From d2c1ab854491ff047135fa8377400a68499e72de Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Thu, 17 Jul 2014 12:53:30 +0200 Subject: Handle non-convergence and maximum number of iterations For details see NEWS.md --- vignettes/FOCUS_L.html | 118 ++++++++++++++++++++++++++++--------------------- vignettes/FOCUS_Z.pdf | Bin 212996 -> 214130 bytes vignettes/mkin.pdf | Bin 160326 -> 160326 bytes 3 files changed, 67 insertions(+), 51 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index bb02ec3e..85fadbfe 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -193,7 +193,13 @@ hr { report, p. 284</p> <pre><code class="r">library("mkin") -FOCUS_2006_L1 = data.frame( +</code></pre> + +<pre><code>## Loading required package: minpack.lm +## Loading required package: rootSolve +</code></pre> + +<pre><code class="r">FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -223,16 +229,17 @@ FOCUS report.</p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:20 2014 -## Date of summary: Mon Jul 14 20:32:20 2014 +## Date of fit: Thu Jul 17 12:37:41 2014 +## Date of summary: Thu Jul 17 12:37:41 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 14 model solutions performed in 0.087 s ## ## Weighting: none ## @@ -325,16 +332,17 @@ is checked.</p> summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:20 2014 -## Date of summary: Mon Jul 14 20:32:20 2014 +## Date of fit: Thu Jul 17 12:37:42 2014 +## Date of summary: Thu Jul 17 12:37:42 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 45 model solutions performed in 0.266 s ## ## Weighting: none ## @@ -417,16 +425,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:20 2014 -## Date of summary: Mon Jul 14 20:32:20 2014 +## Date of fit: Thu Jul 17 12:37:42 2014 +## Date of summary: Thu Jul 17 12:37:42 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 32 model solutions performed in 0.357 s ## ## Weighting: none ## @@ -526,16 +535,17 @@ mkinresplot(m.L2.FOMC) <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:21 2014 -## Date of summary: Mon Jul 14 20:32:21 2014 +## Date of fit: Thu Jul 17 12:37:43 2014 +## Date of summary: Thu Jul 17 12:37:43 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 39 model solutions performed in 0.235 s ## ## Weighting: none ## @@ -611,16 +621,17 @@ plot(m.L2.DFOP) <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:23 2014 -## Date of summary: Mon Jul 14 20:32:23 2014 +## Date of fit: Thu Jul 17 12:37:44 2014 +## Date of summary: Thu Jul 17 12:37:44 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 54 model solutions performed in 0.423 s ## ## Weighting: none ## @@ -697,16 +708,17 @@ plot(m.L3.SFO) <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:23 2014 -## Date of summary: Mon Jul 14 20:32:23 2014 +## Date of fit: Thu Jul 17 12:37:45 2014 +## Date of summary: Thu Jul 17 12:37:45 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 44 model solutions performed in 0.241 s ## ## Weighting: none ## @@ -782,16 +794,17 @@ plot(m.L3.FOMC) <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:24 2014 -## Date of summary: Mon Jul 14 20:32:24 2014 +## Date of fit: Thu Jul 17 12:37:45 2014 +## Date of summary: Thu Jul 17 12:37:45 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 26 model solutions performed in 0.208 s ## ## Weighting: none ## @@ -854,16 +867,17 @@ plot(m.L3.DFOP) <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:24 2014 -## Date of summary: Mon Jul 14 20:32:24 2014 +## Date of fit: Thu Jul 17 12:37:46 2014 +## Date of summary: Thu Jul 17 12:37:46 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 37 model solutions performed in 0.338 s ## ## Weighting: none ## @@ -944,16 +958,17 @@ plot(m.L4.SFO) <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:27 2014 -## Date of summary: Mon Jul 14 20:32:27 2014 +## Date of fit: Thu Jul 17 12:37:46 2014 +## Date of summary: Thu Jul 17 12:37:46 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 20 model solutions performed in 0.127 s ## ## Weighting: none ## @@ -1018,16 +1033,17 @@ plot(m.L4.FOMC) <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.31 +<pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 14 20:32:28 2014 -## Date of summary: Mon Jul 14 20:32:28 2014 +## Date of fit: Thu Jul 17 12:37:46 2014 +## Date of summary: Thu Jul 17 12:37:46 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## -## Method used for solution of differential equation system: -## analytical +## Model predictions using solution type analytical +## +## Fitted with method Marq using 53 model solutions performed in 0.355 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index 559504cc..43f3e2e2 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf index f06d38f9..9cf1b3e5 100644 Binary files a/vignettes/mkin.pdf and b/vignettes/mkin.pdf differ -- cgit v1.2.1 From a1567638a3ba9f4d62fa199525097a94ddfd7912 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Mon, 21 Jul 2014 08:20:44 +0200 Subject: Bugfix, model shorthand, state.ini[[1]] from observed data - The bug occurred when using transform_rates=FALSE for FOMC, DFOP or HS - Make it possible to use mkinfit("SFO", ...) - Take initial mean value at time zero for the variable with the highest value in the observed data - Update of vignette/FOCUS_L - Improve the Makefile to build single vignettes --- vignettes/FOCUS_L.Rmd | 81 ++++++------ vignettes/FOCUS_L.html | 328 ++++++++++++++++++++++++------------------------- vignettes/mkin.pdf | Bin 160326 -> 160326 bytes 3 files changed, 200 insertions(+), 209 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.Rmd b/vignettes/FOCUS_L.Rmd index 04d5f831..cd7711f6 100644 --- a/vignettes/FOCUS_L.Rmd +++ b/vignettes/FOCUS_L.Rmd @@ -13,7 +13,7 @@ opts_chunk$set(tidy = FALSE, cache = TRUE) ## Laboratory Data L1 The following code defines example dataset L1 from the FOCUS kinetics -report, p. 284 +report, p. 284: ```{r} library("mkin") @@ -25,27 +25,18 @@ FOCUS_2006_L1 = data.frame( FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) ``` -The next step is to set up the models used for the kinetic analysis. Note that -the model definitions contain the names of the observed variables in the data. -In this case, there is only one variable called `parent`. +Here we use the assumptions of simple first order (SFO), the case of declining +rate constant over time (FOMC) and the case of two different phases of the +kinetics (DFOP). For a more detailed discussion of the models, please see the +FOCUS kinetics report. -```{r} -SFO <- mkinmod(parent = list(type = "SFO")) -FOMC <- mkinmod(parent = list(type = "FOMC")) -DFOP <- mkinmod(parent = list(type = "DFOP")) -``` - -The three models cover the first assumption of simple first order (SFO), -the case of declining rate constant over time (FOMC) and the case of two -different phases of the kinetics (DFOP). For a more detailed discussion -of the models, please see the FOCUS kinetics report. - -The following two lines fit the model and produce the summary report -of the model fit. This covers the numerical analysis given in the -FOCUS report. +Since mkin version 0.9-32 (July 2014), we can use shorthand notation like `SFO` +for parent only degradation models. The following two lines fit the model and +produce the summary report of the model fit. This covers the numerical analysis +given in the FOCUS report. ```{r} -m.L1.SFO <- mkinfit(SFO, FOCUS_2006_L1_mkin, quiet=TRUE) +m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet=TRUE) summary(m.L1.SFO) ``` @@ -64,32 +55,30 @@ For comparison, the FOMC model is fitted as well, and the chi^2 error level is checked. ```{r} -m.L1.FOMC <- mkinfit(FOMC, FOCUS_2006_L1_mkin, quiet=TRUE) +m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) summary(m.L1.FOMC, data = FALSE) ``` Due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the chi^2 error level is actually higher for the FOMC -model (3.6%) than for the SFO model (3.4%). Additionally, the covariance -matrix can not be obtained, indicating overparameterisation of the model. -As a consequence, no standard errors for transformed parameters nor -confidence intervals for backtransformed parameters are available. +model (3.6%) than for the SFO model (3.4%). Additionally, the parameters +`log_alpha` and `log_beta` internally fitted in the model have p-values for the two +sided t-test of 0.18 and 0.125, and their correlation is 1.000, indicating that +the model is overparameterised. The chi^2 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 chi^2 error levels -as the kinfit package. - -Furthermore, 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 is a widely used standard package in this field. -Therefore, the reason for the difference was not investigated further. +as the kinfit package. Furthermore, 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 is a widely used +standard package in this field. ## Laboratory Data L2 The following code defines example dataset L2 from the FOCUS kinetics -report, p. 287 +report, p. 287: ```{r} FOCUS_2006_L2 = data.frame( @@ -100,10 +89,10 @@ FOCUS_2006_L2 = data.frame( FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) ``` -Again, the SFO model is fitted and a summary is obtained. +Again, the SFO model is fitted and a summary is obtained: ```{r} -m.L2.SFO <- mkinfit(SFO, FOCUS_2006_L2_mkin, quiet=TRUE) +m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) summary(m.L2.SFO) ``` @@ -130,7 +119,7 @@ For comparison, the FOMC model is fitted as well, and the chi^2 error level is checked. ```{r fig.height = 8} -m.L2.FOMC <- mkinfit(FOMC, FOCUS_2006_L2_mkin, quiet = TRUE) +m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) par(mfrow = c(2, 1)) plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) @@ -144,7 +133,7 @@ experimental error has to be assumed in order to explain the data. Fitting the four parameter DFOP model further reduces the chi^2 error level. ```{r fig.height = 5} -m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, quiet = TRUE) +m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP) ``` @@ -153,7 +142,7 @@ to a reasonable solution. Therefore the fit is repeated with different starting parameters. ```{r fig.height = 5} -m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, +m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, parms.ini = c(k1 = 1, k2 = 0.01, g = 0.8), quiet=TRUE) plot(m.L2.DFOP) @@ -180,7 +169,7 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) SFO model, summary and plot: ```{r fig.height = 5} -m.L3.SFO <- mkinfit(SFO, FOCUS_2006_L3_mkin, quiet = TRUE) +m.L3.SFO <- mkinfit("SFO", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.SFO) summary(m.L3.SFO) ``` @@ -191,7 +180,7 @@ does not fit very well. The FOMC model performs better: ```{r fig.height = 5} -m.L3.FOMC <- mkinfit(FOMC, FOCUS_2006_L3_mkin, quiet = TRUE) +m.L3.FOMC <- mkinfit("FOMC", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.FOMC) summary(m.L3.FOMC, data = FALSE) ``` @@ -202,7 +191,7 @@ Fitting the four parameter DFOP model further reduces the chi^2 error level considerably: ```{r fig.height = 5} -m.L3.DFOP <- mkinfit(DFOP, FOCUS_2006_L3_mkin, quiet = TRUE) +m.L3.DFOP <- mkinfit("DFOP", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.DFOP) summary(m.L3.DFOP, data = FALSE) ``` @@ -212,10 +201,15 @@ and the correlation matrix suggest that the parameter estimates are reliable, an the DFOP model can be used as the best-fit model based on the chi^2 error level criterion for laboratory data L3. +This is also an example where the standard t-test for the parameter `g_ilr` is +misleading, as it tests for a significant difference from zero. In this case, +zero appears to be the correct value for this parameter, and the confidence +interval for the backtransformed parameter `g` is quite narrow. + ## Laboratory Data L4 The following code defines example dataset L4 from the FOCUS kinetics -report, p. 293 +report, p. 293: ```{r} FOCUS_2006_L4 = data.frame( @@ -227,7 +221,7 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) SFO model, summary and plot: ```{r fig.height = 5} -m.L4.SFO <- mkinfit(SFO, FOCUS_2006_L4_mkin, quiet = TRUE) +m.L4.SFO <- mkinfit("SFO", FOCUS_2006_L4_mkin, quiet = TRUE) plot(m.L4.SFO) summary(m.L4.SFO, data = FALSE) ``` @@ -235,14 +229,13 @@ summary(m.L4.SFO, data = FALSE) The chi^2 error level of 3.3% as well as the plot suggest that the model fits very well. -The FOMC model for comparison +The FOMC model for comparison: ```{r fig.height = 5} -m.L4.FOMC <- mkinfit(FOMC, FOCUS_2006_L4_mkin, quiet = TRUE) +m.L4.FOMC <- mkinfit("FOMC", FOCUS_2006_L4_mkin, quiet = TRUE) plot(m.L4.FOMC) summary(m.L4.FOMC, data = FALSE) ``` The error level at which the chi^2 test passes is slightly lower for the FOMC model. However, the difference appears negligible. - diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 85fadbfe..614fcf32 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -190,7 +190,7 @@ hr { <h2>Laboratory Data L1</h2> <p>The following code defines example dataset L1 from the FOCUS kinetics -report, p. 284</p> +report, p. 284:</p> <pre><code class="r">library("mkin") </code></pre> @@ -207,51 +207,43 @@ report, p. 284</p> FOCUS_2006_L1_mkin <- mkin_wide_to_long(FOCUS_2006_L1) </code></pre> -<p>The next step is to set up the models used for the kinetic analysis. Note that -the model definitions contain the names of the observed variables in the data. -In this case, there is only one variable called <code>parent</code>.</p> +<p>Here we use the assumptions of simple first order (SFO), the case of declining +rate constant over time (FOMC) and the case of two different phases of the +kinetics (DFOP). For a more detailed discussion of the models, please see the +FOCUS kinetics report.</p> -<pre><code class="r">SFO <- mkinmod(parent = list(type = "SFO")) -FOMC <- mkinmod(parent = list(type = "FOMC")) -DFOP <- mkinmod(parent = list(type = "DFOP")) -</code></pre> - -<p>The three models cover the first assumption of simple first order (SFO), -the case of declining rate constant over time (FOMC) and the case of two -different phases of the kinetics (DFOP). For a more detailed discussion -of the models, please see the FOCUS kinetics report.</p> +<p>Since mkin version 0.9-32 (July 2014), we can use shorthand notation like <code>SFO</code> +for parent only degradation models. The following two lines fit the model and +produce the summary report of the model fit. This covers the numerical analysis +given in the FOCUS report. </p> -<p>The following two lines fit the model and produce the summary report -of the model fit. This covers the numerical analysis given in the -FOCUS report.</p> - -<pre><code class="r">m.L1.SFO <- mkinfit(SFO, FOCUS_2006_L1_mkin, quiet=TRUE) +<pre><code class="r">m.L1.SFO <- mkinfit("SFO", FOCUS_2006_L1_mkin, quiet=TRUE) summary(m.L1.SFO) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:41 2014 -## Date of summary: Thu Jul 17 12:37:41 2014 +## Date of fit: Mon Jul 21 09:14:29 2014 +## Date of summary: Mon Jul 21 09:14:29 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 14 model solutions performed in 0.087 s +## Fitted with method Marq using 14 model solutions performed in 0.081 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100.0 state -## k_parent_sink 0.1 deparm +## parent_0 89.85 state +## k_parent_sink 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_k_parent_sink -2.303 -Inf Inf +## value lower upper +## parent_0 89.850 -Inf Inf +## log_k_parent_sink -2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -259,7 +251,7 @@ summary(m.L1.SFO) ## Optimised, transformed parameters: ## Estimate Std. Error Lower Upper t value Pr(>|t|) ## parent_0 92.50 1.3700 89.60 95.40 67.6 4.34e-21 -## log_k_parent_sink -2.35 0.0406 -2.43 -2.26 -57.9 5.15e-20 +## log_k_parent_sink -2.35 0.0406 -2.43 -2.26 -57.9 5.16e-20 ## Pr(>t) ## parent_0 2.17e-21 ## log_k_parent_sink 2.58e-20 @@ -316,67 +308,70 @@ summary(m.L1.SFO) <pre><code class="r">plot(m.L1.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> </p> <p>The residual plot can be easily obtained by</p> <pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-6"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> -<pre><code class="r">m.L1.FOMC <- mkinfit(FOMC, FOCUS_2006_L1_mkin, quiet=TRUE) +<pre><code class="r">m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) summary(m.L1.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:42 2014 -## Date of summary: Thu Jul 17 12:37:42 2014 +## Date of fit: Mon Jul 21 09:14:30 2014 +## Date of summary: Mon Jul 21 09:14:30 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 45 model solutions performed in 0.266 s +## Fitted with method Marq using 53 model solutions performed in 0.32 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100 state -## alpha 1 deparm -## beta 10 deparm +## parent_0 89.85 state +## alpha 1.00 deparm +## beta 10.00 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_alpha 0.000 -Inf Inf -## log_beta 2.303 -Inf Inf +## value lower upper +## parent_0 89.850 -Inf Inf +## log_alpha 0.000 -Inf Inf +## log_beta 2.303 -Inf Inf ## ## Fixed parameter values: ## None ## ## Optimised, transformed parameters: -## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) -## parent_0 92.5 NA NA NA NA NA NA -## log_alpha 25.6 NA NA NA NA NA NA -## log_beta 28.0 NA NA NA NA NA NA +## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) +## parent_0 92.5 1.45 89.40 95.6 63.60 1.17e-19 5.85e-20 +## log_alpha 14.9 10.60 -7.75 37.5 1.40 1.82e-01 9.08e-02 +## log_beta 17.2 10.60 -5.38 39.8 1.62 1.25e-01 6.26e-02 ## ## Parameter correlation: -## Could not estimate covariance matrix; singular system: +## parent_0 log_alpha log_beta +## parent_0 1.000 0.24 0.238 +## log_alpha 0.240 1.00 1.000 +## log_beta 0.238 1.00 1.000 ## ## Residual standard error: 3.05 on 15 degrees of freedom ## ## Backtransformed parameters: -## Estimate Lower Upper -## parent_0 9.25e+01 NA NA -## alpha 1.35e+11 NA NA -## beta 1.41e+12 NA NA +## Estimate Lower Upper +## parent_0 9.25e+01 8.94e+01 9.56e+01 +## alpha 2.85e+06 4.32e-04 1.88e+16 +## beta 2.98e+07 4.59e-03 1.93e+17 ## ## Chi2 error levels in percent: ## err.min n.optim df @@ -390,26 +385,24 @@ summary(m.L1.FOMC, data = FALSE) <p>Due to the higher number of parameters, and the lower number of degrees of freedom of the fit, the chi<sup>2</sup> error level is actually higher for the FOMC -model (3.6%) than for the SFO model (3.4%). Additionally, the covariance -matrix can not be obtained, indicating overparameterisation of the model. -As a consequence, no standard errors for transformed parameters nor -confidence intervals for backtransformed parameters are available.</p> +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 p-values for the two +sided t-test of 0.18 and 0.125, and their correlation is 1.000, indicating that +the model is overparameterised. </p> <p>The chi<sup>2</sup> 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 chi<sup>2</sup> error levels -as the kinfit package.</p> - -<p>Furthermore, 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 is a widely used standard package in this field. -Therefore, the reason for the difference was not investigated further.</p> +as the kinfit package. Furthermore, 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 is a widely used +standard package in this field. </p> <h2>Laboratory Data L2</h2> <p>The following code defines example dataset L2 from the FOCUS kinetics -report, p. 287</p> +report, p. 287:</p> <pre><code class="r">FOCUS_2006_L2 = data.frame( t = rep(c(0, 1, 3, 7, 14, 28), each = 2), @@ -419,35 +412,35 @@ report, p. 287</p> FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) </code></pre> -<p>Again, the SFO model is fitted and a summary is obtained.</p> +<p>Again, the SFO model is fitted and a summary is obtained:</p> -<pre><code class="r">m.L2.SFO <- mkinfit(SFO, FOCUS_2006_L2_mkin, quiet=TRUE) +<pre><code class="r">m.L2.SFO <- mkinfit("SFO", FOCUS_2006_L2_mkin, quiet=TRUE) summary(m.L2.SFO) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:42 2014 -## Date of summary: Thu Jul 17 12:37:42 2014 +## Date of fit: Mon Jul 21 09:14:30 2014 +## Date of summary: Mon Jul 21 09:14:30 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 32 model solutions performed in 0.357 s +## Fitted with method Marq using 29 model solutions performed in 0.155 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100.0 state -## k_parent_sink 0.1 deparm +## parent_0 93.95 state +## k_parent_sink 0.10 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_k_parent_sink -2.303 -Inf Inf +## value lower upper +## parent_0 93.950 -Inf Inf +## log_k_parent_sink -2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -487,8 +480,8 @@ summary(m.L2.SFO) ## ## Data: ## time variable observed predicted residual -## 0 parent 96.1 9.15e+01 4.634 -## 0 parent 91.8 9.15e+01 0.334 +## 0 parent 96.1 9.15e+01 4.635 +## 0 parent 91.8 9.15e+01 0.335 ## 1 parent 41.4 4.71e+01 -5.740 ## 1 parent 38.7 4.71e+01 -8.440 ## 3 parent 19.3 1.25e+01 6.779 @@ -509,7 +502,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAJACAMAAABWh4TIAAAC/VBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmnzsbAAAA/3RSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+vv8/v+3IrpgAAAACXBIWXMAAAsSAAALEgHS3X78AAAdqElEQVR4nO3dCXgURd4G8JoEQgjhyMoNhiSL3JcQkCMugUSiXF+AiArBoMuhgAheuBiWiHKFXUEQNIjIJafIHY4cXAEUI/ChAsvHGUBdwCScJiD1fHMkIZnpTqq7uqZ7Zt7f89iB6al/FfOamZ7urm5CAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADA5T0yfAQYV1x5UcEP/ErvfxuUIuOvwoIfJaoyaGCRs4IPCq8pqidQwUnBmz7dMCttjKiuQDknBf/Su+bFupai+gLFnBS8tZvnXxHVFyjmpOATO5gXo54V1Rco5qTgm++oTVrsqSyqLzfUPkk77STqO2urvsuO71cGi+rKHb36WohWxo2QqO+s4Fuu2jW/tqiu3NGrz2lWapCOwTdNr0fa7sNbPTs3CX5mF/Pi9RhRfbkhNwn+ixAyhgx8VVRfbshNgn/5TbKfrGwlqi835CbBey1fdmLXO6K6ckduEjwhwSv6iurJLblN8CR2pKie3JL7BN/2Y1E9uSX3Cd5vp6ie3JImwQfRhkT34M2b9cCuePDlJqSlvVOOoZEpsOTfjRH85r+I6sodFQ9++ngv7/emSTwpfv/63IxmhIzKumv+EX15658k4tjt5EASfWXab+eeJn9S2kL/4Gd0EdWVOzIH79W2nU2mdRFq+0tbr4dPiqfjas8769OCvh15cBuJppv6BOVPjkg57hVNEyrPzyLtaafy+gf/olT/IMMcfLUZNrPOWZbnZhX8NeDhk+J/NZGqtFvgQFODNZnm4P9CEs7XqB5G60dTf9I3T/BbvXedVnW9HR92CL7dbO6uPEjxt/pdDQhpILVtHH/UvLj9ov+S32/8Yg7+HiHLqEVYdD4hvcUGX3FOjrmn3Nm+9iscgvfbwdmVRykefOP9//r3vkYST4q/6k1q0fB3s9p4fWgOPo+QxHRCAmL8LH8UHPzyU0NCm7QbdOxL+xWO59Vjs16BEl/nfNq395F6Ujyd033zWZ9ZZ0KfyzoTbEm7zf3RXTf+ZCoKvo+fqOBzell/dMq2X+EY/JZqnH15Eqbv8fEXk28ebE5qpd35YfSRNyxpk/4nbu8Msf7y9zxOvLb90VRU8EfnWj7fvRKO2K9wDH5mZ86+PAlb8N+ylBITfKec7EOpB67nPGG/wjH4uGGcfXkSwwdP/OOmLZwW5//wgQ4TrDbMt39m+494+/IgrrGv3u+JSg//0iDSav1G+2f5b9eiLw9h+ODrbs3e1u0GvdLCfsXs1Q7PxWY9O8MHv/n4O4dpQvDXKfYrJILfWpWzMw9i+OBvRZNutCbpdct+hUTwiR05O/Mghg/+P7P936ddyLgz9iskgh/6d87OPIjhgx9wn95b99vWu6/Zr5AIvsO/OTvzIIYPnjQa3MQ0YsVgk/3jEsH7J/N25jliM1O08sMgifriDstKBE/2O/zvATpxbvDLMGHWKJwbPGbPGYZzgw+bLqo3UMi5wWOnrWE4N3iSga07g3By8MuxdWcQTg5+HLbuDMLJwT+JrTuDcHLw2LozCicHTw5g684YnB38CmzdGYOzgx+PrTtjcHbwf8PWnTGwBF+tQBVFlaWDx9adQbAETwv8pKiydPDkILbuDIEl+NDQFReGhL1w7kPp1TKzZWWC/yqIfXAgDttn/KU+5sUzl6VWyc6WlQn+DWzdGQJb8JdHmxevZkmtkp0tKxN8uNQlPcDp2IJ/O3/FB8vz35RaJTtbViZ431T2wYE4bMGb+m/8fkM/yc0y2dmyMsGT1IoKhgeiMH6P96of5ie9OS47W1Yu+Kld2UcHwrAFH/gjpTE/hkiuc5gt29F2jZ7vt0nXenqS4kGC9tiC357ShQZslL6GjcmHVAhtUezye/Vss2W/Xi9dqwpOrjcCtuDvdK9OSVeH6XEWjY7GtjpP6f86HHyRe6sne4XdxxjYsQV/fFINSsZI7rnbn1r/4PpGzZIdfo9lg/+ovYLxgSBswT+Vf4Gm3+8nterWM+RWBCHdbtqvkA2+n+TXQnAuxq9zwQnLEltLrkr7ovzO94lpGvPXOVJ9A/vwQBS24E/8o4HcquBzv1+kp8/fcriilWzwZL+X3BpwGrbgP7pI9w4PkF5Xvv+UhTOHOV7rQj74T5szDg7EYdyBY+qQePb+N4oqywc/CLch0x/rGTj1R+2i1xRVlg++3kpFhUAEtuDjv6dXP4tkuUfCQ/LBk32KCoEIbMH/tqCbstRJqcEvkd75C07EFHwg7aW8cinBD49TXg60xRS8aesm5feTKSX4wHWKq4HGGM/AofReXl6eosqlBE8OYne93tiCj7ZRVLm04BPDFZUC7XGfiCGrtODDE5XVAs3xn4ghp7Tgy2UoKgXa4z8RQ05pwZO1QYpqgea4T8SQVWrwQ7HXVmfcJ2LIKjX4mg53MQDn4j4RQ1apwZM9DjNvwKkYt+rlT8SQVXrwCVHKqoHGGIPvXOuRWROV/ZKWHnyHOYqKgdbYgk+gXT699N9PpVcqmy1bwAv3p9EXW/DXX6mU3++Zq1KrlM6WtfJ6vNMqYZfiABZswWf37nm3apTDibQWSmfLWjy6f+bkk58zjxEEYAv+s+yr87pfkzz1SulsWYtVrQipdROXxtATW/A+YydV7ZEoebal4tmyZrsti9M92UYIQjBu1Vd86sW/SZ+Do3i2rNlun2YtKx7Gdr2e2IJvc4nepieln+owW/ZvBbNl5Xftv3l50Zwzmw/h9HodsQX/7dZapNaW3bLra8QW+0vNdlYrZWbLmi2dsHX7pH2z/8Y8StAcW/A3e5gXETekVtW36Evr17dfUcpbfbplsbSPzH4BcAa24Dd95E2850q+dz8ovAqe/YrSgre8yW+u8q0P6yhBcyzBZ2T8TH/9/jcqOQ8i7OzdQR2H0Y4Ot40tJfjRM33IoC/JFBXn7oJGWIJPMJti/m+U5Fr/pFvDOzn8vpcavGnkgW+n+pEmK1hHCZpjeqv3+/Dn++fmyt4DvNevPysLvlAGLoClG5bgK/383xkj3z9/RvYixtWXH3N8kCH4cbFlPgUEYQn+32ermZd+Pyvb48IQfOU9iiqChliCPzHG+mPECUWVGYInnzhsEoKTsAR/03bOVR/Jo3OyWIJvukxRSdAOS/AHbHeVmHxQUWWW4ElyLUU1QTMswQ/K62sipNvtlxRVZgq+33uKaoJmmL7OTX9wLv0Una/sADpT8N4HMH1SH2y7bNtO/mpqmMLKTMGTiQMUlgVtOPsuVPaq48q2+tA7eLICFzjVhe7BP4ZfeV3oHjxZgvMx9KB/8EG4BaEe9A+efB4uagggzwDBB6aJGgLIM0DwZEGEqDGALCMEH5iCSTVOp0HwqmbLljCt2AkZ/hNXJshcIB00xB28qtmydiocrF74x0q7n6sZuV/5dTRBIe7g1cyWddBjYeGfhljuYvv8eM5BQZm4g1czW9bRmsI7m7wXaV60ns05KCgTd/BqZss6qpNRcHi251S/kMrjX+QcFJSJO3g1s2UlvGM7r4+YvruSlvWzxLYiaIt/q95htmy3JKvjii6EWX5vU+vPx7/q9PKTn3TjHRSUhT9431BL6AGhRQ88Ypst+5Wyi9I/eqiy5cdblk2GDjN4BwVl4Q6++UWaN4GQaCWTJiX1+sKyHDXQvOg+mXNQUCbu4HcnNx52f7AGwZPZlt04DfbUIdV34sZ0wnEHf+cZ83ewq1U1CL78rsbmZeiO9NQnOccEZeMO/tSUCsT35Mr+/MGTxw5U4xwMMOMOfsC9u11J66u5GgRPuu6txDkaYMW/Vd9wTBNC6k52uA65iuDJwE2Kb28H6hjhsGwxoxaW/RzQgsGCJ3Mnaj0OkGS04E2zP9R6ICDFaMETMnYpPuedwHjBkyHrcdsS8QwYPBm8vaaWAwEpRgyePH7oaQ0HAlIMGTypsnoGDsmLZczgiWnizoaajQQkGDR4QlrsnVFBo5GABMMGT8q9dVDpRTiAnXGDJyTw829aajESkGDk4AlpuGRpB8an+nfEfh8ljB08Ia1WpPVjuYXJ/t//kztPiw49hdGDJ6R2wv73gsp60qyjhPhdx6xbdsYPnhDvqGXJI+uU+pQTlj0+C5Zq1aMEn0D3uneSKwRvVun5VelTOsrv1DkSY14sWaBhj3Y+SF207wVx5Z3PRYI38+kxJ2PTuMelN+FG/lKTtMttpm2PxQyYQyqX3+pO+5RcJ3iLmgPnpqfPHRrq57Am8dr1X4Zo32GhpEmZ6QenDhPXgXD2H1WGuDCCIuUff+lf2/dumfNaz2aO+YuyYaM38T851mn9aW7epXMXS/x/a4gLI6jg37r/259sSU/fsnj6+Nio1nUFH8L/enclUu//XhPbiUAjfg2r/tzvTYs9YowLI6hXqUl47NjJHy/bmmqWsnbxx1MnvDJi4LOREe0eDwkJCQzwL7sEi/ljd+zaMHmoNsV08EOcebGq+I4OARdGiCqYLbuTs7JypoDAZh0iBzw7fMSEd2ckJi1a+03KQwcyMzP3mH9+Z/6Zantox9pSrUoqsuXkwqTFl5Ynuars7UmjyWdLir1WAi6MUCXEavw7nJXFKxfgqEGIpIk/HjoyWHqVK/jyRIua9X4rfuN2cRdGGCh9f0KXVVnvAfDw+e78j1klbgAk4MIIBdwteNdm6v5ymxIPiPsej+ANDcF7KHHB9zidWcIfd/jlaVHkngY1/sjToIgWA7mbnanO6bqigre3W4MaI7U4TqLFQCImaVBkgwaXAwgUeURSGwjeHoJnhuDtIXh2CN65ELw9BM8MwdtzgeB3aVBj2EANimgxkHAtLtyxvgp/jXoOR0UNR4sJUeW0mEqpxUC8fDQooskUMcwzAwAAAAAAAACtPHEkeynvpIcsSukpvhKmo725B2OrwTeY/idvpzXnHEhBDQ1eFYH8c+b1PsV5G0HfB/ExMVFcJQYsp715B2OrwTeYhvc+6Ln9gi/XQApqaPCqiBSX402G5PDtcG1GgxuV5xtG2hFLaHyDsdXgG8yYuxVIY9qOayAFNTR4VUSadoiQUFqbq0Yfeode68E3Dl9LaJyDsdbgG0yTroQMy/PnGkhBDS1eFYEWpppHSltx1Rh8uEPVpOt8Ny+xhsY5GGsN3sH4Trw/gXsglhpavCoCTT9ASDvKf2JnQ9qRq701NM7BWGtwDqbViauDeQdiq8E5ENGGXvMig27wfcaPm0JIc9qYq4Y1NM7BWGvwDabO1VWWkzC4BlJQQ4tXRSD/3ISORzm36vs+GNt9xyG+SxBZQ+McjLUG32D+mds/Ojo6gGsgBTW0eFVE6nQsZwnv9/jxV26s5/y0sL1N8w3GVoNrMBupRSjXQApraPCqAAAAAAAAAAAAAAAAAAAAAAAAAACoctl6PiNdzTkFCFzNk5FRdFZkZM8ZLn1bElChHB1KSHQe6X3t85zDLxy/Nc9EIo7dTg7Ue1wgWFHw9LUap3OD+9CWQfmTI1KOG3cCA2jiYfCVydp1xI+GJ5yvUT2M1td7YCBWUfB/ErJ6EfGl4cus23theg8MxHIMPjGdkIAY593OGHThGHyb+6O7bvzJpPfAQCz74H9pS/qfuL0zRO9xAQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAgACPDB8BxhVXXlTwA7/S+98Gpcj4q7DgR4mqDBpYhOA9E4L3UAjeQyF4D4XgPRSCdwmtk3i0k6iI4F1C3Dsh6o0bIVFRg+C967Sq6+34MILXUNxLHI0HCQm+4pwcSmnubF/7FQheQwYMfvmpIaFN2g069qX9CgSvIQMGn9PL+qNTtv0KBK8hAwZ/dK7l890r4Yj9CgSvIQMG3ykn+1Dqges5T9ivUBN85bCWnMNxUwYMnvjHTVs4Lc7f4XEVwUdlfLRkW2XeAbkjIwbvYECK1fltShv6HTR/NYiaq/mA3IBLBF9g9mqlLdonmhem3QLG4vIMGHzvQvYrlAffcJF5UXE754Dcksrgg2hDImyrnhawX6E8eNPmKFLp8wGcA3JLRcFX+zgt9TnZp5kCS/5dZPCm5Ud8rexXKA+eVJt7eJ/8v8qTFQZv2vQ08f+q6Hcjfv/63IxmhIzKumv+EX15658k4tjt5EASfWXab+eeJn9S2kLYZ/yANOnHVQQPcgqDD15sXvglFz4cT8fVnnfWpwV9O/LgNhJNN/UJyp8ckXLcK5omVJ6fRdrTTuVdYOMOZBUGb7f9G/+riVSl3QIHmhqsyTQH/xeScL5G9TBaP5r6k755Qt/qZSF4DRUG73fI/JHao+gbb/xR8+L2i/5Lfr/xizn4e4Qss25vhUXnmze7EbzrK9q465HxwaKtRfu44q96k1o0/N2sNl4fmoPPIyQxnZCAGD/LHxG8G3j4da7EXu14Oqf75rM+s86EPpd1JtiSdpv7o7tu/MlUFHwfPwTvymS+x8dfTL55sDmplXbnh9FH3rCkTfqfuL0zxPrL3/M48dr2R1ME78rkgv+WpTGCd10I3kMZcF+9LASvIQTvoRC8h3Lz4Kt9jIM00tw7eOth2UU4LCvBvYMP+cK8qJhc5vM8kHsH32GmeWFKFzAWlzfgSIp6PwySqGik4Cvusxx6+peIwYA9IwVPemR8tGSr44naIIChgseECucxVvDgNAjeQyF4D4XgPZQmwVfq2NjL4UEEb2jcwZ+KIj2uU3qwrv0KBG9o3MHTmPLXPmvYfMe6okfqRVqt/6b40x6dtvIdTIA2EA2Cb0SrENI9t+iRzhOs9hef/Vj/u7DqMXt8OPsC7fAHHx9ypz0hr1+yX1HirX5Kd/NiQh/OvkA73MFnPaD0MJny4HX7FSWCXxJkXsSM4ewLtMO/Ve/brM9gMs7xl7lE8K8PNy8WdubtCzTjpO/xvikJz382X0Q3tRy/SAIDZ+3A8er+clsBnQy/kXNno4C67s+199w1uRFMyh2dI74j98MSfG4hRZWdEfz0DebF4xfEd+R+WIKPKaSosjOC/8DyNt/unPiO3I+Ct/rgNxRVdkbwwTnmt/oj08V35H7Ygm+2LTMz84zD5WpL5ZR99ZaNuw1O6Mf9sAV/4MDaXz7MGq2ospMO0gj6OlcjQEhZ42AL/o+IoBtez2cqquzKR+ceS12+ZktNvUchFFvwl98yZbduf0dRZVcOfkcjQjov13sUQrEFP4YOXJp1NlVRZRcOvop1n5B7z+xg3KpvGej/7qxaiiq7cPC+OyzLPTqPQizX3nMnypq+hIxy7yk9bMH/ZrVXUWVXDj5gwY6d0xyuzutW2IKPjY0dMuHqPxVVduXgPYCCt/rwnxVVRvCGpiD4uJuKKrt08GHvT3hM7zGIxf4Zf51+qqiyKwc/bP2TEalP6z0KoZg/42Njo5TtHHXh4E0Z5Qjxl7kOv5tgCb5agSqKKrtw8DXWWpbufVckluAL7zrzk6LKLhy86dvyhFR1neC77Erf6XDDxzKwBB8auuLCkLAXzn2oqLJd8C0jghQ119XgjU/1Semu9yhYNU6pWbV2WoiyRmyf8ZcsZ08/c1lR5RLBe61aNnH7PxS111Wr9yYE6z0GZu/Oyfx670fKTpNhPTpnORT/apb0Su86rep6Oz5cIviX3zQvVrUq+Fulzm1MSgYJpVqUXp74ZMxT2Igp+LfzV3ywPP9NqVUV5+SYP/5zZ5d++7EvLG9EA1+1/SXswOxFKdWVjRPkLd7uRbzSFc5aYAve1H/j9xv6Sf6WLj81JLRJu0HHvrRfUSL4mV3Mi9dtZ2t6fWf+etBxqbJxgrz4BXs+37vgbWWNWIKvXdr85pxe1h+dsoseiUqyOn4xKSmUNEmyLtdcWR36ZOZi659XnbQsrxRbiyXXct3F7oEdL61R1uoEw/XF6Iw8G6mVR+daPt+9Eh6eiVklxGpxckiIH6kQYlv23Lp3fqDtz213mJd+B4uvxZJrOfRAekaswlZrGpYd/JjO0TZSKzvlZB9KPXA9x+F7pPz3+JUxxHf238vuFwRiPEjTudYjsyZKH6D2j5u2cFqc4/Uo5YP3T/wug+eivKABtuATaJdPL/3Xcw7SeAC24K+/Uim/3zNXFVVG8IbGFnx27553q0Z50PF498cW/GfZV+d1v/ZN2U8sBsEbGlvwPmMnVe2RqGxWEYI3NMateq/6YX4Kd68jeENjCz7wR0pjflR24A/BGxpb8NtTutCAjTsUVUbwhsYW/J3u1SnpektRZQRvaGzBH59Ug5IxHnPqlSdgC/6p/As0/X4/RZURvKExbtUHJyxLbK2sMoI3NCWzZVsoqozgDY0l+PqbLyyu9vY3mX8qqozgDY0l+C3nZ+7Nub76E6lblMpD8IbGEvytV0lDqvjECQRvaEwzaZ4iJhqptDKCNzSm4CNt/ymD4A2NKfgZsbGW/2IVVUbwhqZk0iRVVBnBGxpL8L6FFFVG8IaGy515KNxi1EPhFqMeSsAtRgsgeEMTcIvRAgje0Jx0i1EwGgG3GH02xeqie18uzNU56RajYDTafI+fIXFhE/ngGySnZXyCW4rrS5vgqcQse/ngdzUhJG6KFv2CajoEX896sxf3vvGH8WkT/GcSd2ySDT7Qet2jPVr0C6rpsa9+j+WtfqaofoGJHsEHJW/fj407nelzdK5iOVG9AiMclvVQCN5DIXgPheA9FIL3UAjeQyF4D4XgPRSC91C6BN/ktb8ru1giaE6P4OPW9XphXzNR/QITHYKvcMibkAabRfULTHQIvukCy9J17uPonjQInuW+c8X5pZoXVZRdJhO0xh08233nSpg6p+kTad04+wU+3MGz3XeupF6zp2LbTmfcwTved64AvscbGnfwjvedK4DgDY07eBX3nQMD4N+qV37fOTAA7Kv3UAKCrxlptX695pVBO9zB9y5U9EinCVabkjgrg0j8W/VyF8EbOIqzMojEHbxp+RHpi+AheEPj/4wfIHPlCwRvaOK26hG8oYm7IgaCNzRxF0ZA8IaG4D2UuCtiIHhDw8adh0LwHgrBeygE76EQvIdC8B4KwXsoccH3OJ1Zwh93eORztf4jj6v5Pa7W+Xd5WnMO/XamjNMO95IRhW/O1LJHeVr3eZOrc76hfxnE0/qZCVydG2CmGoJXBcHztEbwOkLwqiB4ntYIXkcIXhXXD34XV+slXN8+eo7j6pxv6F9w/T/b4y2uzvmGrokKOrb2Kq9j5y48dAAAAAAAAADwKE8cyV7qMJOaWRal9JTKtqajvTn6t7VW23//k7fTmqvuvKC1ys5HXri5sS7vC8/NP2de71Oz1bb2fRAfExOlru2A5bS3+v5trdX23/DeBz23X/BV2XlBa5Wdt38w8n9OfsP5wvOLy/EmQ3IkrnjLpBkNbqR2z2XaEUt0avu3tVbb/5i7FUhj2k5l5wWtVXb+3nFCJl7hfOH5TTtESCitrbJ1H3qHXuuhsrGvJTrV/Vtbq+2/SVdChuX5q+y8oLXKzms1qNM7bQXnC89vYar5H0JbqWw9+HCHqknXK6lrbI1Odf/W1ur79514fwJH55bWqjtPoPQJzhee3/QDhLSjPIdWG9KO6hpao1Pdv7W16v5bnbg6WH3nttYqOy/nTUyjr5fjf+H5DL3mRQbdUPtRM24KIc1pY3WNrdGp7t/aWm3/da6uqkZUd17QWmXna1eYN/BoA74Xnp9/bkLHo6o3Lvs+GNt9xyEvdY2t0anu39pabf//zO0fHR0doLLzgtYqO4+99UJ48o8mvhdeA52O5SxR/3Vy/JUb69W+XdnerNX2b2utsv+N1gsAhqrsvLC1us5NCVdubgnhfeEBAAAAAAAAAAAAAAAAAAAAAAAAAEpx2Xa75NU6zkQBPTwZGUVnRUb2nFFZ75GAk5WjQwmJziO9r32ec/iF47fmmUjEsdvJgXqPCwQrCp6+VuN0bnAf2jIof3JEynGVkzvAVTwMvjJZu4740fCE8zWqh9H6eg8MxCoK/k9CVi8ivjR8mXV7L0zvgYFYjsEnphMSEOOn98BALMfg29wf3XXjTya9BwZi2Qf/S1vS/8TtnSF6jwsAAAAAAAAAAAAAAAAAAAAAAACM5/8BlT9AEcl83boAAAAASUVORK5CYII=" alt="plot of chunk unnamed-chunk-10"/> </p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAJACAMAAABWh4TIAAAC/VBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmnzsbAAAA/3RSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+vv8/v+3IrpgAAAACXBIWXMAAAsSAAALEgHS3X78AAAdqUlEQVR4nO3dB3wU1b4H8LMbCCGEkisdDEku0psQkBIvJZEo7QWIqBAMeilSRNAr+DBeIkoL9wqCoEFEmjRFeigptAQUI/BQgcejhqIXMAnVBOR83u6mkOzOJGfmzNmZ3f19Px8nsLPnfw77M7uzM3NmCAEAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAJf32IiRYFwx5UUFP+grvf9tUIq0vwoLfoyoyqCBJc4KPrBbTVE9gQpOCt706cY5KeNEdQXKOSn4V96xLL5uKaovUMxJwdu6efE1UX2BYk4KPr6DZTHmeVF9gWJOCr75ztqkxd7KovpyQ+0TtNNOor6ztuq77PxhdZCortzR6NeDtTJhpER9ZwXfcs3uhbVFdeWORr+gWanBOgbfNLUeabsfb/Xs3CT42V0sizeiRPXlhtwk+C+CyTgyaLSovtyQmwT/6lvkAFndSlRfbshNgjevXHFi9yRRXbkjNwmekKBV/UT15JbcJngSPUpUT27JfYJv+7GontyS+wTvu0tUT25Jk+ADaUOie/CWzXpgVzz4cpNTUiaVY2hkCij5d2MEv+UvorpyR8WDnznR7PXuDIknxR7YkJPWjJAxmfcsPyIvb/uThB27kxhAIq/M+O3cs+RPSlvoH/ysLqK6ckeW4M1t2+XLsC1C8v/S1vzoSbF0Qu0FZ71b0LfDD24nkXRz38C8qWFJx82RNK7ywkzSnnYqr3/wL0v1DzIswVeblW/OOevy3JyCv/o/elLsryZSlXYPGGRqsC7DEvxfSNz5GtVDaf1I6kf65Qp+q/eq06qul+PDDsG3m8vdlQcp/la/uwEhDaS2jWOPWhZ3XvZb9vvNq5bg7xOyglqFRuYR0kds8BXnZVt6ypnrY7/CIXjfnZxdeZTiwTc+8K9/728k8aTYa16kFu32TmYb84eW4HMJiU8lxD/K1/pHwcGvPDU0pEm7wce+tF/heF49NusVKPF1zrt9e2+pJ8XSeT22nPWecybkhcwzQda02zwY23XTz6ai4Pv6igo+u7ftR6cs+xWOwW+txtmXJ2H6Hh97MfHWweakVsrdH8ceedOaNhlw4s6uYNsvf6/jxLz9j6aigj863/r5bo47Yr/CMfjZnTn78iRswX/HUkpM8J2ysw4lp9/Ifsp+hWPwMcM5+/Ikhg+e+MXMWDwjxu/RAx0m22xcaP/M9h/x9uVBXGNfve9TlR79pUG4zYZN9s/y26FFXx7C8MHX3Za1vftNeqWF/Yq5ax2ei816doYPfsvxSYdpXNA3SfYrJILfVpWzMw9i+OBvR5LutCbpfdt+hUTw8R05O/Mghg/+f+f6vU+7kAln7FdIBD/s75ydeRDDBz/wAb3/9W/b7r1uv0Ii+A7/5uzMgxg+eNJoSBPTyFVDTPaPSwTvl8jbmeeIzkjSyo+DJeqLOywrETw54PC/B+jEucGvwIRZo3Bu8Jg9ZxjODT50pqjeQCHnBo+dtobh3OBJGrbuDMLJwa/E1p1BODn4Cdi6MwgnB/80tu4MwsnBY+vOKJwcPEnH1p0xODv4Vdi6MwZnBz8RW3fG4Ozg/4atO2NgCb5agSqKKksHj607g2AJnhb4WVFl6eDJQWzdGQJL8CEhqy4MDX3p3IfSq2Vmy8oE/1Ug++BAHLbP+Et9LYvnLkutkp0tKxP8m9i6MwS24C+PtSxGZ0qtkp0tKxN8N6lLeoDTsQX/dt6qD1bmvSW1Sna2rEzwPsnsgwNx2II3Ddj0w8b+kptlsrNlZYInyRUVDA9EYfweb64f6iu9OS47W1Yu+Old2UcHwrAFH/ATpVE/BUuuc5gt2zH/Gj0/bJeu9ex7igcJ2mMLfkdSF+q/SfoaNiZvUiGkRbHL79XLny37zQbpWlVwcr0RsAV/t0d1Sro6TI+zanQ0utV5Sv/H4eCL3Fs92SfsPsbAji344+/VoGSc5J67A8n1D25o1CzR4fdYNviP2isYHwjCFvwzeRdo6oP+UqtuP0duhxHS/Zb9Ctng+0t+LQTnYvw6FxS3Ir615KqUL8rvep+YZjB/nSPVN7IPD0RhC/7EfzeQWxV07veL9PT52w5XtJINnhwwy60Bp2EL/qOLdN8If+l15QdMWzx7uOO1LuSD/7Q54+BAHMYdOKYO8WcffKuosnzwg3EbMv2xnoFTf8xuel1RZfng661WVAhEYAs+9gd67bNwlnskPCIfPNmvqBCIwBb8b4u6K0udlBr8Mumdv+BETMEH0N7KK5cS/IgY5eVAW0zBm7ZtVn4/mVKCD/hacTXQGOMZOJTez83NVVS5lODJQeyu1xtb8JH5FFUuLfj4bopKgfa4T8SQVVrw3eKV1QLN8Z+IIae04MulKSoF2uM/EUNOacGT9YGKaoHmuE/EkFVq8MOw11Zn3CdiyCo1+JoOdzEA5+I+EUNWqcGTvQ4zb8CpGLfq5U/EkFV68HERyqqBxhiD71zrsTlTlP2Slh58h3mKioHW2IKPo10+vfSfT6VXKpstW8CM+9Poiy34G69Vyuv/3DWpVUpny9qYn+y0RtilOIAFW/BZfXrdqxrhcCKtldLZslaPH5g99eTnzGMEAdiC/yzr2oIe1yVPvVI6W9ZqTStCat3CpTH0xBa89/j3qvaMlzzbUvFsWYs91sXpXmwjBCEYt+orPvPy36TPwVE8W9Zij3ezlhUPY7teT2zBt7lE79CT0k91mC37t4LZsvK79t+6vGTemS2HcHq9jtiC/25bLVJr6x7Z9TWii/2lZjub1TKzZS2WT9624739c//GPErQHFvwt3paFmE3pVbVt+pH69e3X1HKW32qdbG8r8x+AXAGtuA3f+RFvOZLvnc/LLwKnv2K0oK3vslvqfKdN+soQXMswael/UJ//eE3KjkPIvTsvcEdh9OODreNLSX4sbO9yeAvyTQV5+6CRliCj7OYZvlvjORav4TbIzo5/L6XGrxpVPp3031Jk1WsowTNMb3V+374y4Nz82XvAd7711+UBV8oDRfA0g1L8JV++c+sUe+fPyN7EePqK485PsgQ/IToMp8CgrAE/++z1SxL31+U7XFhCL7yXkUVQUMswZ8YZ/sx8oSiygzBk08cNgnBSViCv5V/zlVfyaNzsliCb7pCUUnQDkvw6fl3lZh6UFFlluBJYi1FNUEzLMEPzu1nIqT7nVcUVWYKvv+7imqCZpi+zs18eC71FF2o7AA6U/Be6Zg+qQ+2XbZtp341PVRhZabgyZSBCsuCNpx9Fyp71XFlW33oHTxZhQuc6kL34J/Ar7wudA+eLMP5GHrQP/hA3IJQD/oHTz7vJmoIIM8AwQekiBoCyDNA8GRRmKgxgCwjBB+QhEk1TqdB8Kpmy5Ywo9gJGX5TVsfJXCAdNMQdvKrZsnYqHKxe+MdKe16oGX5A+XU0QSHu4NXMlnXQc3Hhn4Za72L74kTOQUGZuINXM1vW0brCO5u8G25ZtJ7LOSgoE3fwambLOqqTVnB4ttd03+DKE1/mHBSUiTt4NbNlJUzKP6+PmL6/kpL5i8S2ImiLf6veYbZs9wSb44ouhFl+X1Pbzye/6vTq05905x0UlIU/eJ8Qa+j+IUUPPJY/W/YrZRelf/xQZeuPf1g3GTrM4h0UlIU7+OYXae5kQiKVTJqU1PsL63LMIMuix1TOQUGZuIPfk9h4+IMhGgRP5lp34zTYW4dU34Ub0wnHHfzd5yzfwa5V1SD48rsbW5YhO1OTn+YcE5SNO/hT0yoQn5OrB/AHT55Ir8Y5GGDGHfzA+/e6ktbXcjQInnTdV4lzNMCKf6u+4bgmhNSd6nAdchXBk0GbFd/eDtQxwmHZYsYsLvs5oAWDBU/mT9F6HCDJaMGb5n6o9UBAitGCJ2T8cnzOO4HxgidDN+C2JeIZMHgyZEdNLQcCUowYPHny0LMaDgSkGDJ4UmXtLBySF8uYwRPTpO0NNRsJSDBo8IQE7ZhVQaORgATDBk/Mb6QrvQgHsDNu8IQEfP5tSy1GAhKMHDwhDZct78D4VL+O2O+jhLGDJ6TVqpT+LLcwOfD7/+Ys0KJDT2H04AmpHXfg3cCynjTnKCG+NzDrlp3xgyfEK2JF4qg6pT7lhHWPz6LlWvUowTvAve6d5ArBW1R6cU3qtI7yO3WORFkWyxZp2KOdD5KX7H9JXHnnc5HgLbx7zkvbPOFJ6U24UVdrknY5zbTtsZiB80jl8tvcaZ+S6wRvVXPQ/NTU+cNCfB3WxF+/cXWo9h0WSngvI/Xg9OHiOhDO/qPKEBdGUKT8k6/8a8e+rfNe79XMMX9RNm7yIn4nxzutP80tuHTuYon/bw1xYQQV/FoPePuTrampW5fOnBgd0bqu4EP43+ypROr93+tiOxFo5K+h1V/4vWmxR4xxYQT1KjXpFj1+6scrtiVbJK1f+vH0ya+NHPR8eFi7J4ODgwP8/couwWLh+J27N04dpk0xHfwYY1msKb6jQ8CFESIKZsvu4qysnMk/oFmH8IHPjxg5+Z1Z8QlL1n+b9Eh6RkbGXsvP7y0/k/Mf2rm+VGsSimw9uThh6aWVCa4qa0fCWPLZsmKvlYALI1QJtpk4ibOyeOX8HTUIljTlp0NHhkivcgVfnmhRs95vxW/cLu7CCIOk70/osirrPQAe3t+f/ymzxA2ABFwYoYC7Be/aTD1ebVPiAXHf4xG8oSF4DyUu+J6nM0r44y6/XC2K3Negxh+5GhTRYiD3sjLUOV1XVPD29mhQY5QWx0m0GEjYexoU2ajB5QACRB6R1AaCt4fgmSF4ewieHYJ3LgRvD8EzQ/D2XCD43RrUGD5IgyJaDKSbFhfu2FCFv0Y9h6OihqPFhKhyWkyl1GIgZm8NimgyRQzzzAAAAAAAAABAK08dyVrOO+khk1J6iq+E6Wgf7sHk1+AbzICTd1Kacw6koIYGr4pAftkL+pzivI2gz8PYqKgIrhIDV9I+vIPJr8E3mIb3P+i144IP10AKamjwqogUk+1Fhmbz7XBtRoMalecbRsoRa2h8g8mvwTeYcfcqkMa0HddACmpo8KqINOMQISG0NleNvvQuvd6Tbxw+1tA4B2OrwTeYJl0JGZ7rxzWQghpavCoCLU62jJS24qox5HCHqgk3+G5eYguNczC2GryD8ZnyYDL3QKw1tHhVBJqZTkg7yn9iZ0Pakau9LTTOwdhqcA6m1YlrQ3gHkl+DcyCiDbtuJoNv8n3GT5hGSHPamKuGLTTOwdhq8A2mzrU11pMwuAZSUEOLV0Ugv5y4jkc5t+r7PRzfY+chvksQ2ULjHIytBt9g/pkzIDIy0p9rIAU1tHhVROp0LHsZ7/f4iVdubuD8tMh/m+YbTH4NrsFsolYhXAMprKHBqwIAAAAAAAAAAAAAAAAAAAAAAAAAoMpl2/mMdC3nFCBwNU+HR9A54eG9Zrn0bUlAhXJ0GCGRuaTP9c+zD790/PYCEwk7dicxQO9xgWBFwdPXa5zOCepLWwbmTQ1LOm7cCQygiUfBVybrvya+tFvc+RrVQ2l9vQcGYhUF/ycha5cQH9pthW17L1TvgYFYjsHHpxLiH+W82xmDLhyDb/NgbNdNP5v0HhiIZR/81bZkwIk7u4L1HhcAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAI8NiIkWBcMeVFBT/oK73/bVCKtL8KC36MqMqggSUI3jMheA+F4D0UgvdQCN5DIXiX0DqBRzuJigjeJcRMClZvwkiJihoE71WnVV0vx4cRvIZiXuFoPFhI8BXnZVNKc+b62K9A8BoyYPArTw0NadJu8LEv7VcgeA0ZMPjs3rYfnbLsVyB4DRkw+KPzrZ/v5rgj9isQvIYMGHyn7KxDyek3sp+yX6Em+MqhLTmH46YMGDzxi5mxeEaMn8PjKoKPSPto2fbKvANyR0YM3kFUhs3VrUob+h60fDWImK/5gNyASwRfYO5apS3ax1sWpj0CxuLyDBh8n0L2K5QH33CJZVFxB+eA3JLK4ANpQyJsq54WsF+hPHjTlghS6fOBnANyS0XBV/s4JfkF2aeZAkr+XWTwppVHfGzsVygPnlSbf3i//L/KkxUGb9r8LPH7quh3I/bAhpy0ZoSMybxn+RF5edufJOzYncQAEnllxm/nniV/UtpC2Gf8wBTpx1UED3IKgw9aaln4JhY+HEsn1F5w1rsFfTv84HYSSTf3DcybGpZ03BxJ4yovzCTtaafyLrBxB7IKg7fb/o391USq0u4Bg0wN1mVYgv8LiTtfo3oorR9J/Ui/XKFv9bIQvIYKg/c9ZPlI7Vn0jTf2qGVx52W/Zb/fvGoJ/j4hK2zbW6GReZbNbgTv+oo27nqmfbBkW9E+rthrXqQW7fZOZhvzh5bgcwmJTyXEP8rX+kcE7wYefZ0rsVc7ls7rseWs95wzIS9kngmypt3mwdium342FQXf1xfBuzKZ7/GxFxNvHWxOaqXc/XHskTetaZMBJ+7sCrb98vc6Tszb/2iK4F2ZXPDfsTRG8K4LwXsoA+6rl4XgNYTgPRSC91BuHny1j3GQRpp7B287LLsEh2UluHfwwV9YFhUTy3yeB3Lv4DvMtixMqQLG4vIGHklS78fBEhWNFHzF/dZDT/8SMRiwZ6TgSc+0j5ZtczxRGwQwVPCYUOE8xgoenAbBeygE76EQvIfSJPhKHRubHR5E8IbGHfypCNLzBqUH69qvQPCGxh08jSp//bOGzXd+XfRIvXCbDd8Wf9rjM1ZPwgRoA9Eg+Ea0CiE9cooe6TzZ5kDx2Y/1vw+tHrXXm7Mv0A5/8LHBd9sT8sYl+xUl3uqn9bAsJvfl7Au0wx185kNKD5NpD9+wX1Ei+GWBlkXUOM6+QDv8W/U+zfoOIRMcf5lLBP/GCMticWfevkAzTvoe75MU9+JnC0V0U8vxiyQwcNYOHHOPV9sK6GTEzey7mwTUdX+uveeuyc0gUu7oPPEduR+W4HMKKarsjOBnbrQsnrwgviP3wxJ8VCFFlZ0R/AfWt/l258R35H4UvNUHvamosjOCD8q2vNUfmSm+I/fDFnyz7RkZGWccLldbKqfsq7du3G10Qj/uhy349PT1Vz/MHKuospMO0gj6OlfDX0hZ42AL/o+wwJvmFzMUVXblo3NPJK9ct7Wm3qMQii34y/8wZbVuf1dRZVcOfmcjQjqv1HsUQrEFP44OWp55NllRZRcOvoptn5B7z+xg3KpvGeD3zpxaiiq7cPA+O63LvTqPQizX3nMnyrp+hIxx7yk9bMH/ZrNPUWVXDt5/0c5dMxyuzutW2IKPjo4eOvnaPxVVduXgPYCCt/puvyiqjOANTUHwMbcUVXbp4EPfn/yE3mMQi/0z/gb9VFFlVw5++Ianw5Kf1XsUQjF/xkdHRyjbOerCwZvSyhHiJ3MdfjfBEny1AlUUVXbh4Gusty7d+65ILMEX3nXmZ0WVXTh403flCanqOsF32Z26y+GGj2VgCT4kZNWFoaEvnftQUWW74FuGBSpqrqshm57pm9RD71GwapxUs2rtlGBljdg+4y9Zz55+7rKiyiWCN69ZMSXxHUXtddXq3clBeo+B2TvzMr7Z95Gy02RYj85ZD8WPzpRe6VWnVV0vx4dLBP/qW5bF6lYFf6vUuY1JySChVEtSyxPvtAUKGzEF/3beqg9W5r0ltarivGzLx3/O3NJvP/aF9Y1o0Oj8v4Smz12SVF3ZOEHe0h1mYk5VOGuBLXjTgE0/bOwv+Vu68tTQkCbtBh/70n5FieBnd7Es3sg/W9P8veXrQcflysYJ8mIX7f1836K3lTViCb52afObs3vbfnTKKnokIsHm+MWEhBDSJMG2XHdlbcjTGUttf15z0rq8UmwtllzLry/2COh4aZ2yVicYri9GZ+Xmk1p5dL71890c9+hMzCrBNksTg4N9SYXg/GWvbfsWBuT/ue1Oy9L3YPG1WHIth6WnpkUrbLWuYdnBj+scmU9qZafsrEPJ6TeyHb5Hyn+PXx1FfOb+vex+QSDGgzSdaz02Z4r0AWq/mBmLZ8Q4Xo9SPni/+O/TeC7KCxpgCz6Odvn00n885yCNB2AL/sZrlfL6P3dNUWUEb2hswWf16XWvaoQHHY93f2zBf5Z1bUGP69+W/cRiELyhsQXvPf69qj3jlc0qQvCGxrhVb64f6qtw9zqCNzS24AN+ojTqJ2UH/hC8obEFvyOpC/XftFNRZQRvaGzB3+1RnZKutxVVRvCGxhb88fdqUDLOY0698gRswT+Td4GmPuivqDKCNzTGrfqguBXxrZVVRvCGpmS2bAtFlRG8obEEX3/LhaXV3v42409FlRG8obEEv/X87H3ZN9Z+InWLUnkI3tBYgr89mjSkik+cQPCGxjST5hliouFKKyN4Q2MKPjz/P2UQvKExBT8rOtr6X7Siygje0JRMmqSKKiN4Q2MJ3qeQosoI3tBwuTMPhVuMeijcYtRDCbjFaAEEb2gCbjFaAMEbmpNuMQpGI+AWo88n2Vx078uFuTon3WIUjEab7/GzJC5sIh98g8SU9E9wS3F9aRM8lZhlLx/87iaEjJ6mRb+gmg7B17Pd7MW9b/xhfNoE/5nEHZtkgw+wXfdorxb9gmp67Kvfa3mrj5ktql9gokfwgYk7DmDjTmf6HJ2rWE5Ur8AIh2U9FIL3UAjeQyF4D4XgPRSC91AI3kMheA+F4D2ULsE3ef3vyi6WCJrTI/iYzQNe2t9MVL/ARIfgKxzyIqTBFlH9AhMdgm+6yLp0nfs4uicNgme571xxvsmWRRVll8kErXEHz3bfuRKmz2v6VEp3zn6BD3fwbPedK6n33OnYttMZd/CO950rgO/xhsYdvON95wogeEPjDl7FfefAAPi36pXfdw4MAPvqPZSA4GuG22zYoHll0A538H0KFT3SabLN5gTOyiAS/1a93EXwBo3hrAwicQdvWnlE+iJ4CN7Q+D/jB8pc+QLBG5q4rXoEb2jiroiB4A1N3IURELyhIXgPJe6KGAje0LBx56EQvIdC8B4KwXsoBO+hELyHQvAeSlzwPU9nlPDHXR55XK3/yOVqfp+rdd49ntacQ7+TIeO0w71kROGbM7XicZ7Wfd/i6pxv6F8G8rR+bjJX5waYqYbgVUHwPK0RvI4QvCoInqc1gtcRglfF9YPfzdV6Gde3j14TuDrnG/oXXP/P9vwHV+d8Q9dEBR1bm8vr2LkLDx0AAAAAAAAAPMpTR7KWO8ykZpZJKT2lsq3paB+O/vNbq+1/wMk7Kc1Vd17QWmXnoy7c2lSX94Xn5pe9oM+puWpb+zyMjYqKUNd24EraR33/+a3V9t/w/ge9dlzwUdl5QWuVnbd/OOq/Tn7L+cLzi8n2IkOzJa54y6QZDWqkds9lyhFrdGr7z2+ttv9x9yqQxrSdys4LWqvs/N3jhEy5wvnC85txiJAQWltl6770Lr3eU2VjH2t0qvu3tVbbf5OuhAzP9VPZeUFrlZ3XalCnT8oqzhee3+Jkyz+EtlLZesjhDlUTblRS19gWner+ba3V9+8z5cFkjs6trVV3HkfpU5wvPL+Z6YS0ozyHVhvSjuoa2qJT3b+tter+W524NkR95/mtVXZezouYxt4ox//C8xl23UwG31T7UTNhGiHNaWN1jW3Rqe7f1lpt/3WuralGVHde0Fpl5+tXWTbwaAO+F56fX05cx6OqNy77PRzfY+chs7rGtuhU929rrbb/f+YMiIyM9FfZeUFrlZ1H336pW+JPJr4XXgOdjmUvU/91cuKVmxvUvl3lv1mr7T+/tcr+N9kuABiisvPC1uo6N8VdubU1mPeFBwAAAAAAAAAAAAAAAAAAAAAAAAAoxeX82yWv1XEmCujh6fAIOic8vNesynqPBJysHB1GSGQu6XP98+zDLx2/vcBEwo7dSQzQe1wgWFHw9PUap3OC+tKWgXlTw5KOq5zcAa7iUfCVyfqviS/tFne+RvVQWl/vgYFYRcH/ScjaJcSHdlth294L1XtgIJZj8PGphPhH+eo9MBDLMfg2D8Z23fSzSe+BgVj2wV9tSwacuLMrWO9xAQAAAAAAAAAAAAAAAAAAAAAAgPH8P9CbQG1QOu7JAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-9"/> </p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at @@ -524,42 +517,42 @@ models generally only implement SFO kinetics.</p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> -<pre><code class="r">m.L2.FOMC <- mkinfit(FOMC, FOCUS_2006_L2_mkin, quiet = TRUE) +<pre><code class="r">m.L2.FOMC <- mkinfit("FOMC", FOCUS_2006_L2_mkin, quiet = TRUE) par(mfrow = c(2, 1)) plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:43 2014 -## Date of summary: Thu Jul 17 12:37:43 2014 +## Date of fit: Mon Jul 21 09:14:31 2014 +## Date of summary: Mon Jul 21 09:14:31 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 39 model solutions performed in 0.235 s +## Fitted with method Marq using 35 model solutions performed in 0.199 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100 state -## alpha 1 deparm -## beta 10 deparm +## parent_0 93.95 state +## alpha 1.00 deparm +## beta 10.00 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_alpha 0.000 -Inf Inf -## log_beta 2.303 -Inf Inf +## value lower upper +## parent_0 93.950 -Inf Inf +## log_alpha 0.000 -Inf Inf +## log_beta 2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -600,62 +593,62 @@ experimental error has to be assumed in order to explain the data.</p> <p>Fitting the four parameter DFOP model further reduces the chi<sup>2</sup> error level. </p> -<pre><code class="r">m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, quiet = TRUE) +<pre><code class="r">m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, quiet = TRUE) plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> <p>Here, the default starting parameters for the DFOP model obviously do not lead to a reasonable solution. Therefore the fit is repeated with different starting parameters.