2WfF7xmb9vSvAdWT9qxeAuvx9srPCW4GPbLioLIA+soDv8v+dej9TaJtHm0+KPq2BW0EvubaqENp8jvjL75M4PJiS08HuqYvOXx7hMOSLjfvxnO3IvNQeY3+MEdef+yTyItORzCV9FsOsGeT/7OFcjS5VpJ7E3JKMgPrizl4zy5YrBCtpMuP/qUFPymMwKimZX79+0Keae2NGlR0+uUbpTpFeZwBa9Ri/auO8i9pAp0fAA3XAVpgjjBHXLyMjw4P7JuBpryec0vkL6GzJOTxUnoe/l022mKYnw8fN56JrvaXxDWmAFrSbsjgpk/NazKyj6I+1r3PDP1KeUS3V+AkzuuR8+nTv8pWZ+ZbzCYwckL9t26F+JPz5Xy1dpVM5X8BgnqGsh5J225POHmtfQxeonyH+uGMZK3C/nCRnFx4sz7H/cyysqPkkdK2ioMOL47wmqqW6y/ht79diXqVNe6/Ssb2mvsBcTkxZ+mv7H4/MtL/VpNNIyOHTgk+ZS3OtXU3QnR24l7jfsL0WXUWYP5fITpeJdzVhBU17gjvxQTldnLbgqC9PV47vWzBrxSqu67mV8Izv0TZr/0a5jlx/7u6pE0HuTE7ZwX3pbLS7K5+3fFJ5k7zwq93a3cUoiXK8x9tMerVS8hwEraMFgz4HThtSIlzG0dl5BH+HBvz9/tXrWiISYMEMpt9rNi+owKBG0d/vUyNXcKMBqMb18Xu9eodIbyp4vfWViwkI1n1BNcB70+JIxCw7KiflECPooGSd/OFT4XomgXnfQ9YDLDhSUG4Yyd5chBsKb5mTxxAn6KEoEDeBGtmmdTY4UVHeAoJRRIuh890nI1CvGpfD7+x1ieLy9aXRMp4CglFF0FP/rNoTyNw8v+v6H3Twxzyjvlm4BQSmj9DSTyONNIJ9SgKDyUSqoyGO6IZ9SgKDyAUHpAoJSRqmgIpeK2Mrng39UPVMFglLGgZc6mWDW0zV8tqu4PRCUMk4u6K66V9BhL+nbZaijTFB2St8wg5MLOoqf7txDxqOalaJIUKZK3zCCzgU1frO1WH3SR0nmJ6XG0P+MkigSlKXSN6ygb0GvhjzXxfNLGw1OGdaenFFLrMyMg1AkKEulb1hB34IOegehIwZbh+n7O9RJVPOmJEWCslT6hhX0LWjEL9xLwAXVtodH2UGSVembez5uPGU8aPRMp+hb0C4fI3Sjsqx7rh2MIkGLzQDPusUTX5tGz3SKvgU96Dljefhk1TZHgCJBHT4DXIfoW1D0x+hBskoCOBxFgqowA1x36FzQZeFBQ5h62qSy00wwA7wY+hY0LfzguYGdVdscAYoEtZ4BbgEEpYuK+Wz9LUL5VVn6E6rsKN56BrgACEoXFfPZlH9AnbeMqj8OByaLUEbfgs7omGVa2FC1zREAglJG34IaX69QqeVZ1TZHAAhKGcWCti+2RNV8GrNU3BgBIChllAjKVEl1RgBBKaNEUL2VVFcDEJQyinbxOiuprgYgKGWUjUEfL6lumXzjWrzK+pMDCEoZpQdJj5VUFybfLOyvLKSuAUEp44DKIisGKQupa0BQyjigcAMIKgUIKh8QlC4gKGUcUFkEBJUCBJWPAy51gqBSgKDyAUHpAoJSBgSlCwhKGRCULiAoZUBQukDxMMqAoHSB4mGUAUHpQrV4WOFjU3xo9EyngKB0oVo8zLTX/NiUoV1o9EyngKB0cUTxMEgoXSCfUsgtHmYBEkoXyKcUdp5mgoTSBfIphb2ChicJjAtugicslKBRYGN8mwYhBIGCn8W3iapJEKhWvyRRAh0gKHk+gxtiGtStj2kQURvToFEQrhNBjTANQiIwDcJbk+XTTkH/nmVhukt7PDWDCRqVboNvE+ZLEKhSc3ybZ90JAnn2miXK7Fv2JY1OPis3wzTwr4tp0MAb0+D5srhOlIvGNPCpj2kQWo8sn3YKWsT98gSNZkwiaOR+A99mdV+CQOHH8G2+bUMQqOMXBI0og89n5M+YBsMWYxqsj8c0uFQd1wlsheaETzANlr6B24YACCoNCCoJCCoOCAqCygQEpQsIagUIKg0IKol+BM2tQtBodjJBI0Mmvs2nAwgCRZ3At9n7IkGgLio+ba0QfD4b/YZpMHIZpsEW68pQ1lwJwHXimUuYBn03YBqkDsNtQ0CpoOgmQZtskseWkQQy3qEUyERypui2qg+ltoDtPbbBXdwjZvKwfwqUd+JOHqbBg7u4EAKKBQUARwKCAkwDggJMA4ICTAOCAkwDggJMA4ICTAOCAkyjUNANtQLnYhsFlXRx2Wy7SUY9fDBzG0ysBQGGxHuYQEIb24FMIw3ewwvIPh5N8BvEJACbSFwW8RnE5Q+bO0sDAi2QUkGveV7MrFWsqoMVuQYTLs6CMH9sMHMbTKxfq2Zktk+2HUhogwn0Rb2bmXW/IPp4NMFvENNvbCJxWcRnEJs/bO6EBgRa8CgTNK03Qsm4mSC/B7zScLbtq4bfpPpjg5nbYGJt4348NcF2IKENJtCxH5Gx3Q6ij0cT/AYx/cYmEpdFfAax+cPmTmhAoAWPMkGnT0RoJa7+/+7WZy80WGW7zSV/fDC+DT7W9cgt2F5xbbCBZpXuaiL6eDTBbxDXb2wi8VnEZxCXP2zu+AYkWiClgk7je5FI0PCjHrbX82nDBePb4GKZVgWswgUyt8F36mhYGvHHowXZBm31G5tIXBbxGSTIHzZ3XAPbEYpQJijfg6kTMI0OHUdocy/bbfi04YLxbTCx8hM6XcYFEtpgAq1PR2hxf6KPRxP8BnHJxCYSk0V8BrH5w+ZOaECiBVIq6FWfa9mhuKOITeG3ctutsd2GTxsuGN8GE2tLGxO2V0IbTKBl0cbbXRYTfTya4DeISyY2kZgs4jOIzR82d0IDEi2Q4tNM68JCsedhTKO8gidgRsPmHQ8mGN8GE2uEi6ura2/bgYQ2mEDG/gE+Q41EH48q2A3ikolNJCaL+Axi84fNndCARAsEJ+oBxgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpmFW0DNtOuEeSAU8ATAr6Pi9qIXWfQC0R3VB80pUrerZ5w635aru7u7zUMHssGot+Ee6uVR1r9Tir6J2WQX/xNmOlC6yXmwZoGc0EDQH3XmtHbdl4VFjr8VcyN/jt5kT9C66ndDxYcNtsf/ajkQgqLlaa1HFVKHyqpmUb+z+AICqaCIoyvX8wyLo755Z3Ot3Qfm8oGhnYFG7XWMfK4tSkCH8e/gdgaDmaq2osGKqUHn1TJOw2eg67nGqACtoIyiKW2cRdMlA/tXkfYEXNKtPe+6bDT4/G4N2vdqhu/mZp4sCgkflH+jaIIn/Z5oeGDDa/B0yy2he2XYrQlF7hPePC2qu1ooKK6YKlVdHfYua3h9+XMVPrCWkAyprUpMe/U7LgZNGgg5caE5ZIBozzby06XfIxcOrfMM/uffLfxmZ9nxheejvw67ejZ97oPwpxP/bGZ6Z02Y1/w7xaRNWpvVGZwMLhPfmVPqX4OGfZlpYDNNcMRWZK6+eaRox6/Trqn5kDSEeUBWxKZR/feIFjVtrSdmCIealgSfNu3gzuWhS8OHC5m/7R0eH9jzQCiH+39gFCK3vy79DfNqElbc9H8ycYmlolcoiQc0VU4sqr/a+7KhPxxokA6qHIyeeTPOf1Sdd0AdeJywpO1iD9/WowfhQUISm9Cx6mzIVoXzjgVhOUO7fmIUIbezNv0N82oSVqPOu8HOWhuKCChVThcqrHD9ONo2v3+pvB35IZsAPqMzjJfPwKCvO4P8Zl2bjm74NBiTt5IbpSammUQaPnrnpccI6LdBE0HtD2xbtdOK7XTEdrrUaPSJodo1PipofDblp7LakUNAvIrNy264sElRYidZGtypsKLKLLzhlFCqmCpVXuf+fuKxLMWjzVNU+sobgB1T8eEkYHi3tXrCvL5fm5a2zb4ZYBD0ZnW1suCc9TlinBRoI6unp9UpmkaDG5BDPJp+jRwWdFf/Ww/bv1/QdXPQX1DQlKHBEXpGgwkp0t9yqwoYif0FzSlwQKqZaarOiDR8gNC4i+j8Hf1AmwA+o+PGSMDw6Vn38HhOX5pe5Ieq7FkFRxq4Z7jvT44R1WsDglaSb7n92/eN9rXvhHOAHVPxvu2WodHtFl2amA7G9tiA0xyzo0NSDdeYeiOMEFdZp8QkYFHRUX7Shwkate+Ec4AdUvKDC8GjaKNPNCrcOxK6IybkTlpRe13gzIHXmMHTZf3t6nLBOi0/AoKD3jdzAUetOOAn4AZV5vGQeHl1s4u3/Hn+QNNQvYua83MSa7adsPB9Rt+OcqPQ4YZ0WMCgoADwEBAWYBgQFmAYr6OFJ/RMnqvkwKwB4BJygc2olr1w5NXSeKp0BAGtwggbc4V+zQ1XoCgAUByfoM+ZL1tfqq9AVACgOTtB1Xr0nTkz0Wa9KZwDAGuxB0rW0adNXXrNeurHEE8xTB6j/N0A+pbDzKH7FIEX/H/rmxV3UQ0I+pbDzKL4woXkbpm/TZpaLhoCgdFEkqPVR/P0WUTyGmubvjM+1Gdkk7kkzFASliyJBix3F/3aUp4Mg6Keh7l6GoCftFl4QlC6KBJU4iu8TYv4y8Okzzb8r/46S3ukQEJQuigSVOIq3CBofhJI6Vxthf990CQhKF2WCimMRdH7155PKuX9sXwzdokTQzaggrUevNdbzXUFQKZQJetEwfPRLLqfti6FblAjqglIiPtrcbqLVYhBUCpyg9fwFrBZbBEWH2wd1HdDV/r7pEmWCRl1EKOcZq8UgqBQ4QTPCUi/xWC0uFJTH2HCVfR3TK0oELWnqdRahW15Wi0FQKbC7+PmiJ5EeFRT9Ue2M3D7pGiWCBhsCWqPj4aOtFoOgUigbg1qY1yLfvjD6RNFRfM7vu9GhTdbXNkBQKagIWvBCsn1h9Iny00zGC4Xv8lcv53npBaUhdYzjBUXXfL63L44uUS7oqaK057w5mKdmdaUhdYwKgqIdfjfsC6RHlAtakGO1wDqfTxRqCIrGxj45U0YccCUJBJWClqB5z6XYF0mHgKB0UUVQ9K93un2h9IcSQTEXPp5I1BEU7fb5x75YukOJoAQXPqy59e7gZQ/s3yL7qCQomhORbV8wvaFoF09w4eNxbgW+sbxTjDNXU1NLUFN8f/uC6Q11x6ALBnJH/ZF7qW+SHdQSFN1rsMC+aDpDXUGHL+JeEtdQ3yQ7KBNU/K5O8YRervGFjH7pFnUF/SgmH932O0F9k+zgiLs6JRK639OZE1mIuoLmdwl9xTeZ+hZV5Pr7s4/ZWk/1rk4LUgn9NOAJeDaB2udBD6z9k/oGVeSU4dVxvh/aaED3rk4ByYROj7wrscZ5gBP1sui5FKFzbjbmuznwrk4RBnQwYkLqHhBUFmH8g1H9bDw3jepdnfkrzdPDmgRItc/v9kqBKX21Mz+tFQSVRfwShM5UddRf0ILNW01ruiffL/w+d4QwPayG5E9kNx/Zxrtm1STJBroHBJXFaUPf0T6rbDRQJOiwqOad223tY30K3ualOZ/Sw1Z2KOu8f0NBUHnc+GCezZM7igQ13H1Q+U90x8dqsc2ENio3FxVUfRsTWb/QFDTvf7N4IiWHTE8AigT1zchyPYKuWU/4tinoc1EBH+S7TSbrnQ6hKWjupCSeUOkhk/Oj7ES9ocboqJSW1iNKm4IuK9ezWu1yf5D1TofALp4uyo7ifzmO9o4rVqrFZkLzY70CSnYm6psuAUHpotpkkYccWv9twGL7IusAEFQeexK6fGDrvnQNBOX4+5lZ9oVmHxBUFjurr/ys5Vs2GmgjKLpcN8lJ76MDQWXR7nOEsipZ38f6CBoJim41S8yzLzrjgKCyaPAr9+L7r3QDrQRF99p2vo9vpT9AUFm8+ZYJ7Qiy0UAzQVHe4GeL1WZ2AlQXNEtOqLzfTrM1tLrdvHaTGvttNNBOUGR6J/ikfRtgGZUF/cC90jO7iSP9UbteYNPrFLpED9PxAzZvp9RQUITWe35p3xYYRl1B9wadQ+mGK6SRGqchU1IilU6pBVbQJAsynrlNPmba5+10d9I5oEa9jXwmzeZeem4kjH+/Ihf6b387e6cNWEFTU1MH1EmZXe9d8pgyBvUXI/rYOMWgRxxQo95GPpOTuZeO2wnjF1TJQOinhnb2ThtIdvENbiGUGUEeU85RZ3ZCxDny1jrAATXqbeTzuPeX/y31v0O6gfEtdm2uvdre7mkCiaA+3Cg2x588przTIku8tslpzjoOqFFvK5+7m3l3Ij/UzF/WtvMW+/qmFSSCDmm9eUvMMPKYMs/bHQwY4UTVhXCCxhUisk6iRr0znwfFQiKocfnLvVaI3u0mp3CDNLe6RZ1GZ78k3lExDU7Q9ELEVorXqAdBpbAImj+3p3GJ2IVJeYUbbLCsWo2nSrs4xSxmgl187p6dO7dHSa5+WKPeuECYUe9Po2M6hUTQkfGh+d1eE1kvs3CDDV52afPfhyXPyv459iAQ9NWG7nF+cyVXP6xR/2CyMKPej07XdAmJoEF3o5FRrJC/3MIN0tTsP8XrY3cZp7KYhUDQYOOMg/92klwNNeofhURQv2vR6EYtkfWyCzdIEtkB/RLuIuNiALMQCOqVvXMeqiu2hs6Y3qkgEXRhff8pIUvFGlg/jvvOLZ742rK7sbHk4JX1y1RbpP9nfhEIOiI6I2RYpMgKamN6J4LoWvx3b6f8LLbeuPLunYlxKbmF39/zdeMp4ym/Hx8Yyof/ebpNxEH5P8oWBIKaTqKDMy+IrKA3pnceSARNlyox3adN9kvdPo4dbL3Y/oR+6tv/qt0/zAQEgqbyiF0/pzemlyAnOaLRUgfPt8ua1nnYBXrhSASNCpxyXnS9Vw7yuIuyZN0XjyFrbLXZur46TyAod2A+NGCqyAp6Y3oJ3uj08w8N36MVTZS8pv22TfW2fg6E/RDt4s/MDG8ldgU34jcUeQFds86fsoSe7hKwlq1JtbIgvNR5o4PYUusxvQA1QfMrc2OIn8MpRRPnh2e5l9HTqcUjmw/6YFd0OZH1B/16dKoztOYqoy7bogAADuhJREFUq8VKE7qnUeTXyiJoCKGgxprkIakJml2ROwg9b+sGC+Vs6Mm9vD+EWjwSQbf29uy8VrT0bM7Wue+8X2z3rzihpk0hz+n1yRUEgsZz1JExbZjeLr71LJNxID15xLjg/Q/KeW4ttXgkgrZNuy0rJoWE5q8Jfj5dcRQtIBB0J0d6LrZZEfQEvdjMo0pXWTcxyWeZZ2vfAfTGaASCZsit5kkloXmrazb/UodjUZygDr1DAc/1THqxJLj2Pc0ZvgSC5jc4JS8mpYTmr28QtlqYiPfPjtNUQqoATlAFdyjkb3r3C/Z/ZU3bJn9I8amCJLv4LhVaxcbGksek9htv+rqtzzTuoHZ8ObfyPSnFdDQEu3g771DIi35+QuOuzBvau/HUbqH0HpZBIugBM+Qxae6Sfh/klphWauxP80rbelYJQxAIaucdCmvbcn9Eo8jvMdaGn2txO71EevdCKpoPKgHdS3M351UpszQTNX+eZlDHQSConXcovDONe3nLsafZlbPuZe7lg9epxVM0H1QC2teOR1bt6ZZYO4ZuUEdBIKj0HQri6Oov6PHA+wj1WEItnqL5oBLQFvRk6R4rn3vKY6IuyjJjBQ39K9YMQazCCctVuJfvUV4j/6Y+tb5D6K8k8/eMfh1Wp7+fz2hq8Z5VMh9UAuqzb5ZVeLpc0vFxNeq/y/6ce6ygmzKJx/SFt3xU5V72I3Q2oW2/FO7rhVnC94x+/fHtPu/Si9dY0XxQcehPD7t3nD99V/DDEK+IGYz/HSW71Gm6JSMkTLeTAjcfVAIHJjR/z1t+tcbsYbi0KIGgW/uamlWQUQQdBJXiYfEwhn7jTUeTo6rGr2b1wckEgvrtS+9yI5A8JAgqReFkEeZ+4/9b2dO9/sgvWXx2MoGghoKkFSbrp5/ZAASVovAgicXf+PxDM56v2HT8V6yVeyAQtFP3Gpkjm5GHlJvPIz2ajboh70fYhURQ6d94je9CzNkzpXWl8KGfmI/t73++joXdPoGgd5YeQ9NsPIHaGpn5POH14U9vNmJ4mC4LEkElf+NZuAvReHDBS9U9O05b4+HlXZG0DqEDIRDUwVfmxs7kXiJ1f/ehBRJBJX/jmbkL8d+tE9yecm/sXfac5nMpCASVvDInv4CtGANWXv8tu8NXsn6GXUgEvT228XPTxB7J4fC7EGVQfsW5zWNK+FZuOnjxt1ru6gkElbwyJ7+ArRgfG6rWd+dL1TqE/K0Lf3BQaFFIBO3e6/CRV/qKrHf4XYgyqDSX+0P61JXbe5cOaeXh1rjfu5t/ozgrkRyS00xSV+bkF7AV4yMft7BqVRz0qITcps+PqDPUMbFFISpgy42X8vzFGljdhXi/QwyPtzfFDpLSrdzkBT6FpV9v/Jg6tkvdsr4t+0//5KfLD3f7OXO7v3nGsf0gEFTyypwdBWxFGLDyxvGcWAft4pd158Z0QcccE1wMEkFbXkboUhOpRu0fvjXt3c0TY/0nQA1uP1vF0/vx610Ff3//4YReTbxda7bul5z61e83Te06vv2ml2Ov55Nc6pS6MkengK1DD5KG8vOUen/smOBikDzl42WvwYM9JoisT+Apn5BgtVibE8um0z9LTGHLOb175ZQB7ULdXF1KG56u1u7Lo5ds1YZ4t1r5JjILD5h2LztkeYsV9Px+buiReUp0XieVArZ/GFb8NKSxg04zvdcboQchhx0TXAysoMssrBNZP6dyyrp11dZZr2L2ysea0ps2LDRU6xDpW6bSM0079RuTkrol/bdL9x5rtNDljcX+HrLOBuSFlSlZprvwHifowqf8PL6qUbl6S8kWDwvYWpCbz6PxzcbelPcjxNyr1316k1ccFFwMkpvmzn93UuJv0+GG65B/saXMCrq+zFmUF8GXvkCZp3/atnLOuAFdW4b5li/jWavRCz0GjZ62aOXG3d5dz93KdPlJTtzkssdMn7t8Y36PE9T7F7SnxMdS1a54HhawtcBUPrM/fGebmufy8IJuCvRuGmBYI94iK7GfodhCphL6KMcNni8E1hlTbHnu1T8Pfr1x+dy33+r/Ukxpz0C30iUq+NWOim7bI3HwmPGz5i9fvXH77h+O/n7u71u3xNRq0o57CRLObOIErcINCKrYrDIJBWwfBSvoDv/vudcjtaSeFfNp8cdVMJtQU2z7SUMN4oXQioj1uoqmufxz8dTR9F0b1yyf827SyMGJPTrFtIiqF+Tn5layRFm3qkFBIVFR0TExXXv06D948PBgj6RV+VWmmX8cJ6g798/6ML0Iq0vHuZOEGfU1iD+h84EVtJlwWvZQU/KYzAqKchfFj7yAaXO3RkmX0rbuSsy+dePcuZNHj+7ZvXvLxo1py5cvGloyOC60rDA/AyeoW0ZGhgf3T+xEuvWl47z/CTPqAzBddmawglYTdkcFkr/1xWFXUCJM+7fIPcv9mV/ZUMuJI5ygroWIrGPm0jFDYAUNvWh++59oTXVxIKF2wtKlY1bACpryAl9sKqerWMFVCSChdsLApeO/Z4xLV29rBGAFLRjsOXDakBrx8u/jfjJR9Lx4BxewxXPMc/SsmtKPcNIAgvOgx5eMWSDrwhkIqgCRqQIq5rP7h9xorrKMP0YOh/CmOVmAoAoQeUSiivmM+JV7CcSciFMVEJQy+ha0bwpCJ6rZus6lNiAoZZQKurz4IhXzeSmgU39PsSfkaAYIShmlgoqgZj7vb0q7qN7WCABBKaNzQZkDBKUMCEoXEJQyIChdlAkKj48uBghKF0WCslC4gTVAULooEhRm3xQHBKWLIkFh9k1xHCjoV/XKRqlaNIEFFAnKwOwb5qApaM6bg3lqClVITnrvyfnC8C+98LpA2UGS1eyb/NXLeZrIKNXodNAU9PF8zhnLvfRbRS+8LlAk6L3JCVu4L70Lv3/8N/7JxHG7+NnjuJf+adTDs40iQXu3T41cjZD13Quwi6eLJZ9/eP9o3AG7+EfBCep1B10PuAyCPooDD5K+qFs6ci/16I9zevwwtgo3KjvNdAOhtM4mEPQR9H2a6bBn8nv1ZNzd43gUCTrffRIy9YpxsVoMgtJFxXx2XYPQ9cq5qm0Pj7Kj+F+3cYeam4dbLQVB6aLmjPpfuJeAC6ptDw9MFqGMvgUdMAWhX7xgRr0To0RQOjXqlXClZut4T6kqR5oAglJGiaB0atQrInfH+ivqbY0AEJQyygSlUaPeuQBBKaNEUDo16p0LEJQySgSlU6PeuQBBKaPoKJ5KjXrHsq1B1ZjfVNweCEoZ5aeZFNeodyRHfdNvptXIVG+DIChllAvKdI36ifwzbjqq+ExUEJQyygVlukb9uDncS9et6m0QBKUMzStJ2X168PgzNL92X8AJ03YfFZ9GD4JShqagBZ9t5OnbHt9UNVZXd20g6xk9CgFBKaNE0Hr+AlaLVwxS0iHqPFB1ayAoZZQImhGWeonHajFjgqoLCEoZRbv4+d+ILQVBpQBB5eOA6XYgqBRQm0k+DqhRD4JKAbWZ5OOAEuAgqBSyazPdvcUTD4LaDwj6OFRrM93zduNx9aXRM53igBr1IKgUdtZmgoTSBfIphdzaTBYgoXSBfEph52kmSChdIJ9S2Cto4i0Ll887LZduidPaAYI+Cfl8HMJ82inoZ26FlHiKEkSBSBrRC1TeTZzD9iWNTj4pJFz5Nij0shRZPu0UtIj75QkazZhE0MidYILX6r4EgcKP4dt824YgUMcvCBpRBp/PyJ8xDYYtxjRYH49pcAk7+Q9bmSThE0yDpW/gtiEAgkoDgkoCgooDgoKgMgFB6QKCWgGCSgOCSgKCigOCgqAyKRhL0OjbHQSNJhDcZ/DrxwSB3iUw/e/3CAItE5kX52jw+Zx1HdNg835MgxO4pzTcfwfXicn3MA1WH8c0OEj4yHqlggKAQwFBAaYBQQGmAUEBpgFBAaYBQQGmAUEBpgFBAaYBQQGmUSjohlqBc7GNgkq6uGy23SSjHj6YuQ0m1oIAQ+I9TCChje1AppEG7+EFZB+PJvgNYhKATSQui/gM4vKHzZ2lAYEWSKmg1zwvZtYqVnbEilyDdUX2YiwI88cGM7fBxPq1akZm+2TbgYQ2mEBf1LuZWfcLoo9HE/wGMf3GJhKXRXwGsfnD5k5oQKAFjzJB03ojlIybCfJ7wCsNZ9t+9N43qf7YYOY2mFjbuB9PTbAdSGiDCXTsR2Rst4Po49EEv0FMv7GJxGURn0Fs/rC5ExoQaMGjTNDpExFa2R/TaHfrsxcarLLd5pI/PhjfBh/reuQWbK+4NthAs0p3NRF9PJrgN4jrNzaR+CziM4jLHzZ3fAMSLZBSQafxvUgkaPhRD9vr+bThgvFtcLFMqwJW4QKZ2+A7dTQsjfjj0YJsg7b6jU0kLov4DBLkD5s7roHtCEUoE5TvwdQJmEaHjiO0uZftNnzacMH4NphY+QmdLuMCCW0wgdanI7S4P9HHowl+g7hkYhOJySI+g9j8YXMnNCDRAikV9KrPtexQ3FHEpvBbue3W2G7Dpw0XjG+DibWljQnbK6ENJtCyaOPtLouJPh5N8BvEJRObSEwW8RnE5g+bO6EBiRZI8WmmdWGh2PMwplFewRMwo2HzjgcTjG+DiTXCxdXVtbftQEIbTCBj/wCfoUaij0cV7AZxycQmEpNFfAax+cPmTmhAogWCE/UA44CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA04CgANOAoADT6EfQDu5uJdzdX9O6G06DTvKpH0ERyqjAvWwK1bobToMu8qk7QTP/0robToMu8qk7QQ/EHojt3+K1aZ3rXUaLAoJH5WvdK/2ii3zqUdBKWXnuqWjMe9+HXb0br+5twU6FLvKpR0FjEAq/iP6X/LZ/dHRoT617pV90kU89Cvoil9BLXEJTpiKUb9S6V/pFF/nUs6BHQ24auy3Rulf6RRf51LOg6P2avoNZ+43XEbrIp54EfZwC1lKpcxjNp34FBZ4IQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBp/g94dHaoqPf61QAAAABJRU5ErkJggg==" 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" /><!