</p> -<pre><code class="r">m.L2.DFOP <- mkinfit(DFOP, FOCUS_2006_L2_mkin, +<pre><code class="r">m.L2.DFOP <- mkinfit("DFOP", FOCUS_2006_L2_mkin, parms.ini = c(k1 = 1, k2 = 0.01, g = 0.8), quiet=TRUE) plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-13"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:44 2014 -## Date of summary: Thu Jul 17 12:37:44 2014 +## Date of fit: Mon Jul 21 09:14:31 2014 +## Date of summary: Mon Jul 21 09:14:31 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 54 model solutions performed in 0.423 s +## Fitted with method Marq using 43 model solutions performed in 0.241 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 1e+02 state -## k1 1e+00 deparm -## k2 1e-02 deparm -## g 8e-01 deparm +## parent_0 93.95 state +## k1 1.00 deparm +## k2 0.01 deparm +## g 0.80 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.0000 -Inf Inf -## log_k1 0.0000 -Inf Inf -## log_k2 -4.6052 -Inf Inf -## g_ilr 0.9803 -Inf Inf +## value lower upper +## parent_0 93.9500 -Inf Inf +## log_k1 0.0000 -Inf Inf +## log_k2 -4.6052 -Inf Inf +## g_ilr 0.9803 -Inf Inf ## ## Fixed parameter values: ## None ## ## Optimised, transformed parameters: ## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) -## parent_0 93.900 NA NA NA NA NA NA -## log_k1 4.960 NA NA NA NA NA NA +## parent_0 94.000 NA NA NA NA NA NA +## log_k1 6.160 NA NA NA NA NA NA ## log_k2 -1.090 NA NA NA NA NA NA ## g_ilr -0.282 NA NA NA NA NA NA ## @@ -666,8 +659,8 @@ plot(m.L2.DFOP) ## ## Backtransformed parameters: ## Estimate Lower Upper -## parent_0 93.900 NA NA -## k1 142.000 NA NA +## parent_0 94.000 NA NA +## k1 476.000 NA NA ## k2 0.337 NA NA ## g 0.402 NA NA ## @@ -678,7 +671,7 @@ plot(m.L2.DFOP) ## ## Estimated disappearance times: ## DT50 DT90 DT50_k1 DT50_k2 -## parent NA NA 0.00487 2.06 +## parent NA NA 0.00146 2.06 </code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the @@ -699,38 +692,38 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) <p>SFO model, summary and plot:</p> -<pre><code class="r">m.L3.SFO <- mkinfit(SFO, FOCUS_2006_L3_mkin, quiet = TRUE) +<pre><code class="r">m.L3.SFO <- mkinfit("SFO", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/> </p> +<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAFoCAMAAACMkBkOAAAC/VBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmnzsbAAAA/3RSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+vv8/v+3IrpgAAAACXBIWXMAAAsSAAALEgHS3X78AAATy0lEQVR4nO3dCVgTZ/4H8DeAiIjijdei4FEUz3oUK1YUrVWERVSsR6W2Qr3RrletB2grirZe1Xqf6FJPEEStEFTwbKuurbbbf9Wq3R5264V1Pdp5/pNAFZIJzrwz70yS+X6eZ+OSN+/7zua7SWaSd35DCAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAA6IlrrRa1XbXeCFBbucW3OI67vchD6w0BdW355rW2AW0Gndug9YaAum6Fmf/pcFPj7QCVnV1q+nx3STij9YaAujrcunki+9h/b72g9YaAyrxi5q6eG+NldX/V2DiwXzFlZCdv43AueqvW/9ugFPkNZMZu83AuepTMkYGltXKDt3k4h+DtmuzgrQ/nIrab/eufMkcGlmQHb304517ZbMUnMkcGlmQHb/NwblFq8b+8Jy5/w03mVKAg2cHbPJwrEXy1vMHtx+/FTzn2Q37wtpQIfmY4fzO7J6u5QDKVgt9Yj7/pO4bVXCCZ7OB7/8WyoUTwk/rzN8mdZc4FypG/V88VsWwoEbxX7pgeiVtkTgUKkh28YcsZDzPLhpJ79R7D3+tjkDmVrgw9f0gpXw4WGF/+Z3zfHOH7SwYPEo0coNhQg+IE7lRp5w6kQvA6heB1CsHrFILXKQSvUwhepxC8TiF4nVIk+PpcQ4LgHUuJ4N0DA93FdDL4lvwbwTue4sE3yvvoo7xGAg+anrfrdn5TQkZdu8//E/lD5h8k9Ny9LF8S+Z+5P19+hfzBcc0QvGMpHnxGPf7FmyHwoOnc+JrLLrk34yZ1O76PRHLp4fUfzgo9dN4lkkuosPwaacd1KIPgHQsffKWVq8zWXDfdXl9T+NfKyk8fNP0nA/HmuvhGG+p98jkffBWScKV6tWCubiTnRSIe4K3eAfHBu/gXanDSdHuyQdGfLk8fNP0sf3NvqNfG3+78yAf/iJDN5rURwZEPCemN4B1R8bf6Dya4uE5YKPCg6TdciQ8XMvVaK5f3+OAfEJJsJKRyP0/Tf0XwDql48G5TcnMnCy1On84t7rr3kvuC79oOuPadnyntVo9Hd077yvAk+HBPBO9YRB3HT7+adfd4IPHJ+f2L0WfeNqVNoi7eO+hvfvH3Ok9c9v2vCYJ3LOKCPylmKATvSBC8TuG7ep1C8DqF4HUKwesUgtcpBK9TCF6nRk5to5R3GQVvo84dgpelzTzltBYYX3bwNuvcIXi7xq7OHYK3awzq3BVB8HaNQZ27IgjerqlV5w7sDIM6dz0KFwWePyh3ZGBImeN4zxfKP/2jYuGawPU7lBgZGJEdfO3Mm/u63OH+08yyAW/1dk128HvPTz7NJfjtPGTZgODtmuzgCyJJF64GCSuwbEDwdk128P9e5JXIdSTjv7NsQPB2TXbwfR9zj3b8nHl/rGUDgrdr8vfqGw8OMMSlDLYqW4ng7Rp+ltUpBK9TCF6n1At+IKuJgIZ6wS8bxmomoKBe8K4ZnVhNBdKp+Blf5XgdVnOBZGru3LXMKctqMpBK1b36N5eymgykUvdwbiEuQGYv1A3eJb0Xq+lAGpW/wKl4IpDVfCCJ2t/cNT5ZWehuUJvqX9mGZ+DSwvZA/e/qh69lNSNIoMGPNIviWU0J4mkQvMuucFZzgmha/Cxb8WgAq0lBLE1+j/fNq8FqVhBJm4UYTXO8bDeCGjRagROyR6gYM6hHq6VXw1azmhdE0WzNXfI0VhODGJoFb1g7jtXMIIJ2q2xdd/VlNTU8m4bLq8vlYBGedrRcV1/zVGNWk8OzaHpCRcMTdVnNDs+g7Zk0jfJrspoeSqdxSdMWh7EuQxtalzTtmoEl15rQvKRp+M4yMrcAaKhf0tSlTtUSf/8dr3ktiAm+UpGKQo1SS5r6Htm67cDfit/Tbzt+sFGfmOC5Il8JNUotabqnJSGtd5W4a8w6F8GHAkNigm/bNuX714IHXn5PsNWqpGlQYXX8z/YJPdrdXOj0YMnP9YmrrCroAGPiPuOvm1bJ9fxBsM3gTsq2bVbs3dqn8IIY23YJPdrFaLrNtXiJx6/Ha15l4oL/YTR/M/KaUFPjs0NaXOG4f/lZNth4q18VQ8iwFZb3jse7vcrEBT/pYcqcLQ//IdSUl133+K7GTbOyLBtsBO8x/8jRJKuDfjJzEd7tVSUueENU2md7+ghGU9CTFIQS0uWuZYPE4kczlyJ5NYk8jnepG+wpHEzOujIHE4lhruwrVIxMwVGdisQF7/slx/X70l+oye/yb1e5b68UvGjZILnc2VvbkLx6xAW//1BHrnLaAcG2MlGzV88f7m11v/Q6d/EbcD6lasQF/3vXahzpbFWZvFQUBQ4HpZWT3AfoiAv+/IzqHBkj+M2dTTSVLaMPVZDeCWiIC777w+854+M+kkamKmkallf12Q8CBYg8nPNL2JzcUtrIdLVsI/Kq0XQDqcQFf/GdepJHpixi3P50Q6p+II244D+8yh2JlbhIirZ6dZNTLeg6ghQiv8AxtE++9Hi3pJGpy5b7HxO67DUoS+wKnLqjPuV+lTQyfb16n9xQ2q4glrjgp3/G3VjZTdoXazIuVOC5N466L4gjLvifV3SR/HWqmOA9E/MzXxa4323dPKnTgTSigvflwqSPLCb47a+61/zkFYEG1xWL8AM9U6KCN2SmV5E8sojg623mbypmCLbF7hNc2wkKEbkCh+MePXjwQNLIIoIPSjLd5go3vpyH6xowJC74yEKSRhYRfIVjZQh5aaWN1vZ5+CqHHdkLMWwS8xnf1zhjUbbNT5F6uZ2lTQniyV6IYZOow7ka4Z1K+Q3eYzPKpbAifyGGLUpccND1w2Ts3LNhVwsxBMTutl7bAwqwr4UYAoJOtVFkHCjJzhZiCPDL663MQFCcyL161RZiCPDaOQNL7hUnMvgXfaoumGZ9/ktpFLyadNynqHatNHHBJ3AdP77+y8eSRlbyMuKdTrdTbjAwERf8f0eUf9in5w1JIyt6/Xi/o9EKjgZig7/Zu9d97x5Wp8eVStHgidu8TVhzryRxwa+8eWNZ119VWnplw8Cj0r45hFKJC9593Azvl5OlrbaUEXyNCQkC39K3Pt6DekSwJHKvvlz3oS9JXINDH3yTo2EdPhaou1JuySoUyFKKuOBbXefucV9Lq4xGH/xO03v6IaFTamJzpK/vB0Higj+Z6UN8MmysmJBT0lSQeZ65HYSamuX1px0VShAX/F3TisjQO0JNMkuaCtlpKoOXJXwSnVtCKqrfKkFc8OkfuhLXpYI/y8otaSqg+cmwDiuEa6vx/p6P8y0UICb4/PwL3E+f/cxtE2qUXNJUhFqCe/V/qZ05GT/SyyYm+ATebP4/o4QapZY0VcLQnEbMxtYLUW/1nu9deHx5qfCKCKklTRXR/OhgdoPrg5jgy1/4Zd5biVe+E17oblXStNUUszzh9fLKKJu804fh8DogJvgPLlXibz0vLLb5iOpDiv3RsL9ZZprsjStNu1Nx+JVeBjHBXxxj/ifuolBjXZMIrq7VZYVYvtWbeK3agV/p6YkJ/m7hmqtwwV/n/vyrqLllA+vg+d2LEziplpqY4I+Zz3Qis44LNQZfuj8oaDgXFGTZwD54Un7xturMJ3FSYoIf9CCC/zjtcm+YYKvXqoLYDlavd1WC51/0h2PwSU9F1OFc0p+Xjd9wy209xWE/XdAqeOIWf6CpGvM4HXFf2T4/a+v7wbabq205Z32nOsET8lzWO7iOlXTaXmlSGeG5PQhp1QcLdKRwhuBJlTXrdmyYlvaOahM6AacInpC5/x7hQjajQJ54ThL8ukZjDrWPHqnijI7OSYJP6kh81p54U8UZHZ2TBN/AGODa6fOMBGlneemZkwRPWqSeXF7TEGOUVqdHx5wl+CLu8TkhGkzrgJwseEJ8t6bigF4EpwuekCapm2ppNLUDccLgCYk4PgVnWD6DUwZP3N48GYuL2JXKOYMnpNzE49FYhF0KZw2eEO/EvAhtt8CuOW/whFSdnytUEh1MnDl4Qnw+yOmp9TbYKecOno9+oRHRC3H24AmpscDYS+ttsEPOHzwffbKxD/bwLegheEIqxOfHl9d6I+yLPoInxHPMsUm4yE0xegmeELdBh+f5ar0R9kM/wRNi6HFwTyetN8Je6Cl4XsuN+QOxCt9EZ8ETUmvO2Rk4y1aHwRNSNubYZqtTPHVHh8HzgjYdj9X54Z0+gyek6sTTS5povRFa0mvw/D5+SErWAPG1cZuH1me3LRpQIHjFS5qqpkp87sIAUY90SdkwLW0q481RlezgGZQ0VVOHdQeGi/hG7w3TJS+d6tw82cEzKGmqLq+YrE3dS7nOqdk605Jtpzo3T3bwLEqaqs0/8fjC0i+ultSRv4mPUmdzVCE7eC1KmirPJXTzsclWFdueMp+bl+up3gYxJzt4TUqasuARlXogVrhWOik6N0/NzWFN/l69VUnTvypbpssdWXU1RudkDtXJj7fyg/doawq9ctsnd7SOM8vOlDuyFv42IXtntDO9pdsiO/jAq9yDKYREalDZkpHaozNTB3g9+3GOTXbwuVnPDX882JmC59WISz80to7WW8GU7OB/70nIuze8nSt4XpVhe3MnOfH1EGQH/83sssTj621RzhY8r2K/9blJHZ/13Y6Dkh1830f3O5OWN247YfA8l3ZzcrcOsnmQ58Dk79U3HBPA7xDNsrosgVMEb+I//oBxWitnq5Ws359lpfDqs/rU5sGsV2xVe74K4xmKQfBiBYzLyJvXleHFbcceXpC+RLU3FgQvgXuXpCP7p7Zns7sXuNOFkMRXmYwtAMFLVDlySX56fAvlX5kjovmbRqsVH9cGBE/B59VV+Wlvt1O2ys7AEfxNULKiY5YCwVPy6bfkaNb0l5SroVolP5DUOqjaIh8EL4N3WFJOzvuvCF+CUzL/VOM+wWunM4HgZSoXMjMzb82wJnZ+nN885dNlJdYTIHgFGJq+sTYvY8Yr1bTeEJsaHAt0Dc0v/nMzgldKpR7vpuV/MjGkgtYbIsR8KakJxUt7I3hF1Y6Ysy9v09gge6uousGPv+lf/DrwCF55ftELDh5NmRhqR7/tjDCfF9Cm2D0InhHfiFl7jqS/P6C5XZyO77p13bi0d4vfg+BZqtRpxPJDRzaOD9H+xd/a4rp8CJ49/6jEXUcOLHqriz2tz0bwKvFo/WpCam7OytFda2u9KWYIXlVuAf1nbsvNTUkc2lHjC6AjeC34dhv90YFTR9ZNi25TSaNNQPDacfPvPmLBbqNx25zXO6n+/o/gtVczOGb2VqNxz8JRvZupdgIXgrcb3s/3e3tJutG4d+k/+rdnfgCA4O2OV2DYyKRt2cYDaxNie7di9Q0Agrdb7v6dBk74cLsxJ2ttwlvhQfWVPZUTwds/D//gAeMXphz54uThLQviozs2UOKMTgTvSNzrtAkfkbgmPTsnN3PDvPj+HetT/38AwTso76bdhkxIWp+Rd+qLo7uWJ4zu91ITH3cJ/RG84/OoFRjcZ+TMZduPnP7iROaGBe+MiO7exu8Z3wwheCdTsX7r0H5xU+ev2ZnN27/1o9kTYiKCW/lXtTgHCME7tTI+AS+GvTYuYcmWzGklWxC8TiF4nULwOoXgdUrXZcv1TO9ly3WLQdnyOt3Mdu2WOTKwxKBs+YtTzNJXyhwZWGJXtjx6lMCjwV6wK1uO4O0ag7LlRRC8XWN3HI/g7RqC1ykEr1MIXqfYBf/yt5+X8L/f5XmE/rIUlIzjW9XO3clFf4fur9nE6K9tf80mRn9t+2s2Mfpr21+zidFf2/6aTYz+2van9in6O3R/anIv6oH+2vYHAAAAAAAAAKf3wpmbm2iv0Rb19b2cQFlDBP9ZV0Z/v+y7RxvJ6N/n67tptaj7G872Jk86U4xR2F/+c0jH69ay3t8souvb8NGcXvu/95AxhNclri79JrhdXt8z+4yBun/jx4v7nM+gnb/vFq73k+ePYozC/vKfQ0oxt1zJa7forsY75n5Z8hzXRsYQq+7zwVP3j/qtDKk7xJ26/2DOi0y+STt/zhlTcEWdKcYo7C//OaQ09wQhbTm6Aq0BnQkZ/sCLfoiwgkF88NT9Z189ePdYc/r+AY8SQ07vo+7vYQquqDPNGOb+sp9DWquz+ck52gunekx7PIV+iGo/xQbxwVP3X83NDt19uSx1f8MOjuNCqOc3B1fUmWYMc3+5zyG1pGOEtOEoF3a2uHhjsIwhtlzoMJyLqE7df/7/8Z/TXFvq/nG/BNdIvONJ298cXFFnmjEKg5f5HFJ7/VcXMugO3YdLrRv/rCRniHOcyRDq/sOvGEgTrhl1/5RM/sCAe4G2vzm4os40Y5j7y30OqXndTgg6S7k7OfN2VGRkZGU5Q5je6qn7exe8H5J13o26/5D78b12/Fietr85uKLONGOY+yvwHFLqcO7WRsoDyDTzK7atnCFMwdP3Dz5bsN+Pvr9h3OV7Oc2p+xe+VRd1phjD3F+B5xAAAAAAAAAAAAAAAAAAAAAAAAAAwNIP5iWKXKqq556A9jp168Et6Nat17wKWm8JqMyNe52QyAek969rbp0eeL5gmYGEnruX5av1dgFjT4Lnxlb/9rZfONe8/sNZoYfOu2i9YcDW0+ArkO07iCcXknClerVg0/kZ4MyeBP8HIalriQcXstm8vxes9YYBW9bBJxsJqdzPU+sNA7asg2/1eHTntK8MWm8YsGUZ/I/Pk6iL9w76a71dAAAAAAAAAAAAAAAAAAAAAAAAYH/+H+/EK+pEfMJCAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-14"/> </p> <pre><code class="r">summary(m.L3.SFO) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:45 2014 -## Date of summary: Thu Jul 17 12:37:45 2014 +## Date of fit: Mon Jul 21 09:14:32 2014 +## Date of summary: Mon Jul 21 09:14:32 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.241 s +## Fitted with method Marq using 44 model solutions performed in 0.242 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100.0 state +## parent_0 97.8 state ## k_parent_sink 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_k_parent_sink -2.303 -Inf Inf +## value lower upper +## parent_0 97.800 -Inf Inf +## log_k_parent_sink -2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -770,7 +763,7 @@ plot(m.L3.SFO) ## ## Data: ## time variable observed predicted residual -## 0 parent 97.8 74.87 22.9273 +## 0 parent 97.8 74.87 22.9274 ## 3 parent 60.0 69.41 -9.4065 ## 7 parent 51.0 62.73 -11.7340 ## 14 parent 43.0 52.56 -9.5634 @@ -785,40 +778,40 @@ does not fit very well. </p> <p>The FOMC model performs better:</p> -<pre><code class="r">m.L3.FOMC <- mkinfit(FOMC, FOCUS_2006_L3_mkin, quiet = TRUE) +<pre><code class="r">m.L3.FOMC <- mkinfit("FOMC", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-16"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-15"/> </p> <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:45 2014 -## Date of summary: Thu Jul 17 12:37:45 2014 +## Date of fit: Mon Jul 21 09:14:32 2014 +## Date of summary: Mon Jul 21 09:14:32 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.208 s +## Fitted with method Marq using 26 model solutions performed in 0.143 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100 state -## alpha 1 deparm -## beta 10 deparm +## parent_0 97.8 state +## alpha 1.0 deparm +## beta 10.0 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_alpha 0.000 -Inf Inf -## log_beta 2.303 -Inf Inf +## value lower upper +## parent_0 97.800 -Inf Inf +## log_alpha 0.000 -Inf Inf +## log_beta 2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -858,42 +851,42 @@ plot(m.L3.FOMC) <p>Fitting the four parameter DFOP model further reduces the chi<sup>2</sup> error level considerably:</p> -<pre><code class="r">m.L3.DFOP <- mkinfit(DFOP, FOCUS_2006_L3_mkin, quiet = TRUE) +<pre><code class="r">m.L3.DFOP <- mkinfit("DFOP", FOCUS_2006_L3_mkin, quiet = TRUE) plot(m.L3.