-- --></p> <pre class="r"><code>print(p9a)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1823,23 +1790,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 88.1933 3.06e-12 79.9447 96.4419 -## k_parent_sink 0.0409 2.07e-07 0.0324 0.0516 -## sigma 7.2429 3.92e-05 4.4768 10.0090 +## Estimate Pr(>t) Lower Upper +## parent_0 88.1933 3.06e-12 79.9447 96.4419 +## k_parent 0.0409 2.07e-07 0.0324 0.0516 +## sigma 7.2429 3.92e-05 4.4768 10.0090 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02 -## k__iore_parent_sink 1.93e-05 1.13e-01 3.49e-06 1.06e-04 -## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00 -## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 9.89e+01 1.12e-16 9.54e+01 1.02e+02 +## k__iore_parent 1.93e-05 1.13e-01 3.49e-06 1.06e-04 +## N_parent 2.91e+00 1.45e-09 2.50e+00 3.32e+00 +## sigma 2.35e+00 5.31e-05 1.45e+00 3.26e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 9.85e+01 2.54e-20 97.390 99.672 ## k1 1.38e-01 3.52e-05 0.131 0.146 -## k2 5.75e-13 5.00e-01 0.000 Inf +## k2 6.69e-13 5.00e-01 0.000 Inf ## g 6.52e-01 8.13e-06 0.642 0.661 ## sigma 7.88e-01 6.13e-02 0.481 1.095 ## @@ -1848,24 +1815,19 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 16.9 5.63e+01 1.69e+01 ## IORE 11.6 3.37e+02 1.01e+02 -## DFOP 10.5 2.17e+12 1.21e+12 +## DFOP 10.5 1.86e+12 1.04e+12 ## ## Representative half-life: -## [1] 101.4264</code></pre> +## [1] 101.43</code></pre> <p>In this example, the residuals of the SFO indicate a lack of fit of this model, so even if it was an abiotic experiment, the data do not suggest a simple exponential decline.</p> </div> <div id="example-on-page-9-lower-panel" class="section level2"> <h2>Example on page 9, lower panel</h2> <pre class="r"><code>p9b <- nafta(NAFTA_SOP_Attachment[["p9b"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p9b)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p9b)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1876,24 +1838,24 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 94.7123 2.15e-19 93.178 96.2464 -## k_parent_sink 0.0389 4.47e-14 0.037 0.0408 -## sigma 1.5957 1.28e-04 0.932 2.2595 +## Estimate Pr(>t) Lower Upper +## parent_0 94.7123 2.15e-19 93.178 96.2464 +## k_parent 0.0389 4.47e-14 0.037 0.0408 +## sigma 1.5957 1.28e-04 0.932 2.2595 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 93.863 2.32e-18 92.4565 95.269 -## k__iore_parent_sink 0.127 1.85e-02 0.0504 0.321 -## N_parent 0.711 1.88e-05 0.4843 0.937 -## sigma 1.288 1.76e-04 0.7456 1.830 +## Estimate Pr(>t) Lower Upper +## parent_0 93.863 2.32e-18 92.4565 95.269 +## k__iore_parent 0.127 1.85e-02 0.0504 0.321 +## N_parent 0.711 1.88e-05 0.4843 0.937 +## sigma 1.288 1.76e-04 0.7456 1.830 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 94.7123 1.61e-16 93.1355 96.2891 -## k1 0.0389 NaN 0.0316 0.0478 -## k2 0.0389 1.13e-08 0.0203 0.0743 -## g 0.7599 NaN NA NA +## k1 0.0389 1.43e-06 0.0312 0.0485 +## k2 0.0389 6.67e-03 0.0186 0.0812 +## g 0.7742 5.00e-01 0.0000 1.0000 ## sigma 1.5957 2.50e-04 0.9135 2.2779 ## ## @@ -1904,7 +1866,7 @@ div.tocify { ## DFOP 17.8 59.2 17.8 ## ## Representative half-life: -## [1] 14.80013</code></pre> +## [1] 14.8</code></pre> <p>Here, mkin gives a longer slow DT50 for the DFOP model (17.8 days) than PestDF (13.5 days). Presumably, this is related to the fact that PestDF gives a negative value for the proportion of the fast degradation which should be between 0 and 1, inclusive. This parameter is called f in PestDF and g in mkin. In mkin, it is restricted to the interval from 0 to 1.</p> </div> <div id="example-on-page-10" class="section level2"> @@ -1913,7 +1875,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p10)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p10)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1924,25 +1886,25 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 6.42e-11 91.9259 111.5371 -## k_parent_sink 0.0495 1.70e-07 0.0404 0.0607 -## sigma 8.0152 1.28e-04 4.6813 11.3491 +## Estimate Pr(>t) Lower Upper +## parent_0 101.7315 6.42e-11 91.9259 111.5371 +## k_parent 0.0495 1.70e-07 0.0404 0.0607 +## sigma 8.0152 1.28e-04 4.6813 11.3491 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.86 3.32e-12 90.848 102.863 -## k__iore_parent_sink 2.96 7.91e-02 0.687 12.761 -## N_parent 0.00 5.00e-01 -0.372 0.372 -## sigma 4.90 1.77e-04 2.837 6.968 +## Estimate Pr(>t) Lower Upper +## parent_0 96.86 3.32e-12 90.848 102.863 +## k__iore_parent 2.96 7.91e-02 0.687 12.761 +## N_parent 0.00 5.00e-01 -0.372 0.372 +## sigma 4.90 1.77e-04 2.837 6.968 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 101.7315 1.41e-09 91.6534 111.8097 -## k1 0.0495 6.41e-04 0.0303 0.0809 -## k2 0.0495 1.66e-02 0.0201 0.1219 -## g 0.6634 5.00e-01 0.0000 1.0000 -## sigma 8.0152 2.50e-04 4.5886 11.4418 +## Estimate Pr(>t) Lower Upper +## parent_0 101.7315 1.41e-09 91.6534 111.810 +## k1 0.0495 6.48e-04 0.0303 0.081 +## k2 0.0495 1.67e-02 0.0201 0.122 +## g 0.6634 5.00e-01 0.0000 1.000 +## sigma 8.0152 2.50e-04 4.5886 11.442 ## ## ## DTx values: @@ -1952,7 +1914,7 @@ div.tocify { ## DFOP 14.0 46.5 14.00 ## ## Representative half-life: -## [1] 8.862193</code></pre> +## [1] 8.86</code></pre> <p>Here, a value below N is given for the IORE model, because the data suggests a faster decline towards the end of the experiment, which appears physically rather unlikely in the case of a photolysis study. It seems PestDF does not constrain N to values above zero, thus the slight difference in IORE model parameters between PestDF and mkin.</p> </div> </div> @@ -1964,7 +1926,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p11)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAADAFBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcICAgJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7////isF19AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dCXwM1x/AlbaKBhG57wRFxBWtW5Q4U+KOCHG0TVuqrTiiqDgbt2rRkr+4WimC0halGv70H1QVVZQ6WtUiQg6S2CT7/jN7BLOz897svN15u/l9P59Mdmd+++bNL9/NXO+9qYAAgGEqqF0BAJACBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZiGSUEvde71sdp1ANiASUEnH0Lt1K4DwAZMCppX+leU2nUA2EAdQY+09AycXIIq1HJxcVmESueH1m639/HlOyP/li4gQ0RgsXmAvaOKoPfdd5fkdFuBKuTr3r4RcbXkoF/6o+V7J5ZiSsALGlSxUiW+SO1MH99ZWrTA23OsodDkfQqqDtgYVQQ971aC0M9bDIL+6pbHTQ8ElZQtH9Wzf+xj4aVZ+p9H7/CCFnlo9S+2N/r3Rrvvf/HIyg9Lv9QqdD66HU1xSwBro4qgpaHtUv/iV64TdPlr/FTreZWbbvb6WRNUtrf/KCA4oSSzb9NE/kc7OzBgvO4d0smoW9h1O0JhB/WvnxD014AhLebz/zInTUPo43dP++Y9bLst4XvU+sG7Z2y3oWyAO6ASkJL4+Du1D5zUOQZ9+J/+NTqe16UsEE2YpZvX+gA3WXVyXOrLhv996IfQm/nRCzOrnkf8z54mOYWd1/GvEJ82/cLUoeiPwFL9a10q/SvwFKP9nf642nQtN2Ndi+ysLoNQwnPVe2svtW427/c31dhgNcEdUD1iawg/BUF1/NuqiyFlS0brZgSe4yZFaGrwcWPINP/w8JBBmR0R4n8mLkHoy+H8K8SnTb/wntvDuTMMgSap3DCQm5SMdq8/9LVDYcfPd9vAzxx6wwYbxxRSB1SPjpx4ci7yUxAUbUniJj85G1J21LeQm57w0OiWzRhUFpY8k/NLkxnJCcr9TFjKfXAo/wrxadMvRL33NrlsCHwilce4HXl6DPei6AFnevIk7r/09v7c28PTtZMbd/zTJpvJCOYPqHTHS7rDo7woD/8dXJo1b3s3fTVxD3eYnpiiTfBwHVSUEaVfphYqnST9qL2T9IpxpxPd71/t8XrrdK8LfD8vCztRP1vTb7lR0K+b5xV1XVMmqH4h2hTe0Rj4xC5+a5O7Rd3Xl57XHKybdzPw2vqmt3OHJXF/mKi86xEofaatt1hVzB5Q8cdL+sOjlf1Ljwzn0ryqU0F2fYOg58ILNC0OZkTpl6mFOrv4Xc1qu8feNAqqSarv1uor/ZJ50e88CltR1zu+7D+odkZQ4HvFZYLqF6L8KmuNgU/8B9UmuAe/X1pY4ar2fY9G6ah0kpfnCO5/6ebPuNOmZuH/2GQrGUL8gIo/XtIfHp3ymXxQy6V5MHds+qFBUJS1d47Lnowo/TK1YOxOUrbLhb6/rVC7Fo6F+QMq/ttuOFS6t7pPG21mZMw2hBboBB2TcrTBwswoTlD9MrVqz5igCcPR5mpb1K6FY2H+gIoXVH94NCtBm13tbmbk6ojC3NDEjIaa7ICUuWPRDf9dGVH6ZWrVnjFBH3BfbNxdJEAmZg+odMdLusOja608/ZfxJ0lj/JrNXVQUV7fHjC1XmjV8ZUFYRpR+mVowJigAPAkICjANCAowDQgKMA0ICjANCAowDQgKMI2Fgv67qhyzOo/u3wDyKZEZCwVNqR9ffvH5zrKkQT7FkcwnVtDjU0fGTflJOHf164r+HvZNN6n26JYB+TQHTtAF9ZLWrJkZskgw25jQLV7P+Ze7PmggKF0UCRqQy08LQgSzDQk9XjFuR9TTVyyvm10CgtJFkaB1dI3PbzUWzDYktF8oN/F52+Kq2ScgKF0UCZrmPnTKlDivLwWzDQntzI9Q0zDW5FOODQhKF0WColups2avuSWca0hoytOZ6JtK9E9q2QYEpYsyQaXP4gc99XTFtyyumZ0CgtLFmmfxKP9wobmPXkj96iFJ/ewOEJQuVM/iNYvn8XTDD4+40mtk54aq9RSwJiAoXaiexT+ckcgT4odba26tvxB6bxJZFe0LEJQu1jiLH1ZfLPjf5QvOGl8fe4mbfNeVrIr2BQhKF2ucxYsKesotfoKncdyFLBfu4GDuaMI62hUgKF2UCSqOqKCvrEPoNzfjuwlNF43RDbDicICgdLGqoNqOsYu+z9a/rnPp4uni2mXDUX0zfr7Jf16HAASliyJBG/nrEcx+9B/0t7XvtK/u32fGV9dQ93oBoX4ullbTfgBB6aJI0KzQlOs8gtlP7uK1l7a838PT2e3pDsNq+FtSRftCkaDQfNEEZbv4xaKt6cSOQW/6vNXC7blKsR8fyiGum12iRFDcjY/yiM1OktodQOifaivfbP18UN8ZOxy3EZ4SQaWbL5ZPbCbol8Gb94YncC9Kf98yJdK3Rvsxq489sKx8plEiqHTzxfKJzQRFu/p0XVlc9i77h6Ujm1d5YeCcXdcsWwmrKBFUuvli+cR2gopQfHZTYnfvmu3HrMrMt2xV7KHoJElw4+N+gyCe6thbxw6MqoLquXPgo1Etqgb3S9p6sQQfzTpULzNdv8zTpx7FIu0NBgTVUXJhy7Q+QdVavLr0+9uWrZURrHAd1JJ8OgwqCfpgTpeYTNPZeZmrxoTXcot47z/H71u2btVRIij2xkc5RCVBowbuS/E4ambh398tGN68anCfaZvPaiyrgYooEZToxkc5Qx1B//ThzudXDZcKKbmwNWlAvSqNB3/41WV7Gvdb0S6e/MZHuUEdQU+EcZPdPfBFFZ7cmNgzoNqLIxfu/cuyytgaOAalizU6zeETWujxI3rYdz4uzEhuZsp7XTxrtH596X7mn1VoTUH3zfiReuGsY41OcwTf+O+8WngNED3AvDZ/1gnxz9z972dvd3Jzbhe/7HuGn7VlRUEbV3KtGE69dMaxxtA3JLukgp/E7yAdcxs3zWetxAezMlaOftnVuW38R/v+xq/G9lhP0NlVstG1ZzZQL55trDH0jZJjpm5pCF1wxT66LOvgp2939qjR6tWFe66q9yQ+MawnaPjL3KTxQOrFs43tOs2REcS3c6qVTRacfXj1e139qoUN/XD778X4cJtgPUGHBXITl8nUi1eXH0cMWCN1lYZup7nTJ3h61iWvn5DeKQj97CPrI3nH1yX2rlMlZMC0tJMFlq+ZEtYT9K9nmo9/oco9sYhffyD8SjPHPq9Pt7RNkAhQJGhp+nbt+v5JZa3mHrQL43HxlFXHJzjnOSTe7SsLPlh0evOMQU2qBPYYv/rwHcvXrxgrniSdb+vTWey4u6hnnZfd7PQZpj22I5TjVGQ+QJGgY8Pa9u6+fdhIwWwlu/iSSS5O0aL/Jcg+/sfXC15t7Vy7/euLvr2sStMTFa6Dzh9Ugs665lJfry1oeoqb+Eic7ioS1CP/YfULKNdLMFuJoB++fOnmawMs/7yeWwc/G9c9oErjgdO++In+Qw0kUUHQPvwep/1/qa/