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-17"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-16"/> </p> <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:46 2014 -## Date of summary: Thu Jul 17 12:37:46 2014 +## Date of fit: Mon Jul 21 09:14:32 2014 +## Date of summary: Mon Jul 21 09:14:32 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.338 s +## Fitted with method Marq using 37 model solutions performed in 0.21 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 1e+02 state -## k1 1e-01 deparm -## k2 1e-02 deparm -## g 5e-01 deparm +## parent_0 97.80 state +## k1 0.10 deparm +## k2 0.01 deparm +## g 0.50 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_k1 -2.303 -Inf Inf -## log_k2 -4.605 -Inf Inf -## g_ilr 0.000 -Inf Inf +## value lower upper +## parent_0 97.800 -Inf Inf +## log_k1 -2.303 -Inf Inf +## log_k2 -4.605 -Inf Inf +## g_ilr 0.000 -Inf Inf ## ## Fixed parameter values: ## None @@ -936,10 +929,15 @@ and the correlation matrix suggest that the parameter estimates are reliable, an the DFOP model can be used as the best-fit model based on the chi<sup>2</sup> error level criterion for laboratory data L3.</p> +<p>This is also an example where the standard t-test for the parameter <code>g_ilr</code> is +misleading, as it tests for a significant difference from zero. In this case, +zero appears to be the correct value for this parameter, and the confidence +interval for the backtransformed parameter <code>g</code> is quite narrow.</p> + <h2>Laboratory Data L4</h2> <p>The following code defines example dataset L4 from the FOCUS kinetics -report, p. 293</p> +report, p. 293:</p> <pre><code class="r">FOCUS_2006_L4 = data.frame( t = c(0, 3, 7, 14, 30, 60, 91, 120), @@ -949,38 +947,38 @@ FOCUS_2006_L4_mkin <- mkin_wide_to_long(FOCUS_2006_L4) <p>SFO model, summary and plot:</p> -<pre><code class="r">m.L4.SFO <- mkinfit(SFO, FOCUS_2006_L4_mkin, quiet = TRUE) +<pre><code class="r">m.L4.SFO <- mkinfit("SFO", FOCUS_2006_L4_mkin, quiet = TRUE) plot(m.L4.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-19"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-18"/> </p> <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:46 2014 -## Date of summary: Thu Jul 17 12:37:46 2014 +## Date of fit: Mon Jul 21 09:14:33 2014 +## Date of summary: Mon Jul 21 09:14:33 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.127 s +## Fitted with method Marq using 20 model solutions performed in 0.109 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100.0 state +## parent_0 96.6 state ## k_parent_sink 0.1 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_k_parent_sink -2.303 -Inf Inf +## value lower upper +## parent_0 96.600 -Inf Inf +## log_k_parent_sink -2.303 -Inf Inf ## ## Fixed parameter values: ## None @@ -1022,42 +1020,42 @@ plot(m.L4.SFO) <p>The chi<sup>2</sup> error level of 3.3% as well as the plot suggest that the model fits very well. </p> -<p>The FOMC model for comparison</p> +<p>The FOMC model for comparison:</p> -<pre><code class="r">m.L4.FOMC <- mkinfit(FOMC, FOCUS_2006_L4_mkin, quiet = TRUE) +<pre><code class="r">m.L4.FOMC <- mkinfit("FOMC", FOCUS_2006_L4_mkin, quiet = TRUE) plot(m.L4.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-20"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-19"/> </p> <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Thu Jul 17 12:37:46 2014 -## Date of summary: Thu Jul 17 12:37:46 2014 +## Date of fit: Mon Jul 21 09:14:33 2014 +## Date of summary: Mon Jul 21 09:14:33 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.355 s +## Fitted with method Marq using 48 model solutions performed in 0.26 s ## ## Weighting: none ## ## Starting values for parameters to be optimised: ## value type -## parent_0 100 state -## alpha 1 deparm -## beta 10 deparm +## parent_0 96.6 state +## alpha 1.0 deparm +## beta 10.0 deparm ## ## Starting values for the transformed parameters actually optimised: -## value lower upper -## parent_0 100.000 -Inf Inf -## log_alpha 0.000 -Inf Inf -## log_beta 2.303 -Inf Inf +## value lower upper +## parent_0 96.600 -Inf Inf +## log_alpha 0.000 -Inf Inf +## log_beta 2.303 -Inf Inf ## ## Fixed parameter values: ## None diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf index 9cf1b3e5..b69ddddc 100644 Binary files a/vignettes/mkin.pdf and b/vignettes/mkin.pdf differ -- cgit v1.2.1 From 4eb6682cf4e25cfe4d58a49c8632307a7cac1ca4 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Thu, 24 Jul 2014 14:42:00 +0200 Subject: Update vignettes with 0.9-32 just released to CRAN --- vignettes/FOCUS_L.html | 72 +++++++++++++++++++++++-------------------------- vignettes/FOCUS_Z.pdf | Bin 214130 -> 214013 bytes vignettes/mkin.pdf | Bin 160326 -> 160326 bytes 3 files changed, 33 insertions(+), 39 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 614fcf32..ab7ccaee 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -193,13 +193,7 @@ hr { report, p. 284:</p> <pre><code class="r">library("mkin") -</code></pre> - -<pre><code>## Loading required package: minpack.lm -## Loading required package: rootSolve -</code></pre> - -<pre><code class="r">FOCUS_2006_L1 = data.frame( +FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -223,8 +217,8 @@ summary(m.L1.SFO) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:29 2014 -## Date of summary: Mon Jul 21 09:14:29 2014 +## Date of fit: Thu Jul 24 10:32:09 2014 +## Date of summary: Thu Jul 24 10:32:09 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent @@ -315,7 +309,7 @@ summary(m.L1.SFO) <pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-5"/> </p> <p>For comparison, the FOMC model is fitted as well, and the chi<sup>2</sup> error level is checked.</p> @@ -326,15 +320,15 @@ summary(m.L1.FOMC, data = FALSE) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:30 2014 -## Date of summary: Mon Jul 21 09:14:30 2014 +## Date of fit: Thu Jul 24 10:32:10 2014 +## Date of summary: Thu Jul 24 10:32:11 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.32 s +## Fitted with method Marq using 53 model solutions performed in 0.321 s ## ## Weighting: none ## @@ -420,15 +414,15 @@ summary(m.L2.SFO) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:30 2014 -## Date of summary: Mon Jul 21 09:14:30 2014 +## Date of fit: Thu Jul 24 10:32:11 2014 +## Date of summary: Thu Jul 24 10:32:11 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 29 model solutions performed in 0.155 s +## Fitted with method Marq using 29 model solutions performed in 0.196 s ## ## Weighting: none ## @@ -502,7 +496,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at @@ -523,22 +517,22 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:31 2014 -## Date of summary: Mon Jul 21 09:14:31 2014 +## Date of fit: Thu Jul 24 10:32:11 2014 +## Date of summary: Thu Jul 24 10:32:11 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 35 model solutions performed in 0.199 s +## Fitted with method Marq using 35 model solutions performed in 0.223 s ## ## Weighting: none ## @@ -616,15 +610,15 @@ plot(m.L2.DFOP) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:31 2014 -## Date of summary: Mon Jul 21 09:14:31 2014 +## Date of fit: Thu Jul 24 10:32:12 2014 +## Date of summary: Thu Jul 24 10:32:12 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 43 model solutions performed in 0.241 s +## Fitted with method Marq using 43 model solutions performed in 0.271 s ## ## Weighting: none ## @@ -703,15 +697,15 @@ plot(m.L3.SFO) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:32 2014 -## Date of summary: Mon Jul 21 09:14:32 2014 +## Date of fit: Thu Jul 24 10:32:14 2014 +## Date of summary: Thu Jul 24 10:32:14 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.242 s +## Fitted with method Marq using 44 model solutions performed in 0.251 s ## ## Weighting: none ## @@ -789,15 +783,15 @@ plot(m.L3.FOMC) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:32 2014 -## Date of summary: Mon Jul 21 09:14:32 2014 +## Date of fit: Thu Jul 24 10:32:14 2014 +## Date of summary: Thu Jul 24 10:32:14 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.143 s +## Fitted with method Marq using 26 model solutions performed in 0.154 s ## ## Weighting: none ## @@ -862,15 +856,15 @@ plot(m.L3.DFOP) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:32 2014 -## Date of summary: Mon Jul 21 09:14:32 2014 +## Date of fit: Thu Jul 24 10:32:14 2014 +## Date of summary: Thu Jul 24 10:32:14 2014 ## ## Equations: ## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.21 s +## Fitted with method Marq using 37 model solutions performed in 0.228 s ## ## Weighting: none ## @@ -958,15 +952,15 @@ plot(m.L4.SFO) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:33 2014 -## Date of summary: Mon Jul 21 09:14:33 2014 +## Date of fit: Thu Jul 24 10:32:15 2014 +## Date of summary: Thu Jul 24 10:32:15 2014 ## ## Equations: ## [1] d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.109 s +## Fitted with method Marq using 20 model solutions performed in 0.141 s ## ## Weighting: none ## @@ -1033,15 +1027,15 @@ plot(m.L4.FOMC) <pre><code>## mkin version: 0.9.32 ## R version: 3.1.1 -## Date of fit: Mon Jul 21 09:14:33 2014 -## Date of summary: Mon Jul 21 09:14:33 2014 +## Date of fit: Thu Jul 24 10:32:15 2014 +## Date of summary: Thu Jul 24 10:32:15 2014 ## ## Equations: ## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 48 model solutions performed in 0.26 s +## Fitted with method Marq using 48 model solutions performed in 0.296 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index 43f3e2e2..f7b0a65a 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf index b69ddddc..0cc413a1 100644 Binary files a/vignettes/mkin.pdf and b/vignettes/mkin.pdf differ -- cgit v1.2.1 From 37a252cb44fed78c4f7a00a2f7874f1c47456468 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Tue, 19 Aug 2014 17:55:06 +0200 Subject: Improve formatting of differential equations in output Rebuild of FOCUS_Z vignette with improved formatting --- vignettes/FOCUS_Z.Rnw | 1 + vignettes/FOCUS_Z.pdf | Bin 214013 -> 220196 bytes 2 files changed, 1 insertion(+) (limited to 'vignettes') diff --git a/vignettes/FOCUS_Z.Rnw b/vignettes/FOCUS_Z.Rnw index 5b0ee79e..e2a2473e 100644 --- a/vignettes/FOCUS_Z.Rnw +++ b/vignettes/FOCUS_Z.Rnw @@ -20,6 +20,7 @@ <<include=FALSE>>= require(knitr) opts_chunk$set(engine='R', tidy=FALSE) +options(width=70) @ \title{Example evaluation of FOCUS dataset Z} diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index f7b0a65a..210ce099 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ -- cgit v1.2.1 From f30472ecd2afea6bd2153b8ad2bb2f663f3a2742 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Mon, 25 Aug 2014 10:39:40 +0200 Subject: Bug fix and unit tests for mkinerrmin See NEWS.md for details --- vignettes/FOCUS_L.html | 112 +++++++++++++++++++++++++++---------------------- vignettes/FOCUS_Z.pdf | Bin 220196 -> 220177 bytes 2 files changed, 61 insertions(+), 51 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index ab7ccaee..2dd186de 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -193,7 +193,13 @@ hr { report, p. 284:</p> <pre><code class="r">library("mkin") -FOCUS_2006_L1 = data.frame( +</code></pre> + +<pre><code>## Loading required package: minpack.lm +## Loading required package: rootSolve +</code></pre> + +<pre><code class="r">FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -215,17 +221,17 @@ given in the FOCUS report. </p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:09 2014 -## Date of summary: Thu Jul 24 10:32:09 2014 +## Date of fit: Mon Aug 25 10:34:14 2014 +## Date of summary: Mon Aug 25 10:34:14 2014 ## ## Equations: -## [1] d_parent = - k_parent_sink * parent +## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 14 model solutions performed in 0.081 s +## Fitted with method Marq using 14 model solutions performed in 0.083 s ## ## Weighting: none ## @@ -318,17 +324,17 @@ is checked.