XFozmdu/7AiQCFAnqnZVX+Sd0S3jIqETQpr8g9NDJ7Jhjsij45csZ0U2rend686O9V2x1u1QFQd9ajJDG7w/q67UFd18Kae99WCJA2YV6D9/xYckdEgWzlQja6FduP12dZl8Q7bV9n4yO8H2u8YCpG45hDx405xReXVVB0LNuC7a90p/6am1D6Xdpkgcnys7iT55BhyatF/5zUiLotF7ZhRN6Wv55s9z/OW1GTJiTW/vX5u84b3bkx+NBDdyjFF0KUONe/G9v9ltmp4NZ3u/tVueF0xIBrDRYNvJwbPVq/W9a/vkyclPmiTXnu/HDZwmRdZ+r03Pcyu//NLnErw3+Cmlip8he2b3k+JUGQ6CxiCwmjihGnzeUCGBNUE4SKpfcb/gNSQxMNrdUc2Hnwtc7elVpPOD91COPXce9GpB38Of/tZa7spzgN1ZFvay/aACCyiLsZ27iKdG2gj1B6TA2iTuXd8YMEpH/85ezh7V0qfHikKQvjvHD6eY4uYc3qN9N7so+7tbas0ejA7rXSgRNR6WpA2OoHjKxTsQehAqrS3SfcFRBI/ZfO1n44nGi2DuZ66dFh1Wv3SpuVuVmuzc7Rcld2Qinb/5JqfaJ7rUSQSuh5GYb0rsLDzFYyKe12FJn1+Fewgvpj4MVNNGAsE2dBCwkdHQDr2YeTrKG2bl5OHV0Nc9KzwbUnpX2k6x7BQMDSlBu1cW618oEDbvG/UOpY3xfNE0/YrWv5UUyz7au7eZKXVXECpqSkvJqg+T5jT4kXycLgk6pGj3J00nuqe39Gvno3+nNpwxq7lS7ZWzS55lZ+M/wKwtpGOsTskn3WomgFbUxfyB01934vniZbsz/5lIXsh0dkl18U+7wLKcZeZksCNp9x3/mH292Uu7HxnRI/9hDd0/m5o/rpsW86FwzbNCUNQcx10b/F7D58w0e+iN9JYIGewR0QmeajBfMZiGfqkEiqFcBt9/xJy+ThYQO+w/3D8hL2D8SS8l/Brz2RBeWO8c2zRre1qNKo6iEFXsvmutkusbVqY6hvxtO0CgjYgsLf92Pjm0VXv1iIZ+qQSLo6E7p2yLGkpfJQkJ/9Ez9vn9fasXdP7194ZtdgioHRsTP33pS5NC2bOgenKAZRshXzkI+VYNEUM2qwTGrZfRQZyKhR6I7z6XeOlRz6buVE/o2eb72S4On/OeHa2LNpQh28UUH9+zZFUa+VibyqRYkgpYsHKRZLuPyueMn9FbmF7NHdPCpXLfbWwvTTz5xxk8g6KgWLlF+C8nX5vj5lIBE0HHRISX93iAvs9wktOjC7uUJfZo41QobuNg4j0DQYM2co3/3Il9NucmnGCSCBuWHI42MbhjlLqFZx77cZHxNIKh7wZ5FSOr+s4Byl8/HIRHU71Y4uiM6BKClAzc4MASCvheeVX9sc/IiIZ/mMAi6tLH/jPorRZZbPnCD40IgqPYcOjr3KnmRovm8ve07iZ486kF2a4MconvxB6Yl/yy2XMHADQ4LgaApPDI6uYnlc49b3/C67I0DtMWjtutGqiWSCJphrruEFQZusHsIBE1MTBwTMJO8SLF8eh9FaE6cjHrZhAuep9E571M0iyQRNCxwhvhjY6wwcIPdQ3ir846MXgMi+fyH76j4m4wTLduwYgw3mbSAZpFEu/hLc5t0XCcWIBz039OZ51lXihW0NwgF1cgY3EJE0OLqNxHaTDB+pW1ZO5ybvLWcZpFk7UEf7g2vIrJcsyY/d0pU8qOD9fy7PNHwH1SSaI4GMnbPYnukOY1WznY7IqNeNuGmV+qfGz2ojuRKIuj2oW69N4k9J2ZY54IB/TZGxgtng6CS7OHIkHEKLprPr9+Y+Bt5EbbiVKRfd5PLjoogEbRrqpnmu+6FyDUf5dHsF2/34AS10wbgqkEgaJaw33sZzU6j5lfRLWH+IKES2GkDcNUgELSk6Xkzy4/6DezVYEzdtYLZkFBp7LEBuGqQ7OL7VOsYGRkpFlC4feEHK0wuQUFCpbHHBuCqQSJopg7yMiGh0phvAA5tG0yA9qCUIRDUbANw+2/b8HM3nwhzz2ezDGgPShmsoCEXI3WILDJp23BkP0+ENzf5B6H7+x/93pPUOWrrftP56v7eXCvx5zSP8zTL7QjtQemCFXRrjtlDJmHbhgc9Ing8q3CTVIT+F/Hot0/I/i+9GpvOV/d3Qy/u9+gEmuU2UtQeVBwQFIv2rthc8rYNhU73Edr7svzqWZcVo7nJRJvfizfbHtQMIKg024dr21T7WGyJ8KEUekTyeYsf3OFMqAX1syq/e/6CznqfoVmkovagZgBBpfE7ktHnTiB5kWL5bLAFlbz5jox62YZ0LxePTfgwGRAOHia+SzIDCCqNR2niaq1wYH8JxPJ5MriRbxcWn5tA+3k4RI1FzO6SxAFBpenV3zdnXBvyIkXzqTkj3kbX0SDqNEdhl1RuIBA0d+UpNOtP8iIhn+YwCEpjl1RuIBAUbnzIgERQKruk8gKBoHDjQwYkgprfJcG9YxMIBIUbHzIgEfTexJbtZ4k9usj+7x3Th+QyE9z4IIdE0P4xx38aMlxkOfSLN4VAULjxIQOiAWy5A/pif5Hl0C/eFJJbnXDjgxwSQTvcQOh6K5Hl0C/eFKygV/5XgFDO+WXkRUI+zWF4ysdg9/h41/fFAgT3jguGDOTx96ZcSXsCJ+jSp/xcv/Wt7tOBvEgQ1By8oJ8aSDMX9NjoAdpdW3g6BNOrn92BE9TzJDpYYaOsZy+DoOYwdJq7cuCc+ADgsTxVY2MFsyGhEtTkvsc1xcYONw/k0xw6QbcGerYO8FgvtnxB9eS0tNppwn+ukFAJXLgfd+kQIZBPc/CC7vb/gZv+VG+nWMDxFmnI32QuJFQC56ysLFfuR8Y4mpBPc/CCttE91godE38EcF7cCA+TmZBQCSobEVlWDh8miwUraG398VKpud3SF6ZPpIKEWkg5fJgsFqygIdd0L/+BQf/JoPkwWQOQT3PwgiZ3yeOmhX0VjghcbqD5MFkDkE9z8IKWxru9Nmu0b7S9PWlOLWg+TPa+FwwITHAd9MzyCUtkjRYBglqK8GGyefoBgV9QXi3LKP79mlqrNkLYaU4WIKgCLpnOwufz/nXhE5KpcLZ+Pa8IkSfn2hIQlDJKBRV5yCwun9p3qvsEHVa2WlGar0Mlo9+yQsEyAEEpo4Kgqzrmoj3e1B/tjHJqcpMLKv8xQVDKKBV0leksXD4HpHOT1qaDPd2aM+ZzWa1SBGic8hDKaKugBAqAoJRRKqgIuHyOTOF28y+YPFLhhve7yzsIG/LI4u0uGV/V+1xJCcoBQSmjgqCHfHb8Oratyf/K9ycjVOR/QcGKNYs69khX8HkagKCUUUFQtCci9G3TxicxfCuz7nuoV8e2gKCUUUNQcRYMQehfN/YeOCsPZYJCv3gT2BG0oO2LwzyXUq6LzVEkKPSLN4UdQVHpgY0il/3tDEWCQr94UxgS1CFQJCj0izcFBKWLIkGhX7wpDiXo/vcS1X5irbKTJEG/+JLUVTytAmjUzE5xJEGX1Fs60+2QSis3oEjQ+9Njt3G/hhrfF74Tz1PXl0rV7BNFgrJ1VaTU+S+EtkWos3IjigQd2iOl+TqEhP2/YBdvIYxdFbnDd5H+Q+VROBQJ6p6LbgfcAEEfR4mgrF0V8f8RocXRKq3cgLLLTHcQSu2tBUEfQ4mgrF0V2ec2sGugym3qFQm62GUq0sZEVBLMBkEthLmrIrc2f02/mak8lJ3F/7KTO3VPf1cwFwS1FOInzZUfoLEIZRzpMhMLgKCUoSlo4Rj9ZTsZD1xwOEBQyigRtJG/HuP70g26Gx8Du1Gol70CglJGiaBZoSnXeQSzV7+uqEb2DQhKGUW7+MX7xOZaLGjeFXkj5bIICEoZK5wkWSioNqFGoI+o8fYECEoZK4wsYqGgG9rkoP96qDwwiGJAUMpYYeAGCwWN28BNOn+vrDqqA4JShh1Bxy7hJk1PKKuO6oCglLHCyCIWCnrC88tfxr0k47HfTAKCUoadkyR0sGezt2/TrYrtAUEpw5CgDgEIShkQlC4gKGVAULqAoJQBQekCQ99QBgSlCwx9QxkQlC50h7756zJPn3oUKmavgKB0oTr0zf0GQTw1A2nUzE4BQelijaFvIKF0gXyaQ+7QNwYgoXSBfJrDwstMkFC6QD7NYamgcXcNXLpCxAX7D7ts3OROVhBUTj5//4PKNpFsNp1iLl+/K4VkPi0U9BsXZz01KzxFhAOEPWXYZGfXk5YljWY+KWwTyZooFVPJWQrJfFooaBkPqhKF5T9PFJbnRBSWU4Mo7K4zUZhuAC08t23yQGKSfL61EhtySfhAehF678SGHAzHF9PC5C6OCV8MwRdjDhAUBDUPCCoABAVBBYCgIKh5QFABICgIKgAEBUHN4wCCls6mGVYyh2ZY8VyiMM2HNMMUQpKoXfjLXPmL8cVsuIIN+Wc1vpjld7Ah5zfjizGHUkEBwKqAoADTgKAA04CgANOAoADTgKAA04CgANOAoADTgKAA0ygUdHO9wIW4mCUBHnH3SSLnxxIUeLS5e1wxPmyBt+fYUlxYViNkXKNUpC6MeCsUgS2fqBr4RBJkEZ9BgvQpT50yQW+5Xcuph7kX+0utrJweSQSRP1WPxRdY6HeqsHMqNuwXj6z8sHRM2JJQf+MmSEXqwoi3QhH47SKpBj6RBFnEZ5AgfRRSp0zQ1KEIJU2VjtnJLU+JxUfmtUiOxRe4fSBCBfexYad98x623YYJ25fib9wEqUhdGOlWKANbPkk1CBJJkEV8BgnSRyF1ygSdPQWhNSOxYbebb8NHxqWlxeILXNg7zG1gLr60hOeq99biwq77GzdBMpIPI90KZZCUj60GQSJJsojPIEH6lKdOmaCz+LXGYYK0awPW4iO/GIG4vGLDpte/kt9vKjbsUNjx89024ML49OljJCN1WSbbCoXgy8dXgySRBFkkyCBB+pSnTpmg/Bpnvi8dUxLb6wZBZJSHf+2qPbBhK8YgtDEKGzZpFrcf648L49Onj5GM5MMIt0Ih2PIJqkGSSIIsEmSQIH3KU6dM0JtetwpCMEe+2zprCSO5Lz427Eq9y3mRydiw9U1v5w5LwoXx6dPHSEbyYeRboQRs+WTVwCaSIIsEGSRIn/LUKbzMlBYagrt28F6lypUrDyWJ5PKKD0sN8Hq9CBtWOsnLc8QDXJhuB6SPkYrkw8i3QhG48smqgU8kPosEGSRIn/LUwYV6gGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaUBQgGlAUIBpQFCAaRgV9FLnXh+rXQeABRgVdPIh1E7tOgAswKigeaV/RaldB4AF1BDU+zwqnR9au91e7nVlZ5eanku5etRycXF57VHMzsi/pQvJEBFYbB5g36gk6BsRV0sO+qVzgmYhdPKpK6hC/hMheyeWYgrBCqod5+H5rq4U/dCp+mFUdSTvU1B7wKaoI+ivbnnc7wNBJTpBkd//hIKO6tk/9rG3pVn6n0fv8IJ+3Sg7p+HXyDjGqn4Y1UutQuej29EUNwawLuoIuly3M9d6XuUF1f639oNHgm72+lkTtNcY+lFAcEJJZt+mifyPdnZgwHjdO6STUbew63aEwg7qXz8h6KnDSNN9NzKOsaofRjXhe9T6wbtnbLet6kNyQCUgJfHxd+oeOKkj6IRZuhetD6DKru41K3xjSNkibt6qk+NSX9YaIn8IvZkfvTCz6nnE/+xpklPYeR3/CvFp0y/k9PsjsFT/WpdK/wo8xdyrec/05QsqGzr1dvNtl1o3m/f7mypss3oQHFA9YmsIPwVBl4zWvQg8x6esZIMvepSyIjQ1+Lgxcpp/eHjIoMyOCPE/E5cg9OVw/hXi06ZfeM/t4dwZhkBhKk+EpqKyUWF1w6jyDL1h7Q1kCswB1aMjJ56ci/wUBD3qW8j9PuGh0aWstHrO4ymbMajsZfJMhEo0mZGcoNzPBG7ntGUo/wrxadMvRL33NrlsCHwilV9mIPQx/49TP3SqfhhVjsPTtZMbd/zT+pvJCJIHVLrjJd3hUV6Uh/8OLs2at72bvpq4hztMT0zRJni4DirKiNIvUweVzuKj+/2rPV5vnX6ng3zOPyZoge/nZZEn6mdr+i03Cvp187yirmvKBNUvRJvCOxoDn9jFfxquudfn49LzGv3QqfphVLm/U1Te9QiUPtOmW6wmkgdU/PGS/vBoZf/SI8O5NK/qVJBd3yDoufACTYuDGVH6ZeqgkqCapPpurb5CBkHDl6EKru7u7i35pfOi33kUuqKud3zZf1DtjKDA94rLBNUvRPlV1hoDn/gPqhkZ4DVGU1jhqn7oVMMwqmjzZwhNahb+j602VnUkD6j44yX94dEpn8kHtVyaB3OHqh8aBEVZe+e47MmI0i9TB+buJGW7XOj72wq1a+FASB5Q8d92w6HSvdV92mgzI2O2IbRAJ+iYlKMNFmZGcYLql6lTfeYETRiONlfbonYtHAjJAypeUP3h0awEbXa1u5mRqyMKc0MTMxpqsgNS5o5FN/x3ZUTpl6lTfeYEfaDhvuRqV8KRkDyg0h0v6Q6PrrXy9F/GnySN8Ws2d1FRXN0eM7ZcadbwlQVhGVH6ZerAnKAA8DggKMA0ICjANCAowDQgKMA0ICjANCAowDQWCno+sTzzL92/AeRTIjMWCrr6pXnll3p78QmCfJIjmU9LBX3dss85BN2sICjk0wxYQY9PHRk3xeQ532UJLTxZbGm97BYQlC6KBF1QL2nNmpkhiwSzjQkd+lSliu8qqJtdAoLSRZGgAbn8tCBEMNuQ0NSnDx3aVrG8deIFQemiSNA6ur4RtxoLZhsSGtFOW39Tw6GW180uAUHpokjQNPehU6bEeX0pmG1IaN/G6FdXrzFKameHgKB0UXaSdCt11uw1t4RzDQnNrBi/p9lT5y2vm10CgtLFmmfxX7g/69NmpFr9VVQCBKWLNc/ieQrafCD60b3vL8oSXWDvgKB0oXoWXxA7kKdBo8dibtddKfLJyaEfvuHpkN3PFQmKua5cHqF6Fq/9agtPh6DHgy6L9FjJcrmH0GyHPH9SIijBHqncYY2z+GH1n3j7Z925/K+LSZMOG2cde4mbfNdVRj3tBiWCSl9XluKB5StlHGucxQsERf82fv0h+tFtSnKgcW+fU+sGQuMnyKmovaBEUOnryuaZXb3qi6csXy3TKBNUHKGgKL9Ph9tdtyB0pZbxlP5j77d6vHDHsuLZRomg0teVzfJ5ixvaDYFFlq+XZWwiKCqd7u196cJJTe0yJ8+uTHfMjCo6SRLske43DOKpESj9qTwKOWcAACAASURBVOjN3OTF49JB9ooiQRv56xHMNhUUoe+fqxnYxMdDXuXsEaqXmf66zNOnnnTUsPXcJPQ0xfUyhCJBs0JTrvMIZosJivo8/XzX6ph/BY6AFa6DiubzMXYHH/l3ZlMHbdmobBe/WLStkmhCXzg5sdbAGiqN4WNDlAgqY4/0BJ839oj+y/LVMo1tjkE52h1Aee8+Ncdxr4cYUCKonD1SecFmgm4OStvd/rVor+WOeW5UhqJdvIw9UnnBZoKib/p1/6wEnXzFe+l9y4q1D1Q4BnVobCeokV8G1p6MeVCcPQOC0sX2giJ0+Z1a/b931FZ4IChd1BAUofxPmwbPcczzThCULuoIitC3ca1rdUxxwJudIChdVBJ0YpP5oz1WR9fottqkqYmdA4LSRR1Bs1xyEJozGj3YMrhm23lnLVsNm4CgdFFH0MfagxbtHRPg/8a2e5atij1AULqoI2huresIjZtkfHt+SXenlpP3OcT1URCULtbo1UmQ0BVer3dp8PiN+aKD09s/33rizizt+ZMa7McZBgSlizV6dZIk9HzKjofCeQUZs7o5VXHy8TmJ/zyzgKB0scbYTEoSOnLkqlE+FduMXXfGTpuPgaB0scbYTEoSWvcSQtoauxcNeaHqi68vP5xjeUkqAYLSxQa9OmXR5geEsquXcK/yf1wZ3+p5/8jEDT8XWF6ezQFB6UK1V+f9+ro+NE7ultfny+C0b9qVnd4j7eWv5sY0rRLUc/zqgzctL9WGgKB0USRoafp27fr+SY8aIf+t60PT9wUFFUoNrTe1RDCv5NKuBa+2rV2jRcz0jUdvKyjcBoCgdFEk6Niwtr27bx82UjBbSUKPu7431Wed+LLsoxs/iHmxVvVmAyZ+uvd3Rhs+g6B0USSoR/7D6hdQrpdgtpKEdktD6IKrZMjdnzYnx3epU9mr7ZD3P/32bJ7lK7MGIChdFAnqnZVX+Sd0y0cwW0lCg65wk1rZBJGl1w9vnBvfvWG1mo0i35i1bv+5fMvXShEQlC7KLtR7+I4PS+6QKJitJKG9UhD62VvWR7JPf/3ptLjODao5Neg0LHHZlsOXLT/rL00dHH/C4k/zKBE0nVv/wJj1pYLZIKg5sGfxJ8+gQ5OoJvQ3j9g33L6y7LO5v32/PvmdAW2DqlSv3yHm3eS13/78t8ntKmnGtk/7yOMwPs6EssYuSgSthJKbbUjvPkUwGwQ1hxUbLJsle+2nVxR8XE/uuYMjXav4d2ru/axz/fb9Rid9svng2ZvCiwOm3K/BHdF+3kf2+la51AzYo3+pTNCwawgV1jG+fxCjG2/VX3iQX55gTlBKbA45dWdGmBahrHOHtnySNLp/h4ZuFV1eaBs1KnHh2l0/nr8t/Lev43LQyj5xa16Su7IjgTvT09xv6F4rEbSiNuYP7iSw7DKy9lv9eKvBlhdp9ziqoP3TuUnd35+Yp7197vCOlLkJw19p/ULtp2rVbdkjdmzSso3f/Hj+lqEBVWm1sB0rqw2Qu7IpofX6e4V+oXutRNBgj4BO6EyT8YLZLORTNbCCGh86K2yyJAELCY36zwdvpzf8zXyA9s7vmd9uXJY0NrZnqxdcn37eJ7RD1IgxzzqF+Pu0vyaz+fQgv2J0r+pS3WtFZ/GFv+5Hx7YKO7yykE/VwAqakpLyaoPk+Y0+JC+ThYQmPzN6mV8t0f24KHl/nT64I3Wq8xsRHZpW9atewTmgSYdeQ8dMmb8qbfeRM9fuSvaSHun01d8rn/9E9xouM9GFZBff9C5COc3Iy2Qhoa/3cKkSXlPug0S0PhlI+ya3i9XevfLLwZ0bPpkzKT66R5tGfs4VnLzqvxjRf8TYKfM+3bhj/4kLfz/W0GpZz3CfqCbf616DoHQhEdSrgNv5+JOXyUJCu+xDGtTCpLE/jgyvti90EL03lfv3uWP7t6Yum5P4RmxU57B6XjUqOHnWCesYOSh+3PhaLycMaK+/SACCyuPWJ8mS7dNJBB3dKX1bxFjydbKQ0ESuvn+4yO/ldP/IGeIxT3JvXDzxw9ebVy2e/c6LTT4u1M/ECRplhLxOLOTTWpxzfz3RZ7VEAImgmlWDY1bL6CjEQkJzmrQd4r5ehRXjBM0wQl4kC/m0FgM/ReiKs8QVahJBSxYO0iyX0QODiYQW7/tClQeFEeziiw7u2bMrjLxIJvJpJRqf4SZ+En8pEkHHRYeU9HuDfKWOnFAsBIKOauES5beQvEhHzmf0JwhdlLraQiJoUH440gibLEngyAnFQiBosGbO0b97kRfpyPm86Bn3npfUoRiJoH63wtEd0SdNWNwv3nEhENS9YM8i1JC8SDvKZ8mmDzbKG9Yge/VSidspZIIubew/o77YI2EV9It3WAgEfS88q/7Y5uRFiuUz953gJsuZG2O1JCJiVvd2VAfeILoXf2Ba8s9iy63QL97uIRBUew4dnXuVvEixfA4Ycelo88/Iy7ANX7fjvjNdhL18JfmkceBoqc7lJIJmmDuGtUK/eLuHQNAUni3kRYrk834N7t/Uf9vKqZgtWJzATabPkvGJ1c2PXx4l1b6RRNCwwBniTTSt0C/e7iEQNDExcUzATPIiRfJ5pzY3OSnj9rNt2NuiBJW23y7jE+EZCBXXzDUfQLSLvzS3SUfRnpbCpx3n3uWJVtLt2N4hvNV5pyd5kWJf+NYLtfl9PyAvwzZoB7QY1/IV8iY6CL3EP2LUTaIrOVl70Id7w6uILNesyc+dEpVc1gH4vo8zz7NuMmroaBAKqqlLXqSYoFc6VK/2msz+LLbguyW7ZZ26zeyVp13YUiKARNDtQ916bxLrMzmsc8GAfhsj44WzYRcvSTRHgzjyIsXzmcugnvLRvFnNqaNUHx8SQbummmnA616IXPNRHs1ux3YPgaB7ODJkjDvh2PnUSHcXJxA0S9ituIxmp1Hzq+iWMH+OnVAMOEHttIeCahAIWtL0vJnlR/0G9mowpu5awWxIqAR22kNBNUh28X2qdYyMjBQLKNy+8IMVJkcQkFBp7LGHgmqQCJqpg7xMSKg09thDQTUctj2oWhAIar6Hgt03vin9as42OZdB8UB7UMoQCGq2h4LdN74pjWw7tUNn/AguMoD2oJTBChpyMVKHyCJh45uiaboT/pAa3GQ/QhcSH/u9+8T584ki81X9PcBr0n7ty8tolttCUXtQcUBQKbbmmD2mFza+Kf5oHk9zF25yFKGNtSq7juZ+/8m9fdsjLLBlkn4+/56N35Htud/TE2iW20pJe1AzgKBYtHfF5ko3vjldqe/qTs/8rZ9XbycqHTva8mpah90tS5G241aaRSpqD2oGEFSa7cO1bap9LLZE2PhGjyGfg/hG+F7v6V7f9OAmvzZSUE+roO3Vemr7LjY/BkXmvvFmAEGl8TuS0edOIHmRhnx26sBNQgbrXhc55XH/rzpZUkGrot01d4ftz+LNf+PFAUGl8ShNXK2VMeanIZ8rnvkVHa60Sz/v7U67P/ffKf254m/W/Eq+FkYh6jRn2Te+fEIgaK/+vjnj2pAXacxnz6eeq2hsBFW8vPvAfdIfu9+iwyjfZPLVsAmJoBZ+48snBILmrjyFZskYVaIsn3/vMDk+lSB5OEJZbn/L+ASLkAhq6Te+XEIgqI3uzMWkcZMeeyz6LDuQCGrxN748QiCoje7MTZ2IUKHvRYs+yw4kgt6b2LL9rAdiAXZ/75g+BILa6M7cTd83lrYaZdFHGYJE0P4xx38aMlxkud3fO7YCJJeZbHRnLnvhuG2KxnYoXtm93zdKCqAA0QC23PFSsb/Ichi4wRQCQe3mzlxC+O5NQZtVWrkBEkE73EDoeiuR5TBwgykktzrt5M5ccfV7CH3fTp2VGyF5ysdg9/h41/dFlsPADaZgBb3yvwKEcs4vIy8Sm8+lns++bK5bjgLuOXOT31Ue5AAr6KcG0sQCBPeOtd/Ag6dwgi59ys/1W9/qPh3Ii8QJuiX0oubTulSH7NLTOA1p3xN2KrcxJJ3mrhw4J7H1PR69hEf34QX1PIkOVtgo6341TtCYTdwkTNkzcEU5Fdw0sL2cVhhWAC/o1kDP1gEeomOMxvJUjY0VzIZdvAQ1uR1NTXntfXD5jP2cmzSVfFaGhWhO/q72EI9YQXf7/8BNf6on1jBhQfXktLTaacK9PwgqgQv34y4dIgSXz6/qn8pf3NAKu3gWwAra5r+6l8daiwUcb5GG/E3mgqASOGdlZblyPzKeMYbN52dBz0f+QV6eXYEVtLZ+d1Qq/q3PixvhYTITBJWgshHyIiGf5uAFDbmme/mPuTHVvzB9IhUklC6QT3PwgiZ34Z8MWNhX2YCr5QcQlC5YQUvj3V6bNdo32s6eNKcaSgRNR6WpA2PWC69BQT7Nob8Oemb5hCVH5ZQJCbWQSii52Yb07lMEsyGf5qggsUwCSKiFVEJh3BF/YR3BbBXzmVOo2qoNgKCUUSJoRW3MHwjdFV4wUS2f18JrVB1WoNLKDYCglFEiaLBHQCd0psl44/uStat4WsnosEiVDvO0DwZPVGnlBkBQyig6iy/8dT86trXs7mLh2/E8dWW0vqdJXg2uJmeFrX1tDAhKGaWXmS6ZzlIrn0VO3BHo0ZfUWbkREJQySgUVeY68pfnU7Fj5i6K6DB184WiL5YqKUAwIShl2BM1v2vlN/9lK6pI/oV6zT1RuzgSCUkapoKtMZ1mYzw9HIZTt/pey6qgOCEoZdm516gdu2E23LjYHBKUMO4J+kIDQAx97b4anTFAYuMEEdgS9HTAyucWblOticxQJCgM3mMKOoChn2eRddGuiAooEhYEbTGFIUIdAkaAwcIMpIChdFAkKAzeYAoLSRdlJkmDghodJ+uf6+NGomZ0CgtJFkaD3p8du434NNb7XLNY/18efRs3sFBCULooEHdojpfk6hIQdFCGhdIF8mgMnqHsuuh1wAwR9HBCULsouM91BKLW3FgR9DBCULooEXewyFWljIioJZkNC6QL5NAf2LP6XnQiVpL8rmAsJpQvk0xzQWEQ+igSFtg0mgKCUUSIotG0wBQSljBJBoW2DKSAoZZQICm0bTAFBKaNEUGjbYAoIShlFJ0mCtg0GIJ/mAEHlA5eZ6AKCUoaqoEf280QIRxMrT4CglFEiaCN/Pcb3D3pE8PjLeK6nwwGCUkaJoFmhKdd5BLNXv66oRvYNCEoZRbv4xfvE5oKg5gBB5WOFkyQQ1BwgqHysMLodCGoOEFQ+Vhg8DAQ1BwgqHxCULiAoZawwuh0Iag4Ym0k+cJJEFxibiTIgKF2ojs30oKfuzoenF42a2SkgKF2ojs2kPaS7dzymD42a2SkgKF2sMTYTJJQukE9zyB2byQAklC6QT3NYeJkJEkoXyKc5LBW0SaKeScGtiAgkimrJclgDwyYnBlpBUDn5bNgEG/ISQTH1mmFDwurii6nTAhvSrH2iFJL5tFDQP+cZmF2pBwldCMOeJgqLIAx7hiisM2FYZeM2z79rWdJo5dMvBBvSoSq+GLfm2JCXauGLqdEGG9Kk7jwpJPNpoaBlPKhKFJb/PFFYnhNRWE4NorC7zkRhd1yIwm67EoUphCSfb63EhlwiaKHfeyc25GA4vpgWJndxTPhiCL4Yc4CgIKh5QFABICgIKgAEBUHNA4IKAEFBUAFKBS2qSRRWQObKg1pEYfdrE4XluRGF5XgQhd31JApTCEk+x6ZgQ64SNObph3/G55HO+GJa4Z8HviUOX4w5lAqKsiGMKgRryX9IpZh7JdgQLcEFNYI1FefiY8yhWFAAsCYgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0CgXdXC9wIS5mSYBH3H2SyPmxBAUebe4eV4wPW+DtObYUF5bVCBnXKBWpCyPeCkVgyyeqBj6RBFnEZ5AgfcpTp0zQW27Xcuph7sX+Uisrp0cSQeRP1WPxBRb6nSrsnIoN+8UjKz8sHRO2JNTfuAlSkbow4q1QBH67SKqBTyRBFvEZJEgfhdQpEzR1KEJJU6VjdnLLU2LxkXktkmPxBW4fiFDBfWzYad+8h223YcL2pfgbN0EqUhdGuhXKwJZPUg2CRBJkEZ9BgvRRSJ0yQWdPQWjNSGzY7ebb8JFxaWmx+AIX9g5zG5iLLy3hueq9tbiw6/7GTZCM5MNIt0IZJOVjq0GQSJIs4jNIkD7lqVMm6Cx+rbimKtq1AWvxkV+MQFxesWHT61/J7zcVG3Yo7Pj5bhtwYXz69DGSkbosk22FQvDl46tBkkiCLBJkkCB9ylOnTFB+jTPfl44pie11gyAyysO/dtUe2LAVYxDaGIUNmzSL24/1x4Xx6dPHSEbyYYRboRBs+QTVIEkkQRYJMkiQPuWpUyboTa9bBSGYI99tnbWEkdwXHxt2pd7lvMhkbNj6prdzhyXhwvj06WMkI/kw8q1QArZ8smpgE0mQRYIMEqRPeeoUXmZKCw3BXTt4r1LlypWHkkRyecWHpQZ4vV6EDSud5OU54gEuTLcD0sdIRfJh5FuhCFz5ZNXAJxKfRYIMEqRPeergQj3ANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0ICjANMwKeqlzr4/VrgOgPswKOvkQaqd2HQD1sbmgxRVq1XIblsutuZaLi8siVDo/tHY7/on2lWq5OLW7WBaXV/pXlHRJGSLLxeYB9owKghai3De6c2vO171/I+JqyUG/dE7QfHQv9pVHgTsj/5YuCS+ofuRUHt1AqvohWXUk77N4AwCbooqgqMjtN4Ogv7rlcdMDQSW8oGhPYFnc3omlj3+sNEv/8+gdXlD9yKn8K/1AqrohWS+1Cp2PbkfT2yDAqqgjKIpKMwi6/DV+qvW8yguaN6wH92az18+aoL2jevaP5Rd9FBCcUJLZt2ki/6OdHRgwXvcO6WTULey6HaGwg/rXTwiqHzmVf6UbSFU/JGvC96j1g3fP2HSb1YP0gEpISuLj79Q8cFJJ0NeW6lIWiCbM0s1tfQBVcnWv2uIC93rVyXGpL2sN4T+E3syPXphZ9Tzif/Y0ySnsvI5/hfi06RemDkV/BJbqX+tS6V+Bp5iPud18m64Y3ThXuiFZL7VuNu/3N227zepBfEBVxtYQflruBY3aZEjZktG6uYHndLt4HUVoavBxY/g0//DwkEGZHRHifyYuQejL4fwrxKdNv/Ce28O5MwyBT6ZSN3KqDl5Q/ZCs/LuhN6y5hSxBckD16MiJJ0f3b7W8C/rQ/awhZUd9eV9PeGgeCYrQjEFlL5NnIlSiyYzkBOV+JixFaMtQ/hXi06ZfiHrvbXLZEPhEKvUjp+rgBdUPycq9OTxdO7lxxz+tu51sgD+g0h0v6Q6P8qI8/Hdwada87d301cQ93GF6Yoo2wcN1UFFGlH6ZGqgi6P0xXct2OtH9/tUer7cOPSZoge/nZeEn6mdr+i03Cvp187yirmvKBNUvRJvCOxoDn9jF60dOLT2v0QuqH5KV+/tE5V2PQOkzbbjRqoE/oOKPl/SHRyv7lx4ZzqV5VaeC7PoGQc+FF2haHMyI0i9TAxUEdXNzH5JTJqgmqb5bq6/Q44LOi37nUfyKut7xZf9BtTOCAt8rLhNUvxDlV1lrDHziP6h+5NTCClf1ghqGZEWbP+P+mzYL/8faW8oC+AMq/nhJf3h0ymfyQS2X5sHcIeqHBkFR1t45LnsyovTL1IDBO0nZLhf6/rZC7Vo4BvgDKv7bbjhUure6TxttZmQMd165QCfomJSjDRZmRnGC6pepsQUMCpowHG2utkXtWjgG+AMqXlD94dGsBG12tbuZkasjCnNDEzMaarIDUuaORTf8d2VE6ZepsQUMCvqAO2YsxYcBBOAPqHTHS7rDo2utPP2X8SdJY/yazV1UFFe3x4wtV5o1fGVBWEaUfpkaMCgoADwCBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYxkJBt1QoxzyVSfdvAPmUyIyFgq5+3bLPOQTd9lIvEvJpDhBUPiAoXawp6A7fKsE/WFaE/QKC0sWKgp6oGPNlz6evWVaG3QKC0sWKgg4I0dbd5zPWsjLsFhCULlYUtHN7lOnhGWNZGXYLCEoXKwq6+pkTKKXCMHU6A6gGCEoXa54k9X3q2Ypx7QcVWFaKnaJI0ONTR8ZN+Uk4FwQ1h9LLTPf25aPCmJZiwyhlzkvJs6x0xlEi6IJ6SWvWzAxZJJgNgppDrqBF0xJ52r30+EztR54HTCLn1kkc7OeQXSeVCBqQy08LQgSzQVBzyBW0+KN5PM0Dnpz9ncdiwYHo3Vq3EZr2rszi7QIlgtbRjRdxq7FgNghqDgt38cPqC2b82SrqLjo9YXTZuo615Cb7ulpWPNsoETTNfeiUKXFeXwpm4wUtybV8pYxjE0HRwwT/j9znflLfeHSl+w86Ff6DCrmVOmv2mltlbzWLdXuk7rjRpKc5VW98QsFqWcY2giK0p/KAh+h6TWOP4Q+DJw/2/9ey4tmG5mWmh0m6Y/oQP+mwda1uoy3+hfTWyxK2EhQFdgxIvuFyx/g2c95/8kWi7B8rXAcVzedjDOIHsnjpGPX1MoHNBI2s2fS559wtK9CeUCJoI389gtk4QYev5SYhDjrsrs0E7enSucezTx+xrEQ7QomgWaEp13kEs3GC7gs8cG1KixLL18syNhO0ztk96dlOXqOyRJY5Eop28YtFH9+AExRtfdE/zlGH3bWZoF22InS1Vu44t0+KLSvVTlDhGNShsZmgR9w+WBC0HKGzEY3o/wkZAgSli80ERb9Pm3BI92Jng4ifLSvYHgBB6WI7QR9R/Jl3/98sK5p9QFC6qCEoQgUL3aJ/taxw1gFB6aKOoAjlz/HoS78HOQOAoHRRSdDTYc8/3y2www7HGywZBKWLOoIWB29Et9us2twqeLEqY5tbERCULuoIeoZv8rgzCqGjQ51HOdaeHgSlizqCXqzDTTbpnip8a0G9RotvWrYiFgFB6aJMUPFOXviEalsl/nWozteGN4dG1Oz5xQPcZ+wEEJQuigQ108mLIKH/xvmGffHo7f3Pe9SITucdLZ0T7POmPbcPB0HpokhQM528LEronZQu1futvzO/w/m/RkRb8HlWAEHpokhQM528LE1o9vr+NZ8fc1Jb5FRkWQEsAILSRZGgZjp5KUhokf+wF9yHVDlvcQGqA4LSRdlJkqCTV8GwgTz+3pbXZ0rfnMs9Pd2DR63/w/JC1AQEpYsiQUvTt2vX908qOwEv3bGFp0OQ5fUpGvlc5c7/as+uiPb26LfwsP2d24OgdFEk6Niwtr27bx82UjBbSULPeUaPctupe3lt0zstqzUe+cmPdtW7DgSliyJBPfIfVr+Acr0Es5UktPdqhE74lL3VnPgs/sWqdQfMTP/dTvrcgKB0USSod1Ze5Z/QLR/BbCUJDbrCTWplPzGv+Lcvp/QJrhI6YMr6zDviH2MHJYKmo9LUgTHrhS1oQFBzYC/Ue/iOD0vukCiYrSShXb9E6HdXsTFFC099OTPmxZrOLQZNXvXdBSsNU6D5+JUh/1VUghJBK6HkZhvSu08RzAZBzYE9iz95Bh2aRPUbf8w1Icl3rURA1rFNc1+LqFvZPaz32A/Xf/+b2dZQWkvGJX2t69drvL+X/7mc34xrUyZo2DXum1jH+F6zSD8Ym7/lRdo9ajVYNsvV5CSiYYb+Pb7j48nDOjWs+Zx/q96vTl26cc+Ja4+dTWmnOlVtJbtfSV7NBwht7i33Y2imc8PaG/UvlQhaURvzB0J3y4a30MwiGvrGoWFOUNkUXsvcuXr2O0O6NfOr9qxHw7avDBv7waKUNxruOTs/WO6J1ZWAcSGtZ7SUW4XvGq6c+anHFd1rJYIGewR0QmeajBfMhl28OXhBEw0IW4RIoF5Ci/45e3jX+mUzE171avJSA59KFaq5BjVrE9FvSPy4xHmLVq3dsmv/4RNnLv959664utoq3c5+X/MVuasd7/3K9JcC1uteKzqLL/x1Pzq2VXgEDoKagxc0JSXl1QbJ8xt9SF4mCwkdMDmiWULgpfxbl0/+uD/981WL5yUmxA8f2CuibVijID9n54oVnnN29gsKDgt7KSIiYuDAgaPi499MfLeyk5+LZ51PVnFs5u85fLWf58AJPRcv6+EEN5LDr6xfXW53/PwC3YrhMhNdSHbxTbkTkZxm5GWykNAZzyz4rkkNqSeMFN69e+3yHydOHOMM5FRcs2rVp/Perzlz2GuxPm/Hcwzi79pGRfB0CtNTN0gPJ7iRGoZnnjb8oManumJBULqQCOrFnaEW+pOXyUJCY+JDvWK9r8r9WMgadL/3HLmf+s/LMS3j6+q7roCgdCERdHSn9G0RMp4Yx0JCe37LTZqckvux35q5Ob2mkfuph206T2o6XP8aJ2iUEfLiWcinapAIqlk1OGa1jL8aCwld2v0+2uknWzWEblnSPqU4ff5+w0ucoBlGyEtnIZ+qQSJoycJBmuUyxqRjIaElr9fwr3NUhRUT7OKLDu7ZsyuMvEgW8qkaJIKOiw4p6fcGeZlsJDT3mioPYSQQdFQLlyi/heRFspFPlSARNCg/HGmELUIkgIRKE6yZc/TvXuRFQj7NYRDU71Y4ulOPvExIqDTuBXsWoYbkRdpTPvd9tIfujotE0KWN/WfUXykWYGm/eAeGQND3wrPqj21OXqT95FM7sPm7L75CdcAtonvxB6Yliw45a3m/eMeFQFDtOXR07lXyIu0nn9+1KEGl7bbTLJJE0AxzXwma/eIdBQJBU3i2kBdpP/lcnMBNps+iWSSJoGGBM66ILqfcL94hIBA0MTFxTMBM8iLtJ59ft+MOQLsIu6ErgmgXf2luk47rRJbT7xdv/xDe6rzTk7xI+8lnSUSX2T3aybo9curtUVukTqvI2oM+O+cUywAADIhJREFU3BteRSxA0C/+QQ9d2wpPTzk1dDAIBdXUJS/SfgRFJZumbZT1mKH/ui9c02yaRACJoNuHuvXeJNb3V7MmP3dKVPKjgWr+q2ud1qWOSGx5gUDQaI4GceRF2pGgsonczO1NnB6aDyARtGvqPfHlwzoXDOi3MTJeONuBE4qFQNA9HBkyhp9y5Hw2Oc1NfP8yH0AgaJaw12YZ7oXINR/l0ex2bPfgBKXUQ6F03cCRjvDg09emInTER+IglEDQkqbmxvJqdho1v4puCfMHgkpAqYfChDabV3o7wCP7bjVq1dN9v0QAyS6+T7WOkZGRIsuP+g3s1WBM3bWC2SCoNBR6KDx04o66dnaR/txnvs+0Z/5pVMUHv82WWk4iaKYOsYDC7Qs/WGFyjRQElYZCD4V/+QslZ4V3SJ7k2xd+06QGWjI6gE0plPTTYduDqgaBoOZ7KJC3bQj6DqGJwrPTJ3kthZu0O4ytDk1Kt8/aIquvd9HIarVaXZIIcNz2oCpBIKjZHgoy2jYc9urQqIX0QFWvr+YmbW16JlXas/0HL78s50Lo9H4PtMtaSARAe1DKYAUNuRipQ2SRsG3Dw+n6kUVqcpMfELqY+Njv3QdPXEgUmf/o93f+I8Z3dd5rdjnB7/HDRh6QEz/Ae9IP2k7LZKzH81Pud7Wx5uNehPagdMEKujXH7DG9sG2DZol+bKZa3OR/CL37dIVnenO/r87Tv8f9XuDyTN0EGfEmv9+u6evWIEvG5yLbc7+nj5OxnqDlCF2qMtN8XEtF7UHFAUGxaEXHPJNu2/BNxY9zkyoSDVxFiZAdCCWOkvGBPS+VIm14uoxPpDb+8cKwARIBitqDmgEElWb7cG2bah+LLRG0bTBgyGfkS9ykzqsK6yeDu87c5KKMXSfSRrWa3LarrLOkVWH1EqSei0U4eJj4N94MIKg0fkcy+twJJC/SKCg/qFldOf/QFFJSPRuh7zrI+Yj263k76T7BmqixiNlvvDggqDQepYmrtcJx0yUo28Uvz59h01385Nbb1/rtsOEKRSDqNGfZN758QiBor/6+OePakBdpzGdS5QpVl1tYLYsoXdN3yAFbrlAEEkEt/MaXTwgEzV15Cs36k7xIyKc5DIJa+o0vlxAISufO3L855CXYMSSCwjdeBgSC0rgzd6GZR41o5u+zU4BE0HsTW7afJTqmFvSLN4FAUBp35pp/hgqHTJJRL3uFRND+Mcd/GjJcZDn0izeF5DKT8jtzWa7c5FRTGfWyV4gGsOWOl4r9RZZDv3hTCASlcGfuQfUihDLay6mYnUIiaIcbCF1vJbIc+sWbQnKrk8KduaEDTx4IEesK7miQPOVjsHt8vOv7IsuhX7wpWEGv/I87t8k5v4y8SLF8PpjStO16WRWzU7CCfmogTSxAcO/4fkPdMwac3MViywk4QZc+5ef6rW91Hxl3ELFf+AfrF1llrF7tF4NH2bbBsykkneauHDgnMVhEj8de/6V7SksfOe0LHA2coJ4n0cEKG2Xdr8YJmhXcf3wQ1QGRDHzQYtNKn2+sULAM8IJuDfRsHeAhujuJ5akaGyuYDbt4CWpy/5hqynsCHi6fiRMQulP7pqwySSipfhuh3eHUy5UFVtDd/j9w05/q7RRZvqB6clpa7TTh3h8ElcCF+5F5CITLZ69d3CT8oLxCCbhbi5tclDFGjzXACtpG/3TqY63FAo63SEP+JnNBUAmcs7KyXLmfLPIicfmcmMjJ5PoveYGk1Od27x8Mp1+uHLCC1tbvjkrFv/V5cSM8TGaCoBJUNkJeJC6ftwIGT6v3gciCO2dlDLAjQqZ3x6ZN6B86yAIraMg13ct/zI2p/oXpE6lAULpg85m3eo7IybZ2TK0Qz28Vrfn+D8fkPjCaNlhBk7vkcdPCvg454KoVYOdRiGs75KNj7mbGfbMbsIKWxru9Nmu0b7SdPWlONZQImo5KUwfGrBdeg7Iwn8M2cpMIqXGP7AGC66Bnlk9YIus6MAhqIZVQcrMN6d2nCGZbmM93F3G7+cZy7qmyCGGnOVmAoBZSCYVxR/yFwvF/LcznLx7rM0e3UfsYUikgKGWUCFpRG/MHQneFF0wszef/+rYaLz0ylx0AglJGiaDBHgGd0Jkm443vH36gH/rGl0bF7BQQlDKKzuILf92Pjm0tG2+4eKl+6Bt/5dWyW0BQyii9zCQyFCHk0xwgqHyUCiryHHnIpzlAUPmAoHQBQSmjVNBVprMgn+YAQeXDzq1Ox0CZoNAv3gQQlC6KBIV+8aaAoHRRJCj0izcFBKWLIkGhX7wpIChdFAkK/eJNAUHpouwkSdgv3tOZ51lXGjWzU0BQuigS9P702G3cr6FlM/Lu8kRDQqkCgpoDJ+jQHinN1yEk7P8FCaUL5NMcOEHdc9HtgBsg6OOAoHRRdpnpDkKpvbUg6GOAoHRRJOhil6lIGxNRSTAbEkoXyKc5sGfxv+xEqCT9XcFcSChdIJ/mgMYi8gFB6QKCUkaRoND4xgQQlDJKBIXGN6aAoJRRIig0vjEFBKWMEkGh8Y0pIChllAgKjW9MAUEpo+gkSdD4xgDk0xwgqHzgMhNdQFDK0BS0aCoMfQOCUkaJoI389RjfF3+sG/qmx8sU6mWvgKCUUSJoVmjKdR7B7NWvK6qRfQOCUkbRLn7xPrG5IKg5QFD5WOEkCQQ1BwgqHyuMbgeCmgNGFpGPFQYPA0HNASOLyAcEpQvdkUXyoVenFUa3A0HNIXdkEUO/+MreNGpmp8BJEl2sMbIIJJQukE9zyB1ZxAAklC6QT3NYeJkJEkoXyKc5LBU07q6BG1fKCzeNm9zJCoI6dj6z70ohmU8LBd3hbKTCU0Q4QNhTZdt83LKk0cwnhW0iWROlYio5SyKVTwsFLeNBVaKw/OeJwvKciMJyahCF3XUmCrvjQhR22yYD+pHk862V2JBLwoeBitB7JzbkYDi+mBYmd3FM+GIIvhhzgKAgqHlAUAEgKAgqAAQFQc0DggoAQUFQASAoCGoeBxC0dCJRWMkkmmHFiURhmslEYQ/fpxmmEJJ8bjmKDcmbgS/ms4vYkOtL8cXMN7nNaMKZdfhizKFUUACwKiAowDQgKMA0ICjANCAowDQgKMA0ICjANCAowDQgKMA0CgXdXC9wIS5mSYBH3H2SyPmxBAUebe4eV4wPW+DtObYUF5bVCBnXKBWpCyPeCkVgyyeqBj6RBFnEZ5AgfcpTp0zQW27Xcuph7sX+Uisrp0cSQeRP1WPxBRb6nSrsnIoN+8UjKz8sHRO2JNTfuAlSkbow4q1QBH67SKqBTyRBFvEZJEgfhdQpEzR1KEJJU6VjdnLLU2LxkXktkmPxBW4fiFDBfWzYad+8h223YcL2pfgbN0EqUhdGuhXKwJZPUg2CRBJkEZ9BgvRRSJ0yQWdPQWjNSGzY7ebb8JFxaWmx+AIX9g5zG5iLLy3hueq9tbiw6/7GTZCM5MNIt0IZJOVjq0GQSJIs4jNIkD7lqVMm6Cx+rXGYIO3agLX4yC9GIC6v2LDp9a/k95uKDTsUdvx8tw24MD59+hjJSF2WybZCIfjy8dUgSSRBFgkySJA+5alTJii/xpmYZmglsb1uEERGefjXrtoDG7ZiDEIbo7Bhk2Zx+7H+uDA+ffoYyUg+jHArFIItn6AaJIkkyCJBBgnSpzx1ygS96XWrIARz5Luts5YwkvviY8Ou1LucF5mMDVvf9HbusCRcGJ8+fYxkJB9GvhVKwJZPVg1sIgmySJBBgvQpT53Cy0xpoSG4awfvVapcufJQkkgur/iw1ACv14uwYaWTvDxHPMCF6XZA+hipSD6MfCsUgSufrBr4ROKzSJBBgvQpTx1cqAeYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZgGBAWYBgQFmAYEBZjGfgTt6eJcwcXlDbWr4TDYST7tR1CEsqpxk60halfDYbCLfNqdoDn4Z1MAZNhFPu1O0MzIzMiR7d6Y1bvRDfRRQHBCidq1sl/sIp/2KKhTXrFLCpqw7IfQm/nRVu0F7NjYRT7tUdAIhJpcQ58kTfMPDw8ZpHat7Be7yKc9CtqNS+h1LqHJMxEq0ahdK/vFLvJpz4KeqJ+t6bdc7VrZL3aRT3sWFK2o6x3P2jfejrCLfNqToE9Syloq7RxG82m/ggLlAhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgGhAUYBoQFGAaEBRgmv8DpAR3vajnujkAAAAASUVORK5CYII=" 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" /><!-- --></p> <pre class="r"><code>print(p11)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -1975,23 +1937,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 -## k_parent_sink 0.00321 4.71e-05 0.00222 4.64e-03 -## sigma 6.43473 1.28e-04 3.75822 9.11e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 96.15820 4.83e-13 90.24934 1.02e+02 +## k_parent 0.00321 4.71e-05 0.00222 4.64e-03 +## sigma 6.43473 1.28e-04 3.75822 9.11e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.05e+02 NA 9.90e+01 1.10e+02 -## k__iore_parent_sink 3.11e-17 NA 1.35e-20 7.18e-14 -## N_parent 8.36e+00 NA 6.62e+00 1.01e+01 -## sigma 3.82e+00 NA 2.21e+00 5.44e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.05e+02 NA 9.90e+01 1.10e+02 +## k__iore_parent 3.11e-17 NA 1.35e-20 7.18e-14 +## N_parent 8.36e+00 NA 6.62e+00 1.01e+01 +## sigma 3.82e+00 NA 2.21e+00 5.44e+00 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 1.05e+02 9.47e-13 99.9990 109.1224 ## k1 4.41e-02 5.95e-03 0.0296 0.0658 -## k2 9.20e-13 5.00e-01 0.0000 Inf +## k2 7.25e-13 5.00e-01 0.0000 Inf ## g 3.22e-01 1.45e-03 0.2814 0.3650 ## sigma 3.22e+00 3.52e-04 1.8410 4.5906 ## @@ -2000,10 +1962,10 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 2.16e+02 7.18e+02 2.16e+02 ## IORE 9.73e+02 1.37e+08 4.11e+07 -## DFOP 3.31e+11 2.08e+12 7.53e+11 +## DFOP 4.21e+11 2.64e+12 9.56e+11 ## ## Representative half-life: -## [1] 41148169</code></pre> +## [1] 41148171</code></pre> <p>In this case, the DFOP fit reported for PestDF resulted in a negative value for the slower rate constant, which is not possible in mkin. The other results are in agreement.</p> </div> </div> @@ -2013,17 +1975,12 @@ div.tocify { <div id="example-on-page-12-upper-panel" class="section level2"> <h2>Example on page 12, upper panel</h2> <pre class="r"><code>p12a <- nafta(NAFTA_SOP_Attachment[["p12a"]])</code></pre> -<pre><code>## Warning in summary.mkinfit(x): Could not estimate covariance matrix; -## singular system.</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> +<pre><code>## Warning in summary.mkinfit(x): Could not calculate correlation; no covariance +## matrix</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p12a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p12a)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2034,24 +1991,24 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 100.521 8.75e-12 92.461 108.581 -## k_parent_sink 0.124 3.61e-08 0.104 0.148 -## sigma 7.048 1.28e-04 4.116 9.980 +## Estimate Pr(>t) Lower Upper +## parent_0 100.521 8.75e-12 92.461 108.581 +## k_parent 0.124 3.61e-08 0.104 0.148 +## sigma 7.048 1.28e-04 4.116 9.980 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 96.823 NA NA NA -## k__iore_parent_sink 2.436 NA NA NA -## N_parent 0.263 NA NA NA -## sigma 3.965 NA NA NA +## Estimate Pr(>t) Lower Upper +## parent_0 96.823 NA NA NA +## k__iore_parent 2.436 NA NA NA +## N_parent 0.263 NA NA NA +## sigma 3.965 NA NA NA ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 100.521 2.74e-10 92.2366 108.805 -## k1 0.124 5.43e-06 0.0959 0.161 -## k2 0.124 6.45e-02 0.0315 0.490 -## g 0.880 NaN NA NA +## k1 0.124 5.75e-06 0.0958 0.161 +## k2 0.124 6.72e-02 0.0319 0.484 +## g 0.877 5.00e-01 0.0000 1.000 ## sigma 7.048 2.50e-04 4.0349 10.061 ## ## @@ -2062,23 +2019,23 @@ div.tocify { ## DFOP 5.58 18.5 5.58 ## ## Representative half-life: -## [1] 3.987308</code></pre> +## [1] 3.99</code></pre> </div> <div id="example-on-page-12-lower-panel" class="section level2"> <h2>Example on page 12, lower panel</h2> <pre class="r"><code>p12b <- nafta(NAFTA_SOP_Attachment[["p12b"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in qt(alpha/2, rdf): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in qt(alpha/2, rdf): NaNs produced</code></pre> +<pre><code>## Warning in qt(1 - alpha/2, rdf): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs produced</code></pre> +<pre><code>## Warning in pt(abs(tval), rdf, lower.tail = FALSE): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p12b)</code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAqAAAAKgCAMAAABz4j/3AAAC/VBMVEUAAAABAQECAgIDAwMEBAQFBQUGBgYHBwcJCQkKCgoLCwsMDAwNDQ0ODg4PDw8QEBARERESEhITExMUFBQVFRUWFhYXFxcYGBgZGRkaGhobGxscHBwdHR0eHh4fHx8gICAhISEiIiIjIyMkJCQlJSUmJiYnJycoKCgpKSkqKiorKyssLCwtLS0uLi4vLy8wMDAxMTEyMjIzMzM0NDQ1NTU2NjY3Nzc4ODg5OTk6Ojo7Ozs8PDw9PT0+Pj4/Pz9AQEBBQUFCQkJDQ0NERERFRUVGRkZHR0dISEhJSUlKSkpLS0tMTExNTU1OTk5PT09QUFBRUVFSUlJTU1NUVFRVVVVWVlZXV1dYWFhZWVlaWlpbW1tcXFxdXV1eXl5fX19gYGBhYWFiYmJjY2NkZGRlZWVmZmZnZ2doaGhpaWlqampra2tsbGxtbW1ubm5vb29wcHBxcXFycnJzc3N0dHR1dXV2dnZ3d3d4eHh5eXl6enp7e3t8fHx9fX1+fn5/f3+AgICBgYGCgoKDg4OEhISFhYWGhoaHh4eIiIiJiYmKioqLi4uMjIyNjY2Ojo6Pj4+QkJCRkZGSkpKTk5OUlJSVlZWWlpaXl5eYmJiZmZmampqbm5ucnJydnZ2enp6fn5+goKChoaGioqKjo6OkpKSlpaWmpqanp6eoqKipqamqqqqrq6usrKytra2urq6vr6+wsLCxsbGysrKzs7O0tLS1tbW2tra3t7e4uLi5ubm6urq7u7u8vLy9vb2+vr6/v7/AwMDBwcHCwsLDw8PExMTFxcXGxsbHx8fIyMjJycnKysrLy8vMzMzNzc3Ozs7Pz8/Q0NDR0dHS0tLT09PU1NTV1dXW1tbX19fY2NjZ2dna2trb29vc3Nzd3d3e3t7f39/g4ODh4eHi4uLj4+Pk5OTl5eXm5ubn5+fo6Ojp6enq6urr6+vs7Ozt7e3u7u7v7+/w8PDx8fHy8vLz8/P09PT19fX29vb39/f4+Pj5+fn6+vr7+/v8/Pz9/f3+/v7///+jMlc1AAAACXBIWXMAAA7DAAAOwwHHb6hkAAAgAElEQVR4nO2dC3wM5/r4neP3+yunitw2QW7IQcQl0RZFFFWkxKWEJqJ60f44raIal54iaBB1q8uRVFx6SRFaveC01VCtKNqqtqhbqGvEJffEyu77+c/sbhJ2d3aenXln5p31fD+fbCYzj2fefXx35/bOO7UIgjBMLa0bgCCuQEERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmYVXQU70GLNe6DQgDsCro1L2kq9ZtQBiAVUGLTH/Fat0GhAE0E/T7R/1DplaSWo28vLwWEdOCCO+uu+5evj3mousE2U4EdjYP0TVaCVrit6Oy4MmVpFax5c+XeudW7gnMqlm+a4pJJIO4oKF/r13bknJxsCGxxDov5SvpbUY0QCtBj/tWEvLTZpugv/kWca+7Qyurlz/Xf2j8XeGmfOtPzV/iglYYzNaJXxrlF/SbeapTxAJyLY7ie0BUQCtBTRFdM/7i128RdMUL/KvZP5d73RTwkzG0emu/NLjZpMqcwe2T+B/znJDgyZa/iEVGy8I+2wiJ2mOdvkfQ34Kf6biA/x7ePoOQ9PhJ35DOpROOqvcWmUFsb8qO9KS7/9J6r0mzfdDb7w19qMdxS9VCyOvJlnmdd3Mva36emPG47buPfBtxtTguNafuccL/7GxXUN5rPT9F+MpZF2YkkNMhJuu0pZpBtXjukK97ns5tv86a51rk1lOdO8z/82XV36f2iO1N1bAlnH9FQau40ukJW9UWj7PMCDnGvVSQGc0OVoW8GRQdHT48pwch/M+UxYR8PJqfInzlrAtv+d6eN8sW6FDNjcP4V/O64HWWPxMuKfyWWMTV3lTNbhNPwUn+FQXl2TyTeznU0Fa1A03LudfDBqNl2azh1WEpswmpNObEcIJyP68v4f5hAj9F+MpZF5KBu9qdsQXeU80fuc151khuojJ+gFXMfW+Zp7btcV6Ft8cSwntTlp0ly75RUawh6BOuxsZ/NW7/fNJObk89Kd08yeAzvCI71rpMK7Q7SPrBfH3mU1XbnbghV8wHw9ZbpsuaflAddrjlDeOQFVWCfh5ZVNFnbbWg1oXko+geVYH3bOK3tLtZ0XeD6bhxay/rHoM5tuhCb5I1W913qj2Ce1P8zpJ132jVUNP3o7kar+lZdqOlTdBj0WXGjnuyY63LtEKzTfxnHbz94q9WCWqc2dK306fWJfPjXq0JW9mi8djqb1DzrNCQ1+5UC2pdSIofWFcVeM83qHmSX7NppvJaua/VrlOnTgJ3+PUfQt7oEH1ZlffHFs73pvidJeu+0ZEmU/eYuRqP4PZN37YJSvJ3zfXamR1rXaYV7F1JuuF1YvAfK7VuhQchvDfFf9Rt+0m30gZ1MefEjNxKyEKLoOPTD7RKzYnlBLUu06r17Ak6aTTZVG+z1q3wIIT3pnhBrftGyZPMN+rdzIlJ611eGJGU3dp4Izh93ivkUtBn2bHWZVq1nj1BS7nPtthVJMQdBPemLDtLln2jc538g5bxB0njAzvMW1SR2KLfrM1nO7R+amFUdqx1mVawJyiC3IWooAdnjEmcfkiNpiCII2KCLgybuXbt7PBFqjQGQewREzS4kH8tC1ehKQjiiJigzS3XXfLaqtAUBHFETNBMv4Tp0xMDPlalMQhij+hBUl5G8py1eWo0BUEckXgUf2XNfUxaEfX/BqynEBKP4tNbjr1/afJfmTo6gvUUwt2jeNOnm3lG96fx36JTnnTVH10aaS9ST6kfXNbT3aP4soRhPEGNabRMp6CgdJElqMBR/KiWMhulZ1BQusgSVOAovkbQ7yS2SsegoHSRJ6hzqgU1R429Iy2FfkFB6aKooKToyadKpOXQLSgoXWQJ2ibIit3sakGL/ih4ruMVyW3TJSgoXWQJmh+RfoHHbnaVoKmNWnu9lxz8h4zm6Q8UlC7yNvHvOB3MyCbovuZXyJnGx973+1pa0/QJCkoXBfdBk9/iXv5vNdlnSJeWR5egoHRRUNB3+aFkhnxMyKl/Tq50/S88CBSULgoKejlg2Y/zQgq4qZu9nqLfhYJRUFC6KHma6Xj8I8+fs0zdebnNWWm5dAcKShdlz4PW8K5hj7RkegMFpYtagpKv/VZLy6Yz1Be0YmvaMerrZAbVBCWn24y9LS2frlBd0Bstn3wxYAX1lbKCeoKSosGP2Ybmujyh9/i/pOVmHtmC9nOY41rQKZMIudioQOZamUVFQYl5TpP9/O+i5tO/nhV4XVpy1pEjaDxP3fh4u9muBe3Lr7BTjvS1so2aghLyhR8/Ml3mYO5l9BppyVlHjqAL66dkZnpnZlbPOHKY541hrv7R+LmEFHhdk75WtlFXUHK6bWIZWcYP8fnmHGnJWUfWJv5gx0wSVPNnabcoHi9/V//mnP+4BRFJriJ0jcqCktJR7U79FHyV3PjnXmnJWUfePmhR4rMGh5kidyhcS02if2DGDPIEdX7bseuCrvbdtsirs7enjrUt9yDpQ8fnEuAtNEJIvO1YpKCHQiZe2X9/7jNJAwUVQuLgYWIFvTmw0znRhukVFJQuVG87tiFaUHOq36diDdMrKChdNLrtOCdkQoVokC5BQelC9bZj46L5PJFB4uu9NTTyT0Dz9AcKShdZgpqytpk3DJ1ZWvW3MTmJJzwQsubVPhmQML2BgtJFlqCvRD02sO+2UWPsZgML+nvEiFugQF2BgtJFlqCG4tv1T5DCALvZ0IKWvxLseWfrUVC6yBK0cX5RnUMkr4ndbHhBdwZM9bQueCgoXeSdqDc0nRyV0t3+QrAbBb02qMPv4GBdwJyg2Ys2GWm1RAPkHcX/fJTsfWOD/aPf3CroWp9FHvXoONYEHddmcp/IUvE4VlG7s4gjudHdz0hbD5MwJuhvIWWEjFxKrzFqo72gxLTYZ5V2T3SmDWOCborjXtLGUmuL6jAgKCHHH+2VK21V7MGYoEeaVxCSuJheY9SGCUFJ5QLvlR7yJcqYoOS5yFmD2hRTa4vqsCEo9yXaJfqUtLUxBmuCkh2z1pfTaokGsCIoMS31XuAJwzEzJ6jOYUZQQs72ifxZ2gpZAgWlC0OCErLBMNk6Yrj5o2EjvpSYRGPkCFryVvxW7leC3WwUVAi1BSX5icGf879TOmza0HyD1CyaIkfQhH7pkesJqWM3GwUVQnVBCfmmxdALhPj+RUhOO+lZNESOoH6F5FrwpbsE/eFrnt7NKbRLr4gKmmTD/s44F8j6xJfP9H6ntG4lIXl+MrJohxxBg68TkjHQXC1oad/ePP4u74v3cEQFTU9Pf75VyoI2b8NzytwkneoT0ZrbvKcMlZVFK+QI+o7XDGIe2bu23WzcxAth28S3v0lIQQd4TtkF3eL/j/btIuwfHqIPZB3F/7KdkMqsCXZzUVAhbIIGlHFb3iB4TvkFLZnSYEKZ3CTaICZobBXwlCioEDZBx/XM2tr7FWcBUkYWgXEq5p875GfRADFBs6uAp0RBhbAJalwzYmSas06v0kYWAbLjn/2P08ijMoBNfMWenTs/i4KnREGFsAlamTrcuMLZZUiJI4sAMS72eVV/g4gCBH2uo1dsYCo8JQoqhE3QiXHhlUNecrJc6sgiUPLH+SzW2z1LAEGbGeceuDgAnhIFFcImaGhxNDHa3xnHI31kESjHYppt0Vc/PICgfmU7F5HW8JQoqBA2QQPzosn1MGcBdiOLlCUM4wlqTLGBuyM776OYTnEAgr4Wnd/ylUh4ShRUCJugS9oGzWq5ysly49riwumxKdWjLJk+3czTPZRmC00fBMfq6M5PgKDmY+TAvFx4ShRUiKpr8bvfTPnJ2fJRvcqeHvJ+jP0dL7QLWrHYb8x5uimVAyBoOs9meEoUVAiboNlC9wX7lROfYlIkfeAGKIVver2mkyFvAYImJSWND3ZjhGkUVAiboFEhs5w/abPDryQyl+TZ10+Jgua96jX9Jv209AFe6rzeH54SBRWiahN/al67HuudLD8QOGxAq/Et1tnNVqag51/wTi5UIjFdgIIaW8BToqBCVPcHvb0r+gFnAeXbUv+90uHbVamCnk70fZv5x3oDBI3jaJUIT4mCCmETdFuC78CP3LhzVbmCnkjwY11RgKA7ObLdGGAaBRXCJmifDPfG8VSyoCfifZKZHlVUTFC1O4DrHYCg+e4+xkzZgv75rPeMfCVXIA8xQdXvAK5vAIJWtnezV5HSBc19udFrzPZmBmziVe8Armcgm/hB9XrExMTAcypf0EuTvZ47ofRKpAEQVP0O4DoGImiOBXhONQp6M9lvMJOPoAYIqkUHcN0iqz+oAOoUtHRFaNdP2Rv7FiCoNh3AdYqs/qACqFXQyk0Ph61ibfBgUUHDT8ZYcLLIoQP499b74htzL5cJKfn6PvzdQ05/UAFU/MR/N9hn2kXV1gZBVNAtBYK7TPYdwEv7We+Lf4B7ySBkf+/78HcbWf1BnaPqJun0BK8R+1VcnxiwS51mp/0KlO8A7i4HPtb6WFRWf1ABVC5o4ZJmD29k5smfAEG3jTZ3qbfc2RK7DuA2NBPUNLhVXONZGq3chqz+oAKoXlDT530MM/5SeaUCAAQN/D570PUQeErNBN3Yq5LcCtS2tzhw8DDnmyQBtCjoiQleg/7LwjE9QFCDKSnNbP94PhdoJugE/ukgidqOMgjqLCK4SXKONgUtSYtsvkD7Xs0AQQcMbVowsQs8pWaCLn6R2zpFafu4StBNc4KbJMZOLB8Y02DEbo1vAgUIWrjqCEl24x4Wzep5M2T8ewN7abtdgggquEli78TyrRXtmr99WbPVE5CgbF74cMaNuc+v1nhgAoiggpskZUcWkcjBsQ2f2mYk5NqX+7X47AMEZfXCB5NABBXcJCk9sohEStZ385u41Ld/u0c1uEUEICjDFz7YAyLorSmPdkt2dkmRvRPLVZye8feWS/PGTVF/zZDTTCxf+GANiKBDRx489MxoZwF2J5ZLOkXxeDEwePfv4bsTG3Zuqf4gowBBGb/wwRagAWy5Hfo7QUJB/e6a/uMwT383vh2U4lbDYlLyvH/DZ7+qVHfFkEudrF/4YAmIoN0vEXKhk5Pl8Tx14+PtZjNR0HFdN873/enKkocNr+5X88yTqKBn93Nf6wXHl8FTMlFPrYA85WOE39ixPtOcLF9YPyUz0zsz0242EwU1rR/16jF+4mRyq5Cpv6i2XjFBl/wt0OfLpvWbdIenZKKeWiEq6Gob9hZaONgxkwQ5zGWuoL9ObxY2QyVHxQT1/5nsqfW+WyfAmKunmkBumju7+5iz/t88RYnPGhxmsljQw2+ENJ/qcM1LAcQEbUCIuYF7+8Us1lM1xAXdEuLfOdgg2GPgQ8fHVTBa0MPTwoIn7lP65L2YoF7cj5tnORitpzqICroj6Fvu9VDYdnhOdgt6dHZ7vxe/VPTx6WKCNszPz/fhfty4tZ/deqqAqKBdvrNM/tgZnpPpgp5dHP3Q0xuVeziDmKB1qnCyDJ927IiooN7W/SWTG5sl1gt6ff3Qh7rNV6gfLj7tmC6igoafs0xe9qxB/yt2jgsOGb9DgQtNVJ92bEUH9VQOUUFTnuCHkysf7HkjAv82P/rB/u+eppyV5tOObeiknsogKqhprO8LyeOaxgmdaHKCfgp6a8tzAc3Hf+bG0JKiUH3a8Xf4vHjAedCjK15ffMCdnPoRlOdoau8Hu885QOuSPc2nHZf2x+fFA2+acwt9CcpRunNSRMPBK6k8GlSWoBynHGfprp40QUFtXPlgTFDjUevOyc0jV1AnD5nVZT1pgYLexam0EYaQMRtkSYqC0gUFtePYqjhDcOJ7f0r993IFXeM4S9f1lAsK6oQTaaOCDEOXHnLjzstq5ArqBN3XUw4oqADnP3i5zYOP/3uHu09sQEHpgoK64NaONx9/sPXz7/3uRhcoFJQu8gRlbGQRJbjzy8pRYQ/1nrH9CiweBaWLLEHZG1lEIa5/ObOfV+DQ+bsLRENRULrIEpTJkUUU4/RHk7o92GJE6rcuLUVB6SJLUEZHFlGQyt83vPrYP5oNS9nlMMysDRSULrIEZXdkEUWpPPbB5J4NA/pN23TC8Qo+CkoXeQdJ9iOLPFjLgg+NlrHOuU9nDwmt9/ALy769p3s+CkoXWYLiLQpFP/xnXLcG/k9M2lY1BwWliyxB8RYFK3/tWDi3ahoFpYssQfEWBUdQULrIO82EtyhY+a5nyGDbA4VQULrIEtThFgUr911BTxiyzi4Psp4eRUHpIu8o3u4WBRv3XUHn8iOrDfzEMi1b0H4Oczy6nhU7Nru8iIydRSgw/W3uJf5Dy7QcQVkezlIhLrXoOcznCxcBKCgF9oWeJTkG60Nt5QjK8nCWCvHsHEIOGVwM4IqC0mBVI5/AL62TsjbxOhnOkiJtf+Vemrp4jCUKSgVT9SPu5O2D3jucpXmvx98XP/ADQq7WdzHoAgpKGbkHSXcPZ2m7Lz74n/JSsswB39nvhs9zEYCCUkaB++LTXrRNlJ/S+LFvCnAi6V87XC1HQSmjwG3HVYIubdDcW9tHD2sACkoZ5QTdH3qR/BlwQl563YGCUkaB++Jtgs75N/fy8mp56XUHCkoZBS512gRdPo57edrp01Y8GBSUMsoJetF/9a8Lg9y9T1/voKCUUU5Q8tvwtolnqGdnHBSUMgoKel+CglIGBaULCkoZFJQuKChlUFC64NhMlEFB6YJjM1EGBaUL3bGZ/jrDMyiMQsP0CgpKF6pjM5W0CuVpEEKjZToFBaWLEmMzYUHpgvUUwt2xmWxgQemC9RRC4mkmLChdsJ5CSBU08aaNU2ehnIAGnjpJPaUbkWdvitFTAUHdrCfs7bAaletGPSUK+oVXQysNav0NCjxSgZRurLyhGD4/SysaxXrqO+pvbtRToqDVlNaFRn7TCxo5dwY08qnPoZFe18VjLPyp7Qk0WD0LHoJE5XtDoi6DnmN7PhASdQp0c+qxVpAoGyioHSioU1DQu0BBxUBBwaCgdEFB7UBB7UBBnYKC3gUKKgYKCsY0Bxp58T1o5L7d0MgNudDIt12MDXQPRYuhKRUBVs/KueIxhBjfhkRVzIdElS2ERBW/A4kqXAKJsiFXUARRFBQUYRoUFGEaFBRhGhQUYRoUFGEaFBRhGhQUYRoUFGEamYJuCgtJhUWG/r127SxAXH4baFpLJCTt4mBDYgkkpzUQ2lIlgNUT0kJYHUE1hNUPVDzzRIP/BJMb2sgUNM/3XEGYw7Ajzqhw9SSnu1gcEQRMa4mEpP2lUX5Bv5mAnNZAaEuVAFZPSAthdQTVEFY/WPE+b3OjoPXncG3kCpqRQMhMUNeO34Kf6bjAJB73VXoQMK0lEpJ2O5cpPR6Q0xoIbakSwOoJaSGsjqAawuoHK96RfcTYdwdcG7mCzplOyNoxkMive57Obb8OEHghCJqWjwSmvRa5FZaTCwS3VAFgjQS1EFZHYA1h9QMVb/7/DjbDtZEraDK/pkRo9MZhgCC+ZLC0fCQorXld8DpQTksgKKVCwOsp2kJYHUE1hNUPWrzDERnuaCNPUH4ts6dBIn88SkjWSEAgXzJYWj4SkrYyfsAlUE5rILilCgB746AWwuoIqSGsfrDifZxNyPIxcG3kCno1IK8sHLS3u6XdzYq+kEdU8SWDpeUjIWm39jLDmmoNBLdUAWBvHNRCWB0hNYTVD1a81dHGW4OWw7WRfZopMyIcdr7APMmv2TTIoYdlowNKy0dC0r5Wu06dOgmAnNZAcEuVAPTGQS2E1RFSQ1j9YMUzjgkOGG+Ea4Mn6hHGQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaZhVNBTvQYs17oNCAswKujUvaSr1m1AWIBRQYtMf8Vq3QaEBbQQtPFxYloQ4d11Fzddp6FXA/8lXDsaeXl5vVATsz3mousk2U4EdjYP0TcaCfpS79zKPYFZnKD5hPz8t7OkVvE9IbumiI2cISqobaxUjgORfol3zLObNE22DV6Z8pWM1iOqoo2gv/kWcb93h1ZaBCWB++0Ffa7/0Pi7/jTlW39q/hIX1DpWKjdRHnikvFfGtjZXLnX95lSniAXkWhzFN4MoizaCrrBszM3+ubyg5u+8S2sE3RTwkzF0V1Xo0uBmkypzBrdP4n/Mc0KCJ1v+IhYZLQv7bCMkao91+h5BrWOlchPbhhFSVvLGm4QsnzDpG9K5dMJR9d6r9kB2qOxIT7r7L213nLQR9PVky0Tn3aSOj1+DWl/YSraIm7fm54kZj1eNI/1txNXiuNScuscJ/7OzXUF5r/X8FOHLZl2YkUBOh5is05ZSBtXiuUOsY6Vyv1IHRvkOK1zf8Ub+E8NPde4w/8+XNXjP2gHYoaphSzj/ioIuHmeZCDnGl6xyY1NSU7IKMqPZwarIN4Oio8OH5/QghP+ZspiQj0fzU4Qvm3XhLd/b82bZAu1LeTgig3t9q+XZ4iEzKsf5tUywfGkkXFL+LTKEyA5VzZ4TT8FJ/hUFPdC0nPt92GC0lMxUv+Duks0aXj2ZMpuQSmNODCco9/M6t3HanMBPEb5s1oVk4K52Z2yB95TSOlYqN7FyPCHvx1aUcr6ncH/ue8s8tW2P82q8USZwuUNl2V+y7B4VxRqCPuHKbPxX4/bPJ+3kdtOT0s2TDD7DK7Jjrcu0QaOj+LghV8wHw9ZbNzqkyfG7BC1r+kF15OGWN4xDVlQJ+nlkUUWftdWCWheSj6J7VAXes4m3jpVqOm48G3amKCZlT4uiqyHnuP+n2KILvUnWbLXftGa43KHi95esu0erhpq+H82VeU3PshstbYIeiy4zdtyTHWtdpg0aCWqc2dK306fEJmj0MlLLx8/P71F+6fy4V2tCV7ZoPLb6G9Q8KzTktTvVgloXkuIH1lUF3vMNah0rtbxWLskIDnixwjzN0IZ/gM+m/xDyRofoyyq9V+1xuUPF7y9Zd4+ONJm6x8yVeQRXpLdtgpL8XXO9dmbHWpdpA3NXkm54nRj8x0qtW+FBuNyh4j/ttl2lW2mDuphzYkZuJWShRdDx6QdapebEcoJal2nTfOYEnTSabKq3WetWeBAud6h4Qa27R8mTzDfq3cyJSetdXhiRlN3aeCM4fd4r5FLQZ9mx1mXaNJ85QUuN3Idc60Z4Ei53qCz7S5bdo3Od/IOW8QdJ4wM7zFtUkdii36zNZzu0fmphVHasdZk2MCcogtwNCoowDQqKMA0KijANCoowDQqKMA0KijCNREGPJ93PXKH7f4D1dFEZiYKmPTL//iVsl3iBsJ5wXNZTqqAvSvt3HsGTcgXt5zAH6ykECuo+cgSN56kbH283G+spBArqPnIEXVg/JTPTOzOz6u+Sf1i6r9Yy0GiYTlFW0N33X88OWZv4gx0zSZDD3FEtZaTUO0oKemRQPb/vpaXQL/L2QYsSn3X8ukRBhZAn6F+G9472rf2htBy6Re5B0oeOd6GhoELIE3TZeO6ll+/LpdKy6BTZR/GOoKBCyBN0zpvcy8tLR7U8LC2NPkFB6aKgoDkh58mxgD9Jpm/KfXSshILSRcmDpBUNg335u4TPP979nLRMOgQFpYuip5lu596x/Dal+m6Ulkp/oKB0UelE/a8RT1+XlkxvaCCouYz6KtlBrStJFZMb75CWTWeoLqh55oN1O/1OfaWsoN6lzuzgl0uk5dMVqgua0emKeW2LO9TXyggqXosvfLb5D9apA9E+j+2Vlpt5VBd0MD9yV9tfqa+VEVTtLPKpf1IF9+uSYfO1T/3OSEvOOqoLGsf3LQk7QX2tjKBub6ZrQ9r8xG2U+HEPJyyRlpx1VBd0e1jO5RkPe+yZZrW7233gO9P4H34U4zcWSkvOOuofxa+P8H/Gc4fdVb0/6KWYDjsMP5CfAjz0wBPPg9JFnqAHZ4xJnH7Ifq5Ih+UM31EhDwRlibdNl6CgdJEl6MKwmWvXzg5fZDdbrEf9hb5RDlJ7DCgoXWQJGlzIv5aF280WveXD/J7PHKNYy3QKCkoXWYI2tzxtIK+t3WzAPUkX+nX4RTRIl6CgdJElaKZfwvTpiQEf280G3TS3wfffFYAw3YGC0kXeQVJeRvKctXnVf5q/3Mzz7EDImi8PDj8AidMZKChdZAlqytpm3jB0ZvU9HaUjh/EENYate5P/RM+7HQQFpYssQV+Jemxg322jxtjNBhf0+qjQr4GhugEFpYssQQ3Ft+ufIIUBdrPdKOiO4DE3wMG6AAWliyxBG+cX1TlE8prYzXanoMWv+tsfY+kbFJQu8k7UG5pOjkrpnmQ3272C5kQ85UnPxkRB6SLvKP7no2TvGxvse9K4WVDjXJ+llW79C5ZBQemiRGcRtwt6sufDP0tbFXugoHRhQlBiXm+YVCwepgdQULqwISgh+aMDP5W2NsaQI2jJW/FbuV8JdrNRUCFUFJSQ7JaxnnCwJEfQhH7pkfxTXe1mo6BCqCoouT3Xe6H++zjJEdSvkFwLvlQjaGm3KB4vfxoN0ykMCUrImX5t9kn8p8wgR9Dg64RkDDTXfIMeOczTvwWFdukVpgQlJCvw2TzxKJaRI+g7XjOIeWTv2nazcRMvhPqCkpIpvit0fVJU1lH8L9sJqcyaYDcXBRVCA0EJ+b1Hh/1k6zOjddqNBE8z0YU9QQn5sHHHVu+nh3wkL4tGoKB0YVFQUlS30dI7P7SXmUUbUFC6MCno7bq/92mzzVdmFm1AQenCpKDkkY/IJ40a63JYZhSULqKCVj101v7mdxfIL+iRoK4Pt5zkPVOHd4SgoHQRFTQ9Pf35VikL2rwNz0mhoGV79xvJX1Kc1UgAACAASURBVCMCM82yU6kMCkoXyCa+/U1CCjo4C3A+9A29gu6LekxvY5CgoHSBCBpQRkh5kJPlAkPfUCyoaW3AaH2N3IaC0gUi6LieWVt7v+JkucDQN1QLWjTNe46edkXFBI2tAp4SBRXCJqhxzYiRac76GQkMfUO5oLlxTTfqZ3xWMUGzq4CnREGFsAlamTrcuMLZKP0CQ99QL+gPj3bUzZD2gE18xZ6dOz+LgqdEQYWwCToxLrxyyEvOAuyGviF/neEZFEaxgRbMmcGD/qSdVBkAgj7X0Ss2MBWeEgUVwiZoaHE0Mdrf/M5jXFtcOD02pXoQsNLwUJ4H/ei2kadioc94XXTEAwjazDj3wMUB8JQoqBA2QQPzosl1Z9+Ko3qVPT3k/Zix9rMVKej117zn6uBoCSCoX9nORaQ1PCUKKoRN0CVtg2a1XOVkuV858SkmRXJGFnGHMyObpDH/wCqAoK9F57d8JRKe0pMFrZzu9cDgyy4CQNfid7+Z8pOz5R1+JZG5JM++fsoV9NDjLT9RKjclAIKaj5ED83LhKT1Z0NTHL5VOe9xFAETQbKGTPAcChw1oNb7FOrvZShZ0R7vO3ymXnQIAQdN5NsNTerKgXfZzH1gvFwPIQQSNCpl11nlA+bbUf690WKZoQU3vh8QcUTC/XACCJiUljQ+eDU/pyYI+9gMv6E3hANAm/tS8dj3Ww1eqcEFvL/d/5pSia5AD8FLn9f7wlJ4s6JJuZwsnP+EiANYf9Pau6AfgK1W8oMVzfV66oPA6pAIU1OjGrcSeLKhpVkD9Ea7OH0IE3ZbgO/AjN4ZOUqGgN5K8JrJ5WhQgaBxHq0R4Sk8WVBSIoH0ybrmVU5WCXnnFa7qLXRfNAAi6kyPbjUecoKBCWAXNtx+gVgyVCnr+Re9ZhaqsyR3EBNXkDgUdAxC0sv1x93KqVtAzo33nFam0Lihigmpzh4J+gWziB9XrERMTA8+pYkH/TPBNYWtgUcAmXvgOBefIq+f+ZZ8wf/nNBRBBcyzAc6r6iT8e75fC0rcoQFDBOxQEkFXPiWGvRj9aLiOBxsjqDyqAypuk48+wtKEHCCp4h4IC93j9EVRCyPB3pSfQGnn9QZ2j+j7T8QSfZPdONCgHQFDBOxQUuMdr03DuJc2+v5mOkNUfVAANdupPPus9I1/1tTpDVNDwkzEWnCyyv8er4k3LAX/4Q9zL14ScSHL/97ONb5MTEY9L/vea/+4opz+oAJocdea+7DXZVbcttRAVdEuB4D69/T1ed5bO54n04l4OEHJ+voTffTvNfcp3tvR/r/XvTnL6gwqg0WmRSxO9/k+gU4uKwC51mp1eY1DiHi/zJzPSdNDPWxBZ/UEF0Oy8Xf6b3gm/abTuKgCCbhtt7lJvubMl9vd4WcHzoELUDB7m/BMvgIYFLUzxH/C9ZmvnAQga+H32oOsh8JQoqBBVnUUEP/FKD30jgfLVzbp+puGITgBBDaakNLP9I6JdgIIKUXWQJPSJV37oGylUbooKX3dbq7UDBB0wtGnBxC7wlCioEDZBBT/xagx9I4lvnmyyQKMTowBBC1cdIcluPLRM+3pqCERQwU+8OkPfSOLXhPpd5ri41UUxAIIyfmWOLSCCCn7i1Rr6RgqpgZ3rPKDB4z8BgjJ/ZY4lIILemvJot2Sn59LYPS1S2uAKKRrwYJctaj90CSCoDq7MsQNE0KEjDx56ZrRQUL+ayZIHa1nwodM2OfwWwb3seyyra/CiAlVXDDnNpIcrc6wAGsCW21+6E+RkeTxP3fh4u9ksFLSswQVC5nDb0cMJjcafUHHFAEF1cmWODSCCdr9EyIVOTpYvrJ+SmemdmWk3m4mCvtt0YlzgRX7qylt+T36p2gCjkEuderkyxwKQp3yM8Bs71meas4CDHTNJkMNcNgp6ZPG6ql6iFeujmi9W6bSTqKBn95cRUnB8GTwlG/XUCFFBV9uw/5q0UpT4rMFhJosFzYlvOFaVAUnEBF3yt0CfL5vWb9IdnpLFeqoG5Ka5s7uPOetea+VDx8HW2Szo1blNH/vQjZt9JSImqP/PZE+t993a42CzniohLuiWEP/OwYYNbuRktaCVn/TxSzqj8ErEBG1AiLmBe+e+WK2nKogKuiPoW+71UNh2eE6GC3rqdZ8ntyl6k6OYoF7cj5sjUDNcT+URFbSLdbTDHzvDczJd0PL3uwa8qeBTQMUEbZifn+/D/bhxgwrT9VQaUUG9rZsjkxufetYL+scE775ZwnvV8hATtE4V8JSs11NRRAUNt37bXPaoMdXLP+jhN8XN8VKA4JPm6CIqaMoT/NnE8sGeNuDqyakBj72nwO30KChdRAU1jfV9IXlc0zg3Nok6KWjlZ4MajN5Du/M9CkoXwHnQoyteX3zAnZz6KWje4ojQ2XTvBEVB6QK8ac4tdFXQw6/4RGdQHMURBaXLfS8oIbc/Hdxg5A5aJ0c9S9DS00qd7YCCgvLcWNXF71W3dmME8ShB5z7UzC9Lq5VbQUFtnJkd1vwtCmeePEnQXa3zyK9+f2m0disoaA2HJgZEprpxt6VT5Aha8lb8Vu5Xgt1szer5On9D+ciPNFq7FRT0bkzfjvV+bPkVOSnkCJrQLz1yPSH2F5k0q+ccvhNwnx0ard0KCmqH8cvERo+vlv6IGzmC+hWSa8GX2BH0lN+6X2eHaTv0GArqSMX2+IY9V0n8HpUjaPB1QjIGmpkRlByKbfuctrugMgVlcGwmOpRzjnZfKmV/VI6g73jNIOaRvWtX/W3evpmnezPpKXWPLEHZHJuJEhVfPOf9cIrbN4TKOor/ZTshlVkTqv4se2YYT1Bj29+mKxqOiqYRsgRldmwmStzZ/a8mrab96JYVck8zOXlMblU9070MflvkZdcfsgRleGwmWpgPTg8PePlL+HNc5Aoa5TjLVs9DgafJEQO7z3lWBlmCsjw2E0VOvdOj/qC1V2HBygk6bwb3MnaNvPS6Q95Bkt3YTCWtQnkedPOeGx1w/f3hDR+ZfRiwsZcrqBMBbYIu5R+tNOJDeel1hyxBHa58XDjDM8iNcYf0g/HbyS0NYzaLjf+g3KXOc4b3T69qcp16eraRJShrVz4U58yK/vW7zTvs6q52Ba/FH+rXfPAf1LMzjixBWbvyoQbl/53Y2jd+g+AjmTypswgLyDvNxNqVD5U4nz6sUcSkL0uqZ5g+W/adbRIFpYssQe2vfNi4Hwpa+ePcHv/oNmufpT+vMbrrq61sz8NEQeki7yje7sqHjfuloKW7pkTV77vgx8qMGG7TH/ajZSYKShfsLCKPm5+8GvFQ6FMH7pDn11pmoKB0QUHlkz+2RbtOd9rus/yBgtIFBaVAadshb3cbbD2Lj4LSBQWlQfl707fYTo6ioHRBQSmDgtIFBaUMCkoXFJQyKChdUFDKoKB0QUEpg4LSBQWlDApKFxSUMigoXVBQyqCgdEFBKYOC0gUFpQwKShcUlDIoKF1QUMqgoHRBQSmDgtIFBaWMbEH7OczBegpx/45uJx05gsbz1I2Pt5uN9RTivh7dTiJyBF1YPyUz0zsz02421lMId0e3K+3fm8c/gEbLdIqsTfzBjpkkyGEuCiqEu6Pbmfd+zTN+EI2W6RR5+6BFic8a7vqz8CZP3D9ltknPKDG6XdqLMhulZ+QeJH0YWzNd0qQhT52m8lLqGqqj29lAQWXgZPxPrKcQEk8zYUFl4GR8UKynEFIFbZdk5Y1mnYBEtYBGRoRDI8MioZEhjwIDH2mTJEaIAoK6V89HQyhGhUKiHoFFgVr/cFs36ilR0PPzbcyp3Q/Iw17QyBbNoJG+UdDI/+0FDOz+4HwxFtyUVrQqnAxg62Y9e/8PJKrX/4NE9awDierxACSqe11IVLeH3KinREGrKa0LjfymFzRy7gxo5FOfQyO9oKPC/qnt0LywehY8BInK94ZEXfaHRJ0PhESdag6JOtYKEmUDBbUDBXUKCnoXKKgYKCgYFJQuKKgdKKgdKKhTdCtoRQNo5N4noZELZkIjB4FP+BgKgIFnWkNTKgKsniUg9W4ZxGMIuQa6iHUpFBKVC+pScDICEmVDrqDkBjTQDD45UwZ+OvQtVw/juAdwM92IVATY6u+HKCuyBUUQJUFBEaZBQRGmQUERpkFBEaZBQRGmQUERpkFBEaaRKeimsJBUWGTo32vXzgLE5beBprVEQtIuDjYklkByWgOhLVUCWD0hLYTVEVRDWP1AxTNPNPhPMLmhjUxB83zPFYQ5jOrgjAqDGZRxcUQQMK0lEpL2l0b5Bf1mAnJaA6EtVQJYPSEthNURVENY/WDF+7zNjYLWn8O1kStoRgIhM0FdO34LfqbjAsCFya/Sg4BpLZGQtNu5TOnxgJzWQGhLlQBWT0gLYXUE1RBWP1jxjuwjxr474NrIFXTOdELWjoFEft3zdG77dYDAC0HQtHwkMO21