</p> summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:10 2014 -## Date of summary: Thu Jul 24 10:32:11 2014 +## Date of fit: Mon Aug 25 10:34:17 2014 +## Date of summary: Mon Aug 25 10:34:17 2014 ## ## Equations: -## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent +## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.321 s +## Fitted with method Marq using 53 model solutions performed in 0.3 s ## ## Weighting: none ## @@ -412,17 +418,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:11 2014 -## Date of summary: Thu Jul 24 10:32:11 2014 +## Date of fit: Mon Aug 25 10:34:17 2014 +## Date of summary: Mon Aug 25 10:34:17 2014 ## ## Equations: -## [1] d_parent = - k_parent_sink * parent +## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 29 model solutions performed in 0.196 s +## Fitted with method Marq using 29 model solutions performed in 0.184 s ## ## Weighting: none ## @@ -522,17 +528,17 @@ mkinresplot(m.L2.FOMC) <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:11 2014 -## Date of summary: Thu Jul 24 10:32:11 2014 +## Date of fit: Mon Aug 25 10:34:17 2014 +## Date of summary: Mon Aug 25 10:34:17 2014 ## ## Equations: -## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent +## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 35 model solutions performed in 0.223 s +## Fitted with method Marq using 35 model solutions performed in 0.2 s ## ## Weighting: none ## @@ -608,17 +614,19 @@ plot(m.L2.DFOP) <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:12 2014 -## Date of summary: Thu Jul 24 10:32:12 2014 +## Date of fit: Mon Aug 25 10:34:18 2014 +## Date of summary: Mon Aug 25 10:34:18 2014 ## ## Equations: -## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 43 model solutions performed in 0.271 s +## Fitted with method Marq using 43 model solutions performed in 0.26 s ## ## Weighting: none ## @@ -695,17 +703,17 @@ plot(m.L3.SFO) <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:14 2014 -## Date of summary: Thu Jul 24 10:32:14 2014 +## Date of fit: Mon Aug 25 10:34:18 2014 +## Date of summary: Mon Aug 25 10:34:18 2014 ## ## Equations: -## [1] d_parent = - k_parent_sink * parent +## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.251 s +## Fitted with method Marq using 44 model solutions performed in 0.252 s ## ## Weighting: none ## @@ -781,17 +789,17 @@ plot(m.L3.FOMC) <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:14 2014 -## Date of summary: Thu Jul 24 10:32:14 2014 +## Date of fit: Mon Aug 25 10:34:19 2014 +## Date of summary: Mon Aug 25 10:34:19 2014 ## ## Equations: -## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent +## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.154 s +## Fitted with method Marq using 26 model solutions performed in 0.148 s ## ## Weighting: none ## @@ -854,17 +862,19 @@ plot(m.L3.DFOP) <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:14 2014 -## Date of summary: Thu Jul 24 10:32:14 2014 +## Date of fit: Mon Aug 25 10:34:19 2014 +## Date of summary: Mon Aug 25 10:34:19 2014 ## ## Equations: -## [1] d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * time))) * parent +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.228 s +## Fitted with method Marq using 37 model solutions performed in 0.236 s ## ## Weighting: none ## @@ -950,17 +960,17 @@ plot(m.L4.SFO) <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:15 2014 -## Date of summary: Thu Jul 24 10:32:15 2014 +## Date of fit: Mon Aug 25 10:34:19 2014 +## Date of summary: Mon Aug 25 10:34:19 2014 ## ## Equations: -## [1] d_parent = - k_parent_sink * parent +## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.141 s +## Fitted with method Marq using 20 model solutions performed in 0.123 s ## ## Weighting: none ## @@ -1025,17 +1035,17 @@ plot(m.L4.FOMC) <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.32 +<pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Thu Jul 24 10:32:15 2014 -## Date of summary: Thu Jul 24 10:32:15 2014 +## Date of fit: Mon Aug 25 10:34:20 2014 +## Date of summary: Mon Aug 25 10:34:20 2014 ## ## Equations: -## [1] d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent +## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 48 model solutions performed in 0.296 s +## Fitted with method Marq using 48 model solutions performed in 0.281 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index 210ce099..ca6d2506 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ -- cgit v1.2.1 From 587bdfc102dbaa2c882fb0c008d28a3aea1d74d8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Sat, 11 Oct 2014 11:18:01 +0200 Subject: Updated vignettes by building static documentation --- vignettes/FOCUS_L.html | 208 ++++++++++++++++++++++++++----------------------- vignettes/FOCUS_Z.pdf | Bin 220177 -> 213325 bytes vignettes/mkin.pdf | Bin 160326 -> 160333 bytes 3 files changed, 111 insertions(+), 97 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 2dd186de..c0430358 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -5,6 +5,18 @@ <title>Example evaluation of FOCUS Laboratory Data L1 to L3</title> +<script type="text/javascript"> +window.onload = function() { + var imgs = document.getElementsByTagName('img'), i, img; + for (i = 0; i < imgs.length; i++) { + img = imgs[i]; + // center an image if it is the only element of its parent + if (img.parentElement.childElementCount === 1) + img.parentElement.style.textAlign = 'center'; + } +}; +</script> + <!-- Styles for R syntax highlighter --> <style type="text/css"> pre .operator, @@ -13,19 +25,21 @@ } pre .literal { - color: rgb(88, 72, 246) + color: #990073 } pre .number { - color: rgb(0, 0, 205); + color: #099; } pre .comment { - color: rgb(76, 136, 107); + color: #998; + font-style: italic } pre .keyword { - color: rgb(0, 0, 255); + color: #900; + font-weight: bold } pre .identifier { @@ -33,7 +47,7 @@ } pre .string { - color: rgb(3, 106, 7); + color: #d14; } </style> @@ -43,64 +57,71 @@ var hljs=new function(){function m(p){return p.replace(/&/gm,"&").replace(/< hljs.initHighlightingOnLoad(); </script> -<!-- MathJax scripts --> -<script type="text/javascript" src="https://c328740.ssl.cf1.rackcdn.com/mathjax/2.0-latest/MathJax.js?config=TeX-AMS-MML_HTMLorMML"> -</script> <style type="text/css"> body, td { font-family: sans-serif; background-color: white; - font-size: 12px; - margin: 8px; + font-size: 13px; +} + +body { + max-width: 800px; + margin: auto; + padding: 1em; + line-height: 20px; } tt, code, pre { font-family: 'DejaVu Sans Mono', 'Droid Sans Mono', 'Lucida Console', Consolas, Monaco, monospace; } -h1 { - font-size:2.2em; +h1 { + font-size:2.2em; } -h2 { - font-size:1.8em; +h2 { + font-size:1.8em; } -h3 { - font-size:1.4em; +h3 { + font-size:1.4em; } -h4 { - font-size:1.0em; +h4 { + font-size:1.0em; } -h5 { - font-size:0.9em; +h5 { + font-size:0.9em; } -h6 { - font-size:0.8em; +h6 { + font-size:0.8em; } a:visited { color: rgb(50%, 0%, 50%); } -pre { - margin-top: 0; - max-width: 95%; - border: 1px solid #ccc; - white-space: pre-wrap; +pre, img { + max-width: 100%; +} +pre { + overflow-x: auto; } - pre code { display: block; padding: 0.5em; } -code.r, code.cpp { - background-color: #F8F8F8; +code { + font-size: 92%; + border: 1px solid #ccc; +} + +code[class] { + background-color: #F8F8F8; } table, td, th { @@ -123,54 +144,54 @@ hr { } @media print { - * { - background: transparent !important; - color: black !important; - filter:none !important; - -ms-filter: none !important; + * { + background: transparent !important; + color: black !important; + filter:none !important; + -ms-filter: none !important; } - body { - font-size:12pt; - max-width:100%; + body { + font-size:12pt; + max-width:100%; } - - a, a:visited { - text-decoration: underline; + + a, a:visited { + text-decoration: underline; } - hr { + hr { visibility: hidden; page-break-before: always; } - pre, blockquote { - padding-right: 1em; - page-break-inside: avoid; + pre, blockquote { + padding-right: 1em; + page-break-inside: avoid; } - tr, img { - page-break-inside: avoid; + tr, img { + page-break-inside: avoid; } - img { - max-width: 100% !important; + img { + max-width: 100% !important; } - @page :left { - margin: 15mm 20mm 15mm 10mm; + @page :left { + margin: 15mm 20mm 15mm 10mm; } - - @page :right { - margin: 15mm 10mm 15mm 20mm; + + @page :right { + margin: 15mm 10mm 15mm 20mm; } - p, h2, h3 { - orphans: 3; widows: 3; + p, h2, h3 { + orphans: 3; widows: 3; } - h2, h3 { - page-break-after: avoid; + h2, h3 { + page-break-after: avoid; } } </style> @@ -193,13 +214,7 @@ hr { report, p. 284:</p> <pre><code class="r">library("mkin") -</code></pre> - -<pre><code>## Loading required package: minpack.lm -## Loading required package: rootSolve -</code></pre> - -<pre><code class="r">FOCUS_2006_L1 = data.frame( +FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -223,8 +238,8 @@ summary(m.L1.SFO) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:14 2014 -## Date of summary: Mon Aug 25 10:34:14 2014 +## Date of fit: Sat Oct 11 11:06:43 2014 +## Date of summary: Sat Oct 11 11:06:43 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent @@ -308,9 +323,8 @@ summary(m.L1.SFO) <pre><code