yK2wnFwguKUKAGskqIWwOgJrCKsfqHjz/3ewGa6NXEGT+TUlQqM3DgME8SWDpeUjQWnN64LXgXJaAkEpFQJeT9EWwuoIqiGsftDiHY7IcEcbeYLya5k9DRL541FCskYCAvmSwdLykZC0lfEDLoFyWgPBLVUA2BsHtRBWR0gNYfWDFe/jbEKWj4FrI1fQqwF5ZeGgvd0t7W5W9N0ACORLBkvLR0LSbu1lhjXVGghuqQLA3jiohbA6QmoIqx+seKujjbcGLYdrI/s0U2ZEOOx8gXmSX7NpkEMPy0YHlJaPhKR9rXadOnUSADmtgeCWKgHojYNaCKsjpIaw+sGKZxwTHDDeCNcGT9QjjIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTMO2oKd6DViudRsQTWFb0Kl7SVet24BoijaC3qnVqJHvqEJu9Y28vLwWEdOCCO+u/FPjajfyerDryeq4ItNfsa4zZTtZfu+8xsdtE69Hy2s0ogVaCVpOCl/qy62+2PL3S71zK/cEZnGCFpNb8U/VBG6Pueg6E1xQk88d60TKV5LbjaiOdoKSCt8/bIL+5lvEve4OreQFJTtDquN2TblnGBVTvvWn5i83BL3jR051ilhArsVRexuI8mgoKInNtAm64gX+1eyfywtaNKof98emgJ+Mobue6z80nl+0NLjZpMqcwe2T+B/znJDgyZa/iEVGy8I+2wiJ2mOdthc0Z9gbsQPyEv9n6KRvSOfSCUdVfrNaAt2Xsic96e6/nH0LqIWWgr6wxFK3EPJ6smVu592kto9f3Y4nuOk1P0/MeLxqOOlvI64Wx6Xm1D1O+J+d7QrKe63npwhfO+vCjARyOsRknebrGVSLh9+oc4I2vEZGr+S/QTt3mP/ny5q8Y40A70tVsyWcf0VBLd+gH9nqtnicZW7IMcsm3kIFmdHsYFX4m0HR0eHDc3oQwv9MWUzIx6P5KcLXzrrwlu/tebNsgQ7foD0JmZPKCcqTcEnxd8cQkH2pmp0mngLL1yoKytXttt/vtrodaMr7ethgrBGUkFnDqydTZhNSacyJ4QTlfl5fQsjmBH6K8LWzLiQDd7U7Ywt0EDSmRtB9b5mntu1xXvm3yAbi+1KWXSXLnlFRrCHoE67Cxn81bv980k5uVz0p3TzJ4DO8IjvWukwLtBO0ZHyf6i1P3JAr5oNh68ldgpY1/aA6/HDLG8YhK6oE/TyyqKLP2mpBrQvJR9E9qgIdNvE1gppjiy70JlmzVX23GiK+L8XvKln3jFYNNX0/mqvwmp5lN1raBD0WXWbsuCc71rpMC7QS1NfX75mCakGNM1v6dvqU3C3o/LhXa+JXtmg8tvob1DwrNOS1O9WCWheS4gfWVQW6+Abd9B9C3ugQfVnpN8gK4vtS/K6Sdc/oSJOpe8xchUdwu6hv2wQl+bvmeu3MjrUu0wJWryTd8Dox+I+V9PKZjPRy6QjxfSn+02vbS7qVNqiLOSdm5FZCFloEHZ9+oFVqTiwnqHWZFu+AVUEnjSab6m3WuhW6R3xfihfUumeUPMl8o97NnJi03uWFEUnZrY03gtPnvUIuBX2WHWtdpsU7YFXQUu4bT6uB4j0I8X0py66SZc/oXCf/oGX8QdL4wA7zFlUktug3a/PZDq2fWhiVHWtdpgWsCoogFlBQhGlEBT04Y0zidOAzbRCENmKCLgybuXbt7PBFqjQGQewREzS4kH8tC1ehKQjiiJigzS1XBfPaqtAUBHFETNBMv4Tp0xMDPlalMQhij+hBUl5G8py1eWo0BUEckXgUv7nWfczfcqj/N2A9hZB4FJ/2osz/ET3z5C7qKbGeQrh7FF/i35CnTmMaLdMpKChdZAnqcBRfcpMnriWFhukVFJQusgQVOIofhYJSBQUVQuJRfLWg5hb/ldou3YKC0kWeoM6p+QY94L9aWgr9goLSRVlBycnmb2pzM4BmoKB0kSVomyArdrOrBD3QJ3jA992fLpPeOh2CgtJFlqD5EekXeOxm2wTNNWTmrml8ccRj1+Q0UG+goHSRt4l/x+lQWzZBl/B3Xo78wDwz9HdJTdMnKChdFNwHnTeDexmbRsj7vjulJdIjKChdFBT0cOCf5CfDGW4qp/F8aZl0CApKFyWP4jO8fQO2WabOt3v+trRcugMFpYuip5nM1YdHJUO7XJGWTG+goHRR+DxoNeZZgffHzXUaCHrxaAX1dTKDWoIS8oXfWmnp9IXqghpH+EU0/ob6SllBPUHJH2Gv2sZAurMxKcNT90llC9rPYY5rQVNjb5O9/uUy18osKgpKCgZ0v8r/rny87/wBj3qooXIEjeepGx9vN9u1oDFfcC8dPXb/SU1BiXl2k33cr8+6mbkvig+lJWcdOYIurJ+SmemdmWk327Wgo9O5j3zTXOlrZRtVBSVkh99SQhZP5KZmzZKWnHVkbeIPdswkQTV/ltS33pdjcPVv9ges3zPC2YDynoHKgpLcqKcLv+5wh5i6bpOWnHXk7YMWJT7raKNIB/B9w6Jnl8hZKdPIE9T5XZ0uC1rxctiv8W3+FRnroeMnyj1I+tDxmQR4h4IQEu/qFCnohz7p2e967HkRBU4zoaBCSBybSaygJyLii0QbpldQULrQvavTimhBy8aG/SzWML2CgtJFo7s6P/Zb6qG3gqCgdKF6V6dp4xqeTqHi6z3zaIxndrRHQekiS1BT1jbzhqEzS6v+Lh8/lqdFE8CKjVMbe+STr1FQusgS9JWoxwb23TZqjN1sYEG/DZzkgb1wUFC6yBLUUHy7/glSGGA3G1rQG09HeN7Tr1FQusgStHF+UZ1DJM9+iw4v6DqfRZ52vh4FpYu8E/WGppOjUron2c12o6Bnu/fIBQfrAuYEPbj6Cz1/Ccg7iv/5KNn7xgb79+9OQU2pPunwaB3AmqBvNHu5y2M63tdXu7OII79HxlySth4mYUzQY4HFhAxdQa8xaqO9oMQ423eDtBWxCGOCbhrOvaSNpdYW1WFAUEKOtI+5KG1V7MGYoD/900jICwvpNUZtmBCUGJN90z3k0idjgpIRXRfGhxVQa4vqsCEoIb890vO0tLUxBmuCmre8sVLP3ZlZEZRUvuO98I609TEFa4LqHWYEJeRsnw6Hpa2QJVBQujAkKCHv+0+w9mQ2re0fu0ViEo2RI2jJW/FbuV8JdrNRUCHUFpTceK4p/z9EZj36+ZZWaVKzaIocQRP6pUeuJ6SO3WwUVAhe0CQbbjwUXk5BvwuPOUOI9yVCDkVIz6IhcgT1KyTXgi/VCGre+zVP7+Y0GqZTRAVNT09/vlXKgjZvw3PK+sQb53vPLqzLHS5ddXk3OLPIETT4OiEZA83VgpbG9Obx96fRMJ0C2cS3v0lIQQd4TpmbpL+GNmu9hpC34mRl0Qo5gr7jNYOYR/aubTcbN/FC2AQNKCOkPAieU3ZBvwr9R1iLh/U5oKiso/hfthNSmTXBbi4KKoRN0HE9s7b2fgWeU35BjQsavqjTqx9igsZWAU+JggphE9S4ZsTINKOzAAkjiwC5MjpgnS67MYoJml0FPCUKKoRN0MrU4cYVzq7ySBtZBMjBLh2/o5FHZQCb+Io9O3d+FgVPiYIKYRN0Ylx45ZCXnCyXOLIIEPPHQU/r7/o8QNDnOnrFBqbCU6KgQtgEDS2OJkZntxJLHVkESvnb3hNvUMqlFgBBmxnnHrg4AJ4SBRXCJmhgXjS5HuZkufLPi8/7P59UfY1uDRDUr2znItIanhIFFcIm6JK2QbNarnIWYDeySEnnKB4vmqfYTwwKXK+noyWAoK9F57d8JRKeEgUVoupa/O43U35ytty4trhwemxKzS1ZRw/z9G9BsYGE/NA14jOqCRUFIKj5GDkwLxeeEgUVwiZottA32KheZU8PeT/G/o4X6gX9LKLLXsopFQMgaDrPZnhKFFQIm6BRIbPOOl3uV058ikmR9IEboJg+aP7kQdpJlQEgaFJS0vjg2fCUKKgQVZv4U/Pa9VjvZHmHX0lkLsmzr58SBTWuaTrwCP209AFe6rzeH54SBRWiuj/o7V3RDzhZfiBw2IBW41uss5utTEErlgUM1cFQTkBBjW7sp6OgQtgE3ZbgO/CjYmcB5dtS/73SYfOvVEHLFvs//asyqekBEDSOo1UiPCUKKoRN0D4Zt9zKqVxBS9/xH8L44OEAQXdyZLsxGg0KKoRV0Hz7wcHEULKgpUubPJWjXHr5iAmq8h0KugcgaGX74+7lVLagFatDen6t5ArkISao2nco6B3IJn5QvR4xMTHwnEoX9M7G1h2zWL26BNjEq3yHgr6BCJpjAZ5T+YKaPukUls7mmIIAQVW/Q0HPyOoPKoAqBd0b4z/vpgrrcReAoMJ3KCjXAVy3yOoPKoBKBf1jTKN/sddfFCCo4B0KinYA1ymy+oMKoFpBL0/3GczaRXpRQcNPxlhwssi+A/jttywH/OENuJdvCTmZdB/+flhOf1ABVPzEl65uGbmeqZ1RUUG3FAju09t3ADcuns8T2Yh72U9I7vz78PejsvqDOkfVTZJ5Z1+/GQwNfwu71Gl2uvusfAdwN7mcFDdf47EbZfUHFUDtgp54pdHT2equUhiAoNtGm7vUW+5siV0HcBuaCZrXZOrH8V20HRQTOHiY80+8AOoXtGhl6/CVhWqv1SkAQQO/zx50PQSeUjNBU8dx//OPfqvR2q2AOosIfuKdo0lBs4c3HOvO17xSAAQ1mJLSzPaP53OBZoK+tpRf+0aN1m4FdNOc4CeeqfN2V+YGP5zmtNOVmgAEHTC0acHELvCUmgn6cdfbJK/xnxqt3QpEUMFPPGvn7Uw7Bzd88UeNVm4DIGjhqiMk+Tw8pWb1NI8OivF9R6OV24AIKviJV3bgBklcSWnRZomWT6IHCMrklTnnHPtC6xMkEEEFP/FKD9wgCfPexAaDPzPeWTXkBS32SQGCMnpljk0ggt6a8mi35FIny5k7b2ej8L2ufuGdti7z06DnKEBQZq/MsQhE0KEjDx56ZrSzAPvzdr8qcV+8JA4/ENQ29Z0R6q8YcpqJ3Stz7AEawJbbX7oTJBTUr2aypIt1ZBEGhqz+o7V57wv1G2SoPsooQFCmr8yxBkTQ7pcIudDJyfJ4nrrx8XazWSjoHUMOMT07ZNBDQ7PUHdsJcqmT8StzTAF5yscIv7FjfaY5Wb6wfkpmpndmpt1sJgq6y9Djn4+XkFtrezcc9cVt9dYrKujZ/WWEFBxfBk/JRD21QlTQ1TbsLbRwsGMmCXKYy0ZBC/fYblG++m43r+d2OR0hWgHEBF3yt0CfL5vWb9IdnpKNemoE5Ka5s7uPCf33FiU+6ziUHXsFvbj0Me8xX6jSLU9MUP+fyZ5a77t1RxV79VQRcUG3hPh3DjZsEIr50PFpAEwW9MLSbg2fyVK+75iYoA0IMTeodCslk/VUC1FBdwTxvVkOhW2H52S1oFf+06d+7Lp8ZVciJqgX9+PnXkpW66kKooJ2sT7J4MfO8JwMF/TWB08/1OMdJe9kEhO0YX5+vg/348bnhOF6Ko+ooN7WzZHJjU892wUt//KlgFZvfOfeVhaOmKB1qnCyDJ927IiooOHnLJOXPWlMdfPBf0d6PfPhdSVy49OO6SIqaMoT/CPcywd72oCrF9MGPdR5ziHqA5TQfNqxDV3UUylEBTWN9X0heVzTODfOI+qloLe/mRzuE7/xKtWkNJ92bEMv9VQEwHnQoyteX3zAnZx6Kuhf6U83ajflqzJqCWk+7bikGzN9GzQDeNOcW+hJUI7KnOSu/+iV8iOdwyaqTzs+wkzvMM1AQS0UfTahTcPYZb+ZZWeSJSjHKcdZOqwnPVDQaq5mvtjcd9iK3+VJKldQJw+Z1Wk96YCC3sOFjc839xmy7Bfpm3sUlC4oqAMXP3y51UP95n0nrR+pXEHXOM7SeT3lgYI65dq2iY/U6zz5kytu/0u5gjrBA+opHRRUkNLsuTGNQuNX/OTW4EQoKF3kCcrUyCJKYD6W8UKbB7u9vgU8zgIKShdZgrI2sohCFO6eF+tvGJC8E9IDCQWliyxBGRxZRDH+2jq1V4Pgp+d/JdLFBAWliyxBmRxZREHMJzMnR9cPHjzni0uCMSgoXWQJyurIIopiPvlx0hM+vn2SMo85O1uKgtJF3kGS3cgi5ePH8rRwY1gXvXLh87lPN6/b8fmlu+02+SgoXWQJat8D3LRxDU+nUCpNY5/inDXjujU0PDFx7cHqcUlRULrIEhR7gPNc2LkwMSq66i8UlC6yBMUe4I6goHSRd5oJe4BbKXznX+m2Ww5QULrIEtS+B7iN+66gRWHPvdv/CevtTSgoXeQdxdv3ALdy3xX03VHcAWKU9XEtsgXt5zDHs+tZfsPlYuwsQoHJ/IMGnltrmZYjKMPDWSpFxZh6jTo5uYugGhSUAu/3MpHi0F8s03IEZXk4S4V4a0ipaW6NfAAABVtJREFUeVlHFwEoKAUqB7QdHfyGdVrWJp7l4SyV4WG+L5yfw9Mfa0BBqfDdettgpDL3QXUynCU9euwmxNigSDgABaWM3IOku4ezLHmwlgU3HpuoNzLafn981DAXASgoZRS47TjtRXkpmSatY8vJLr5AUVDaKHBXZ5Wg3zzSoJtbQ7x4AigoZZQT9E/Djlub/C/LS687UFDKKHDbsU3QRZO5l0TBsdg9FBSUMgpc6rQJOn8q9/L8e9TTsw0KShnlBD0acIh8a3DjMd4eAQpKGeUEJVmh/6/1V9SzMw4KShkFBSVExSfmsQIKShlFBb0PQUEpg4LSBQWlDApKFxybiTIoKF1wbCbKoKB0oTs2U/FNnjgUlCooqBDujs1U4t+Qp05jGi3TKSgoXZQYmwkLShespxDujs1kAwtKF6ynEBJPM2FB6YL1FEKqoIk3bVw662Hk3RSjpwKCem49nZDnRj0lCvpJwypq/Q0KPFKBlG6svKEoB6UVjWI99R1lX2JX9ZQoaDWldaGR3/SCRs6dAY186nNopBf0yfF/hkFTKgKsngUPQaLyvSFRl0HPsT0fCIk61RwSdawVJMoGCmoHCuoUFPQuUFAxUFAwKChdUFA7UFA7UFCnoKB3gYKKgYKCMU2BRp5fBo38Zgc0crWrUf3uYRr0VorC2dCUigCr550kSNTtaZCoctDXQelbkKiiWZCoW3MgUTbkCoogioKCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMI1PQTWEhqbDI0L/Xrp0FiMtvA01riYSkXRxsSCyB5LQGQluqBLB6QloIqyOohrD6gYpnnmjwn2ByQxuZgub5nisIcxh2xBkVBjMo4+KIIGBaSyQk7S+N8gv6zQTktAZCW6oEsHpCWgirI6iGsPrBivd5mxsFrT+HayNX0IwEQmaCruX+FvxMxwUm8biv0oOAaS2RkLTbuUzp8YCc1kBoS5UAVk9IC2F1BNUQVj9Y8Y7sI8a+O+DayBV0znRC1o6BRH7d83Ru+3WAwAtB0LR8JDDttcitsJxcILilCgBrJKiFsDoCawirH6h48/93sBmujVxBk/k1JUKjN7p6mlMVfMlgaflIUFrzuuB1oJyWQFBKhYDXU7SFsDqCagirH7R4hyMy3NFGnqD8WmaDOnX9eJSQrJGAQL5ksLR8JCRtZfyAS6Cc1kBwSxUA9sZBLYTVEVJDWP1gxfs4m5DlY+DayBX0akBeWThob3dLu5sVfSHPV+FLBkvLR0LSbu1lhjXVGghuqQLA3jiohbA6QmoIqx+seKujjbcGLYdrI/s0U2ZEOOx8gXmSX7NpkEMPy0YHlJaPhKR9rXadOnUSADmtgeCWKgHojYNaCKsjpIaw+sGKZxwTHDDeCNcGT9QjjIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMg4IiTIOCIkyDgiJMox9B+3s1rOXl9ZLWzfAYdFJP/QhKSH497mVLuNbN8Bh0UU/dCVpwUutmeAy6qKfuBM2JyYkZ0/Wl5IFtLpGlwc0mVWrdKv2ii3rqUdAHi+54pZPXl30bcbU4Dnr7KuKALuqpR0F7E9LuHHl35ptB0dHhw7VulX7RRT31KOiTXEEvcAVNmU1IpVHrVukXXdRTz4IebnnDOGSF1q3SL7qop54FJStbNB7L2ideR+iinnoS9F5MrJVS5zBaT/0KitwXoKAI06CgCNOgoAjToKAI06CgCNOgoAjToKAI06CgCNOgoAjToKAI06CgCNOgoAjToKAI06CgCNOgoAjT/H+YPFZ7JaTUIwAAAABJRU5ErkJggg==" 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" /><!-- --></p> <pre class="r"><code>print(p12b)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2089,24 +2046,24 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.6840 0.00039 85.9388 109.4292 -## k_parent_sink 0.0589 0.00261 0.0431 0.0805 -## sigma 3.4323 0.04356 -1.2377 8.1023 +## Estimate Pr(>t) Lower Upper +## parent_0 97.6840 0.00039 85.9388 109.4292 +## k_parent 0.0589 0.00261 0.0431 0.0805 +## sigma 3.4323 0.04356 -1.2377 8.1023 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.523 0.0055 74.539157 116.51 -## k__iore_parent_sink 0.333 0.1433 0.000717 154.57 -## N_parent 0.568 0.0677 -0.989464 2.13 -## sigma 1.953 0.0975 -5.893100 9.80 +## Estimate Pr(>t) Lower Upper +## parent_0 95.523 0.0055 74.539157 116.51 +## k__iore_parent 0.333 0.1433 0.000717 154.57 +## N_parent 0.568 0.0677 -0.989464 2.13 +## sigma 1.953 0.0975 -5.893100 9.80 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 97.6840 NaN NaN NaN ## k1 0.0589 NaN NA NA ## k2 0.0589 NaN NA NA -## g 0.8275 NaN NA NA +## g 0.6902 NaN NA NA ## sigma 3.4323 NaN NaN NaN ## ## @@ -2117,7 +2074,7 @@ div.tocify { ## DFOP 11.8 39.1 11.80 ## ## Representative half-life: -## [1] 9.461912</code></pre> +## [1] 9.46</code></pre> </div> <div id="example-on-page-13" class="section level2"> <h2>Example on page 13</h2> @@ -2125,7 +2082,7 @@ div.tocify { <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p13)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p13)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2136,22 +2093,22 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 92.73500 5.99e-17 89.61936 95.85065 -## k_parent_sink 0.00258 2.42e-09 0.00223 0.00299 -## sigma 3.41172 7.07e-05 2.05455 4.76888 +## Estimate Pr(>t) Lower Upper +## parent_0 92.73500 5.99e-17 89.61936 95.85065 +## k_parent 0.00258 2.42e-09 0.00223 0.00299 +## sigma 3.41172 7.07e-05 2.05455 4.76888 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 91.6016 6.34e-16 88.53086 94.672 -## k__iore_parent_sink 0.0396 2.36e-01 0.00207 0.759 -## N_parent 0.3541 1.46e-01 -0.35153 1.060 -## sigma 3.0811 9.64e-05 1.84296 4.319 +## Estimate Pr(>t) Lower Upper +## parent_0 91.6016 6.34e-16 88.53086 94.672 +## k__iore_parent 0.0396 2.36e-01 0.00207 0.759 +## N_parent 0.3541 1.46e-01 -0.35153 1.060 +## sigma 3.0811 9.64e-05 1.84296 4.319 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 92.73500 9.25e-15 8.95e+01 9.59e+01 -## k1 0.00258 4.28e-01 1.38e-08 4.82e+02 +## k1 0.00258 4.28e-01 1.45e-08 4.61e+02 ## k2 0.00258 3.69e-08 2.20e-03 3.03e-03 ## g 0.00442 5.00e-01 0.00e+00 1.00e+00 ## sigma 3.41172 1.35e-04 2.02e+00 4.80e+00 @@ -2164,20 +2121,20 @@ div.tocify { ## DFOP 269 892 269 ## ## Representative half-life: -## [1] 168.5123</code></pre> +## [1] 168.51</code></pre> </div> </div> <div id="dt50-not-observed-in-the-study-and-dfop-problems-in-pestdf" class="section level1"> <h1>DT50 not observed in the study and DFOP problems in PestDF</h1> <pre class="r"><code>p14 <- nafta(NAFTA_SOP_Attachment[["p14"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p14)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p14)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2188,23 +2145,23 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 -## k_parent_sink 0.00279 3.75e-15 0.00256 3.04e-03 -## sigma 1.55616 3.81e-06 1.03704 2.08e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 99.47124 2.06e-30 98.42254 1.01e+02 +## k_parent 0.00279 3.75e-15 0.00256 3.04e-03 +## sigma 1.55616 3.81e-06 1.03704 2.08e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 1.00e+02 NA NaN NaN -## k__iore_parent_sink 9.44e-08 NA NaN NaN -## N_parent 3.31e+00 NA NaN NaN -## sigma 1.20e+00 NA 0.796 1.6 +## Estimate Pr(>t) Lower Upper +## parent_0 1.00e+02 NA NaN NaN +## k__iore_parent 9.44e-08 NA NaN NaN +## N_parent 3.31e+00 NA NaN NaN +## sigma 1.20e+00 NA 0.796 1.6 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 1.00e+02 2.96e-28 99.40280 101.2768 ## k1 9.53e-03 1.20e-01 0.00638 0.0143 -## k2 6.17e-12 5.00e-01 0.00000 Inf +## k2 7.70e-12 5.00e-01 0.00000 Inf ## g 3.98e-01 2.19e-01 0.30481 0.4998 ## sigma 1.17e+00 7.68e-06 0.77406 1.5610 ## @@ -2213,24 +2170,23 @@ div.tocify { ## DT50 DT90 DT50_rep ## SFO 2.48e+02 8.25e+02 2.48e+02 ## IORE 4.34e+02 2.22e+04 6.70e+03 -## DFOP 3.00e+10 2.91e+11 1.12e+11 +## DFOP 2.41e+10 2.33e+11 9.00e+10 ## ## Representative half-life: -## [1] 6697.437</code></pre> +## [1] 6697.44</code></pre> <p>The slower rate constant reported by PestDF is negative, which is not physically realistic, and not possible in mkin. The other fits give the same results in mkin and PestDF.</p> </div> <div id="n-is-less-than-1-and-dfop-fraction-parameter-is-below-zero" class="section level1"> <h1>N is less than 1 and DFOP fraction parameter is below zero</h1> <pre class="r"><code>p15a <- nafta(NAFTA_SOP_Attachment[["p15a"]])</code></pre> -<pre><code>## Warning in sqrt(diag(covar)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(diag(covar_notrans)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in sqrt(1/diag(V)): NaNs wurden erzeugt</code></pre> -<pre><code>## Warning in cov2cor(ans$cov.unscaled): diag(.) had 0 or NA entries; non- -## finite result is doubtful</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p15a)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p15a)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2241,25 +2197,25 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 2.00e-15 94.32049 101.615 -## k_parent_sink 0.00952 4.93e-09 0.00824 0.011 -## sigma 4.18778 1.28e-04 2.44588 5.930 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96751 2.00e-15 94.32049 101.615 +## k_parent 0.00952 4.93e-09 0.00824 0.011 +## sigma 4.18778 1.28e-04 2.44588 5.930 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 95.874 2.94e-15 92.937 98.811 -## k__iore_parent_sink 0.629 2.11e-01 0.044 8.982 -## N_parent 0.000 5.00e-01 -0.642 0.642 -## sigma 3.105 1.78e-04 1.795 4.416 +## Estimate Pr(>t) Lower Upper +## parent_0 95.874 2.94e-15 92.937 98.811 +## k__iore_parent 0.629 2.11e-01 0.044 8.982 +## N_parent 0.000 5.00e-01 -0.642 0.642 +## sigma 3.105 1.78e-04 1.795 4.416 ## ## $DFOP -## Estimate Pr(>t) Lower Upper -## parent_0 97.96751 2.85e-13 94.21913 101.7159 -## k1 0.00952 5.68e-02 0.00262 0.0347 -## k2 0.00952 1.52e-04 0.00639 0.0142 -## g 0.22357 NaN NA NA -## sigma 4.18778 2.50e-04 2.39747 5.9781 +## Estimate Pr(>t) Lower Upper +## parent_0 97.96752 NA 94.21914 101.7159 +## k1 0.00952 NA 0.00241 0.0377 +## k2 0.00952 NA 0.00747 0.0121 +## g 0.17247 NA NA NA +## sigma 4.18778 NA 2.39747 5.9781 ## ## ## DTx values: @@ -2269,12 +2225,16 @@ div.tocify { ## DFOP 72.8 242 72.8 ## ## Representative half-life: -## [1] 41.32749</code></pre> +## [1] 41.33</code></pre> <pre class="r"><code>p15b <- nafta(NAFTA_SOP_Attachment[["p15b"]])</code></pre> +<pre><code>## Warning in sqrt(diag(covar)): NaNs produced</code></pre> +<pre><code>## Warning in sqrt(1/diag(V)): NaNs produced</code></pre> +<pre><code>## Warning in cov2cor(ans$covar): diag(.) had 0 or NA entries; non-finite result is +## doubtful</code></pre> <pre><code>## The SFO model is rejected as S_SFO is equal or higher than the critical value S_c</code></pre> <pre><code>## The half-life obtained from the IORE model may be used</code></pre> <pre class="r"><code>plot(p15b)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p15b)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2285,25 +2245,25 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 -## k_parent_sink 4.86e-03 2.48e-10 0.00435 5.42e-03 -## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00 +## Estimate Pr(>t) Lower Upper +## parent_0 1.01e+02 3.06e-17 98.31594 1.03e+02 +## k_parent 4.86e-03 2.48e-10 0.00435 5.42e-03 +## sigma 2.76e+00 1.28e-04 1.61402 3.91e+00 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 99.83 1.81e-16 97.51349 102.14 -## k__iore_parent_sink 0.38 3.22e-01 0.00352 41.05 -## N_parent 0.00 5.00e-01 -1.07695 1.08 -## sigma 2.21 2.57e-04 1.23245 3.19 +## Estimate Pr(>t) Lower Upper +## parent_0 99.83 1.81e-16 97.51348 102.14 +## k__iore_parent 0.38 3.22e-01 0.00352 41.05 +## N_parent 0.00 5.00e-01 -1.07696 1.08 +## sigma 2.21 2.57e-04 1.23245 3.19 ## ## $DFOP ## Estimate Pr(>t) Lower Upper -## parent_0 1.01e+02 NA 9.82e+01 1.04e+02 -## k1 4.86e-03 NA 6.49e-04 3.64e-02 -## k2 4.86e-03 NA 3.36e-03 7.03e-03 -## g 1.50e-01 NA 0.00e+00 1.00e+00 -## sigma 2.76e+00 NA 1.58e+00 3.94e+00 +## parent_0 1.01e+02 NA 98.24464 1.04e+02 +## k1 4.86e-03 NA 0.00068 3.47e-02 +## k2 4.86e-03 NA 0.00338 6.99e-03 +## g 1.50e-01 NA NA NA +## sigma 2.76e+00 NA 1.58208 3.94e+00 ## ## ## DTx values: @@ -2313,7 +2273,7 @@ div.tocify { ## DFOP 143 474 143.0 ## ## Representative half-life: -## [1] 71.18014</code></pre> +## [1] 71.18</code></pre> <p>In mkin, only the IORE fit is affected (deemed unrealistic), as the fraction parameter of the DFOP model is restricted to the interval between 0 and 1 in mkin. The SFO fits give the same results for both mkin and PestDF.</p> </div> <div id="the-dfop-fraction-parameter-is-greater-than-1" class="section level1"> @@ -2324,7 +2284,7 @@ div.tocify { <pre><code>## to the terminal degradation rate found with the DFOP model.</code></pre> <pre><code>## The representative half-life obtained from the DFOP model may be used</code></pre> <pre class="r"><code>plot(p16)</code></pre> -<p><img src="data:image/png;base64,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" 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" /><!-- --></p> <pre class="r"><code>print(p16)</code></pre> <pre><code>## Sums of squares: ## SFO IORE DFOP @@ -2335,22 +2295,22 @@ div.tocify { ## ## Parameters: ## $SFO -## Estimate Pr(>t) Lower Upper -## parent_0 71.953 2.33e-13 60.509 83.40 -## k_parent_sink 0.159 4.86e-05 0.102 0.25 -## sigma 11.302 1.25e-08 8.308 14.30 +## Estimate Pr(>t) Lower Upper +## parent_0 71.953 2.33e-13 60.509 83.40 +## k_parent 0.159 4.86e-05 0.102 0.25 +## sigma 11.302 1.25e-08 8.308 14.30 ## ## $IORE -## Estimate Pr(>t) Lower Upper -## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972 -## k__iore_parent_sink 4.55e-04 2.16e-01 3.48e-05 0.00595 -## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046 -## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062 +## Estimate Pr(>t) Lower Upper +## parent_0 8.74e+01 2.48e-16 7.72e+01 97.52972 +## k__iore_parent 4.55e-04 2.16e-01 3.48e-05 0.00595 +## N_parent 2.70e+00 1.21e-08 1.99e+00 3.40046 +## sigma 8.29e+00 1.61e-08 6.09e+00 10.49062 ## ## $DFOP ## Estimate Pr(>t) Lower Upper ## parent_0 88.5333 7.40e-18 79.9836 97.083 -## k1 18.6317 5.00e-01 0.0000 Inf +## k1 18.5560 5.00e-01 0.0000 Inf ## k2 0.0776 1.41e-05 0.0518 0.116 ## g 0.4733 1.41e-09 0.3674 0.582 ## sigma 7.1902 2.11e-08 5.2785 9.102 @@ -2363,7 +2323,7 @@ div.tocify { ## DFOP 0.67 21.4 8.93 ## ## Representative half-life: -## [1] 8.932679</code></pre> +## [1] 8.93</code></pre> <p>In PestDF, the DFOP fit seems to have stuck in a local minimum, as mkin finds a solution with a much lower <span class="math inline"><em>χ</em><sup>2</sup></span> error level. As the half-life from the slower rate constant of the DFOP model is larger than the IORE derived half-life, the NAFTA recommendation obtained with mkin is to use the DFOP representative half-life of 8.9 days.</p> </div> <div id="conclusions" class="section level1"> @@ -2400,6 +2360,49 @@ $(document).ready(function () { </script> +<!-- tabsets --> + +<script> +$(document).ready(function () { + window.buildTabsets("TOC"); +}); + +$(document).ready(function () { + $('.tabset-dropdown > .nav-tabs > li').click(function () { + $(this).parent().toggleClass('nav-tabs-open') + }); +}); +</script> + +<!-- code folding --> + +<script> +$(document).ready(function () { + + // move toc-ignore selectors from section div to header + $('div.section.toc-ignore') + .removeClass('toc-ignore') + .children('h1,h2,h3,h4,h5').addClass('toc-ignore'); + + // establish options + var options = { + selectors: "h1,h2,h3", + theme: "bootstrap3", + context: '.toc-content', + hashGenerator: function (text) { + return text.replace(/[.\\/?&!#<>]/g, '').replace(/\s/g, '_').toLowerCase(); + }, + ignoreSelector: ".toc-ignore", + scrollTo: 0 + }; + options.showAndHide = false; + options.smoothScroll = true; + + // tocify + var toc = $("#TOC").tocify(options).data("toc-tocify"); +}); +</script> + </body> </html> diff --git a/vignettes/web_only/NAFTA_examples.rmd b/vignettes/web_only/NAFTA_examples.rmd index 26a9240a..d18a3e84 100644 --- a/vignettes/web_only/NAFTA_examples.rmd +++ b/vignettes/web_only/NAFTA_examples.rmd @@ -91,7 +91,7 @@ used by mkin for the IORE model are used. Therefore, a lower value for the rate constant is used here. ```{r p8} -p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent_sink = 1e-3)) +p8 <- nafta(NAFTA_SOP_Attachment[["p8"]], parms.ini = c(k__iore_parent = 1e-3)) plot(p8) print(p8) ``` diff --git a/vignettes/web_only/mkin_benchmarks.rda b/vignettes/web_only/mkin_benchmarks.rda Binary files differindex cc08f7ad..128473e7 100644 --- a/vignettes/web_only/mkin_benchmarks.rda +++ b/vignettes/web_only/mkin_benchmarks.rda |