class="r">plot(m.L1.SFO) </code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAFoCAMAAACMkBkOAAAC+lBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAB1fEeTAAAA/nRSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drc3d7f4OHi4+Tl5ufo6err7O3u7/Dx8vP09fb3+Pn6+/z+/4/AfJ4AAAAJcEhZcwAACxIAAAsSAdLdfvwAABSzSURBVHic7d0HdBTVGgfwbxNIB6SGEoHQIYAIAQF5hiYIBAhFEEJVilRRmkggAZHQW+ggIE2KQEJCTC8kIRQfIijyVJCmPFC6PIrOOW83CbDZ3ZBpd2d25v87x02ys/feD/8nk52dO3eIAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADA4ZUePgLUa3BRVsH32aH0vw1eILM6s+BHs+oZZLARwesTgtcpBK9T9gq+xKT97xZhNRQIZ6fgy2QEZy886MxqLBDMTsHP7EpVjszpxGosEMxOwW+pQjRx21hWY4Fgdgp+8ttEzpf7sRoLBLNT8F6pYzvOiok3sBoMhLLXu3q3YXN6GBb3YTWY9gw6nSiXM8E2+rdX8EW6D3+DPLNKsxpNc0b1la2r/iNsPGmn4N3iZ7yzYg212MVqNM3RSPAfDDQ+rGhJEb1ZDac1GgnedDhHvcaS57FyrMbTGI0EP7uj8WF6V6K2O1iNpzEaCb5i5lsVBie4GL/b2oXVgNqikeDJe+7Oie6mb0p/U5LIx4/ZxB+t0Erwz7XfVezgliUZ7ViNqxHaC57WRwcRFc90ZTWwNmgweM87HSK2Dl3chNXA2iBL8FW5GqSe4Oncjbrek39iNrA25Avexc/PhU8jQ+X8P6ss+FP/GUDtrlVkNbA2mAdfM2PlyoyaNl4UkrHvdmY9otGXHhi/BF2J+ZvanbofW5mCrs69dv4t+pvj6qso+LT3bx2OWNGc1cDaYB58dBXjL2+0jReFcBPKR/ziUp+b3P7IIQriorpWfRTaLvG0UxAXVmzVJWrKtSiqouC3Nm6cXDSjOKuBtcEY/Etr1+XYcNn0eHlD7k9rSz5/UcjvBirBtancx1Bl9zfG4EtR2IWyZVpxPkGcF3V7qLZdfeVj4/Z9P5LVuBphDN6pWq7qR02PR6vn/ej0/EUh3xof7g/y2vLnnd+MwT82/lJxJq2CHhEFqi54cu8+9GgDVuNqhPmufvGHTs4fLrLxopDrzuTNtf74UiOnOcbgHxItSCEq2dvD9C3z4J0rNKxoY/7si+fVVz/M622qfpkHX2RqauoUW5PTQ7hlbQ/+4rLwZ/++l372NaXd6MmYgMjvDc+C7+rBKnj3ZbeM+5bbS90sNxRyQcWEWRIH1jhex/EhF2PvHvEj7+S//j3m5EemtKnn2fvx1XJ++TufJqdD/6vLKvht5wb612nS/9Rmyw2FXUlzAB/avgi/4I/y6YpN8Ldyz7a1uGm5obDgyxzHPKwXUH3w364w/X13CjtpuaHQa+cCvpI4tKap/rP6FrduZidl/XHrNcsNhV80uQCHdAVTffDkNXju+rmDvZ4/0W1Pju++LKylW1ZN475iZEz0cKfCXqo/6g/exOM1z+c/uJTMsXp3oc2qZ7nTjDAvr9nT5ChCW1QffMWYm4fa3OGu1rfcsJTHVOo+qynN9DVNYhEapPrgD56ecpwL8/0q0XIDn+Bp06D4kcnJwxMwK8OS6oO/F0RtuHLU5Z7lBl7Bex65tMjLa8nPEovQINUH/5+lXrO412mCVXa8gqfGd7Kmh2SckFiEBo36uIlcpjMJvtcT7vHeazEPxllu4Be869lFHTp4xmFXb6nJPPm8aqN/6e/qawXXMYzYHmx1BTS/4Cl+Tw+q8LXUIkAodqdleQbfMPtyRIbVIQGwpnjw5DXiu1KsaoACKR880fu25hgAW2oInrb1ZFUEFEQVwbul4o+8vakieKp85CVWZYBt6gie2h3ACTr7UknwFDqFVR1gk1qCdzrQ3vhYKvHK+Q8Z1QP5qCV48sqoQU4XN9cJuDKdUUFgTjXBU83s4i0vGL/6XmNSDuSnnuCpw973so1fDLdYVAMWVBQ8zQm9XZxo3FkW1YAFNQXvtP/gjaijf9ZlUQ1YUFPw5J7cN3yCV+GvA+lUFTxVzMTCl3airuCpCaZd2onKgqehq+WuA2xSW/A062OZ6wCbVBe8YXt/mQsBW1QXPLmltpC3ELBFfcGTzwkfWQsBW1QYPDXJLCZnIWCLGoOn1jioY06VwVO/HbhDHWPqDJ5CwuQqA2xTafCGzTioY0ulwZPznh5yFQK2qDV4ck9oJVMhYItqg6fS2a/IUwjYot7gqfox3MuAHRUHT40zcRktM+xWr5YePDXLxJVVrLBbvVqG4KnN11bdgjzYrV4tR/D09g5cU8cGu9WrZQme3l2HD2+ZYLd6tTzB05CNSJ4FdqtXyxQ8jVtmemwZk7C2ijwdggmD1avb5N4m63Sc1J7zzJlG5JdayXh0h9P08pEevMGFXP3rm90qp3Tueoo79krt+anFM2m+aYcytrdcPYL04Gt9O6DhBY77ztdyg1y7eqPPZn5ezfil7yjZegTJwWck+RzZV6tebKzlBhmDp7lfmSZd720oX4+6J3316k50r53xD/tdyw1yBm9Ynb4vNHmMfB2C5OCTPy8aP4sMc1kdzuUyrJneGpfVyUly8L7n/7zI/XThXkvLDbIGTxSOFVJkxSf4l/LYvv9z0Z6z188fVsLqeZmDp/CZ8vanc3yC5/J8L6hnuYM3rMaKaDLiE7y///ZfB7bqd36OoJ7lDp4My0Nl7lHP+P2Nv9zV+NDpiqCeZQ+eaOpqnKuTC7/gr5iOpEZdEtQzg+Bp8nokLxN+wU9+tP3TbY8mCuqZRfDG5G3M9QER+AVv6Bl54kAPYedHmQRPwfvdWXSrPzyP4518WnkIPC/OJngKjLM+cgTh+AVf+QzH9T5TTVDPjIKnjkmYeysDfsF/nfg6VzJS2Al2VsFTi8MvM+pZT/gF/1fbMhwFWN1F9IWYBU/VMhuz6lo/+AV/ekZZjsYq+8mdmfKZnZj1rRf8gn/z0a9cyhNh168yDJ5Kp/di17k+8Dyc8w3bukDgNYzyBt8y7sROs0k+ngfGytm7DvEL/uw04TNcZQ2+UVx5qp9Z8vkThrAV+BBPCn7BL7nIpQ8vWfjrzMka/KJmxocx+W6pPmULLq+SgOcHOIZmC355sl9Qz7IGv8FU5jsj8z3XN81bxhH0hu8MHJ/RCdwNQT3LGnywabLlbotbGLxyDId1ovELPuQEd31t+yKFv9CMzHPuDixMtppdXSmjq4xj6Au/4K+tbiMsdZL9cK6qrcmWpRKGyzqIjvAKvjLXRXjPLI/jn3Get8GraVMXO4ykNbyCN8RECT8xYpfgiabf3rQyo4ZdhtIUnjNwOO7xw4cPBfVsp+AT+h+tWzXaLkNpCr/gg3IJ6tk+wbvGU6X0gfHY2QvlcBMxLBhSidw2XxT81lP3HG8ihoUF0anx0Yf34tJ5gRxwIkZ+y6Kzs6MWB2bVssto2uGIEzHMGdJMj+kGn+S+hbwS8nHIiRhmXHJ2Q8Y3d55ffoqZ1wI45kQMMzE1iWqZDucMH3yNkzb88XxXr/hEjALVSl+5Ki33A5xG2ZiRxRvP4Ft6l174ibDz33YKnpxq1Hg6JaPUgemYncETv+DDuNfXXP7vGkE92yt4c4aJh7C754df8H+87/moR6frtjeyW71ahHppgUoM63j4BX8zsPODEh2t1jcyYbp6tQgldoUXVWRgB8Mv+LU3r0e0vWFz6hXb1avFeDuzkUIjOxJ+wbuMn1GiwwKbsy0Zr14tRpXkqXiPVxie7+rd3xz0hu0TIaxXrxbDdclBrI1WCH7BN7rM3ed+tPlS5qtXi9L8OG5Y+GL8gj8a403e0ak2t1mtXt1sao6MQ7IUKFKJLas9lBxf9fgFf7eD8aHdnQK3lx1g9kOV9jn2RUosTaIeWa8rW4C68Qs+aokzOa+weVrWx6Qb5+NjuUHRXb1JiS/W4Sx9gfgEn5n5A/f7iWvcTlsb/3m6/KHlBsWDJxp6vLnSJagWn+DDjGYb/xtta2OrXx70bz6Ma271v1gFwVPlmPm4wM42Xrt6jzk/PDm/ooBFh7zW3Rvewur3XR3BE3XNbKN0CerEJ3jPH/47b+SsCz/bXsSYqMvvP6g2eCq/axHe3tvAJ/jFv5ju9Onxw7KCXlBm2ynrJ1USPFFAGu5lY41P8GdzV58YcVZQz6oJnoqv/AIf5FniE/zd3DlXXW2enSuQeoInap4yHLctzI9P8FnhOV9CjwjqWU3BU5GJ8Q2UrkFd+ATf/2E34+9Lm/tDBfWsquCJXt6zGB/nmOF1OBf+z/mUc9wqNSxiLEHrrA9wtvYZfh/ZNg7d8VkrgT2rLnjyXJCA/f1T0m8xWhD1BW/c32/5ApMxc+kreKLux0bhyloTvQVPbh8f6aB0DWqgu+CJvNcfrFv4q7ROh8ETNYxZo/s/9boMnujNjBmeStegLJ0GT06Dj47W9YUXeg2eyHVCdj8df6Cj3+CJis86/JbSNShGz8ETlVseZ3U9gE7oO3iiqlsimypdgyL0HjxRTX1Gj+CJamyObGbj6QorE7bVt3sx9oLgTapvirL6W+/5TcqJzNPCFnV0IAg+V7WNMQH5nxn4cz2qcFzY8i8OBME/9fKKhE7mU002rTc+9PpOqXJYQ/DPeYen939+znZBmvFhTqZi1TCG4M0Vn5w1/ukF343ORI7bdHKIkuWwhODzcxmSNq9S7rfjsyJTBc4zdCAI3sqbUdtz3+KXa11V2UpYQvA2+G1ID9b6PS8QvE3lwk7O1PZcDQRfgKJ94rba+jxPKxB8wV7ZlvWeZi+xRvAvUnbKv5fWVroINhD8izm9uTu2lxbnaMkQvKpWr2ag3KSMpQ2VLkJ2koNX2+rVLBgCtmeOFX6TVVWTHLz6Vq9m4qVRh/d01tLdjiQHr8LVqxnxm38svI7SRchGcvBqXL2aFae2m+NGl1a6CnlIDl6dq1cz495331d9tHBwL/1dvdXq1bVH5EiKktqzSpUdHbu9m6vSVUglPXg3f1PoJf2fPdEgN/jEg1J7Vi+fjxK2BbkrXYUkkoP3u8g9nEoUpMZFjFmq9knWTkf+vZccfGps7WFPgvUXvFGtacmbAh11kWTJwf/ViWj69RJ6DN7Id1LSV4Mc8n2+5ODPzXYltx939tRn8EYvj49PnOCrdBWCSQ6+1+MHAfTK9du6Dd6o1IA9hz/1d6zpedLf1dcYW4eoYqjVHWh0FLxR0TYLE9Z1d6BVNnBaVj6+Yw5ETXSUJRQRvKw8uqw+srlfWaXL4AHBy67ehJjDc1urfZYugmfBtd38jKjx9dX8dg/Bs+Ldb8ORXSNqKF1GQRA8S1UGbUjcMlSV4SN41rzfWX9415gGaltZDcHbw8sD1mZGTQtQ02E+greXMoGfJaZHBKvlw10Eb09FXh29LfPA5FYqOJWP4O2uXLfwhMMbRryq7GUaCF4RTnUGLk9JiRhUT7H3fAheAe/FxkeUM+74Gw1bl564ZFBDJX73Ebz9DT2WlZyRlvexnpv/sFUpaRvHvG7nu+IhePs7G2agpldrmj3jVKtP+KHDez7pVs1uu34Eb383Tbv273paPV/+rY93n8xe+35zL+s2skPw9nepB1Hx3wpYL93Df8jC2NS4laPeYHuVJoK3v/nHtizJOvPC87auDfp8uj89ftW4DlUYneJD8PbnsjHjUGYjPi/06zXti9TUL0PfeVX2q7YQvBK8Kgp5dfnWI5fEpiZtnt67gXxXcCB4R+Fctf2YiLiUuDWTevqXk94dgncwLrU7j12wOyU1asmYwPoSjv0RvIMq1qjXR8ujUlKilk/s3ay88PYI3sEV8+syat7OpJS4jTMGBfjy//AXwWuES7XWg0M/j09KiV4XOqzLK4Wux4rgtca95r/6f7T0y6SUpL3LpwxsU7eAjwERvHaVqt9xSMjKqKTkpOjN8yb1yX8aAMHrgXuNtWcOpXU3fwrB60KPZURF48yzRvC6sM5/0qp3Bw0zewbB68Lak11rDj810OwZBK8LG7I9qdLFfmbPIHhd2DIyLuHAlLFmzyB4XZjc1/iwyPwmqgheF7wyPugYvt38GQSvD0V7TWyX7wncoUKncIcKnWJwh4pK7XPs2y+xZ2CJwR0qWk7NEbVWYs/AErs7VPQZLbFnYIndHSoQvKoxuENFHgSvauyO4xG8qiF4nULwOoXgdYpd8B1++iaf//0lzWO0l+Re/jh+EnT5nhSpaO/Q7RUbGO2Vba/YwGivbHvFBkZ7ZdsrNjDaK9tesYHRXtn2oiWgvUO3F03q8i1or2x7AAAAAAAAANC8107e/MLqKisBLnEcd05sY8O3gZJKyG0vuoSeP95P9pMwfl570eOP/PVuZEXpEYjidSsi8NxS8e3d/gnp3bujyMa9tnGBUkrIbS+6hBqPP+389a9uosfPay96/Kb/jOz+437JEYgz+JYzDbxl45panupxvrVE36sp+aQpOPEl5LYXXcLYB65Um2sievy89qLHn36a6JOrkiMQZ242kT8nYq3dPF25v7gbHcS2djMFJ6GEnPaiS6gTQDTsoZfo8fPaix7fu0qFwOTtkiMQZ32S8R/ANRTdPvh4sxLr/hB7U96c4CSUkNNeQglunzyZKml8U3sJ44dx3GuSIxAnPIuoCSdtYmcNrrnIljnBSSghp734EhqevR4sZfzc9qLHL+JMhjF/FJEjAuGG3HCi/nfE/4GZMJvIj6stsnVOcBJKyGkvuoQK1798iSSMn9de9Ph7thvf4HFVpEYgjtftsObfSnhL2e2f8W3jssXejS8nOAkl5LQXXcLM2z2DgoJKih4/r73o8Qfc69c69oxBagQitTh1a4uUg8gPr97ZJ3o3lburFl9CbnuxJURyJv6ix3/aXuz4hrCrd6OrSY8AAAAAAAAAAAAAAAAAAAAAAAAAAMDClZxpjtwuu19/Asr6V/uO3ML27TvPK6Z0JWBnRbghREEPKfDGhlvH+52+F2Ggdqfux1ZWui5g7Fnw3LiyP9327co1qPootF3iabGXdoCDeB58Mdqzlzy41mEXypZpxfkoXRiw9Sz4v4l2bSQ3rvXWnPd7rZQuDNiyDn5BClHJ3h5KFwZsWQff6MmYgMjvDUoXBmxZBv9bY+p59n58NaXrAgAAAAAAAAAAAAAAAAAAAAAAAPX5P8dp/moUDzaDAAAAAElFTkSuQmCC" alt="plot of chunk unnamed-chunk-4"/> </p> - -<p>The residual plot can be easily obtained by</p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-4"/> +The residual plot can be easily obtained by</p> <pre><code class="r">mkinresplot(m.L1.SFO, ylab = "Observed", xlab = "Time") </code></pre> @@ -326,15 +340,15 @@ summary(m.L1.FOMC, data = FALSE) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:17 2014 -## Date of summary: Mon Aug 25 10:34:17 2014 +## Date of fit: Sat Oct 11 11:06:44 2014 +## Date of summary: Sat Oct 11 11:06:44 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.3 s +## Fitted with method Marq using 53 model solutions performed in 0.314 s ## ## Weighting: none ## @@ -420,15 +434,15 @@ summary(m.L2.SFO) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:17 2014 -## Date of summary: Mon Aug 25 10:34:17 2014 +## Date of fit: Sat Oct 11 11:06:44 2014 +## Date of summary: Sat Oct 11 11:06:44 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 29 model solutions performed in 0.184 s +## Fitted with method Marq using 29 model solutions performed in 0.173 s ## ## Weighting: none ## @@ -530,15 +544,15 @@ mkinresplot(m.L2.FOMC) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:17 2014 -## Date of summary: Mon Aug 25 10:34:17 2014 +## Date of fit: Sat Oct 11 11:06:46 2014 +## Date of summary: Sat Oct 11 11:06:47 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 35 model solutions performed in 0.2 s +## Fitted with method Marq using 35 model solutions performed in 0.206 s ## ## Weighting: none ## @@ -616,8 +630,8 @@ plot(m.L2.DFOP) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:18 2014 -## Date of summary: Mon Aug 25 10:34:18 2014 +## Date of fit: Sat Oct 11 11:06:47 2014 +## Date of summary: Sat Oct 11 11:06:47 2014 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -626,7 +640,7 @@ plot(m.L2.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 43 model solutions performed in 0.26 s +## Fitted with method Marq using 43 model solutions performed in 0.265 s ## ## Weighting: none ## @@ -705,15 +719,15 @@ plot(m.L3.SFO) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:18 2014 -## Date of summary: Mon Aug 25 10:34:18 2014 +## Date of fit: Sat Oct 11 11:06:48 2014 +## Date of summary: Sat Oct 11 11:06:48 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.252 s +## Fitted with method Marq using 44 model solutions performed in 0.261 s ## ## Weighting: none ## @@ -791,15 +805,15 @@ plot(m.L3.FOMC) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:19 2014 -## Date of summary: Mon Aug 25 10:34:19 2014 +## Date of fit: Sat Oct 11 11:06:48 2014 +## Date of summary: Sat Oct 11 11:06:48 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.148 s +## Fitted with method Marq using 26 model solutions performed in 0.159 s ## ## Weighting: none ## @@ -864,8 +878,8 @@ plot(m.L3.DFOP) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:19 2014 -## Date of summary: Mon Aug 25 10:34:19 2014 +## Date of fit: Sat Oct 11 11:06:50 2014 +## Date of summary: Sat Oct 11 11:06:50 2014 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -874,7 +888,7 @@ plot(m.L3.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.236 s +## Fitted with method Marq using 37 model solutions performed in 0.225 s ## ## Weighting: none ## @@ -962,15 +976,15 @@ plot(m.L4.SFO) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:19 2014 -## Date of summary: Mon Aug 25 10:34:19 2014 +## Date of fit: Sat Oct 11 11:06:51 2014 +## Date of summary: Sat Oct 11 11:06:51 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.123 s +## Fitted with method Marq using 20 model solutions performed in 0.119 s ## ## Weighting: none ## @@ -1037,15 +1051,15 @@ plot(m.L4.FOMC) <pre><code>## mkin version: 0.9.33 ## R version: 3.1.1 -## Date of fit: Mon Aug 25 10:34:20 2014 -## Date of summary: Mon Aug 25 10:34:20 2014 +## Date of fit: Sat Oct 11 11:06:51 2014 +## Date of summary: Sat Oct 11 11:06:51 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 48 model solutions performed in 0.281 s +## Fitted with method Marq using 48 model solutions performed in 0.283 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index ca6d2506..426aa0df 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ diff --git a/vignettes/mkin.pdf b/vignettes/mkin.pdf index 0cc413a1..83182e65 100644 Binary files a/vignettes/mkin.pdf and b/vignettes/mkin.pdf differ -- cgit v1.2.1 From 4510a609159216041f10a33146534f5a8366ac76 Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Tue, 14 Oct 2014 22:04:54 +0200 Subject: Further formatting improvement for differential equations --- vignettes/FOCUS_L.html | 96 ++++++++++++++++++++++++++----------------------- vignettes/FOCUS_Z.pdf | Bin 213325 -> 220198 bytes 2 files changed, 51 insertions(+), 45 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index c0430358..60c5132a 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -214,7 +214,13 @@ hr { report, p. 284:</p> <pre><code class="r">library("mkin") -FOCUS_2006_L1 = data.frame( +</code></pre> + +<pre><code>## Loading required package: minpack.lm +## Loading required package: rootSolve +</code></pre> + +<pre><code class="r">FOCUS_2006_L1 = data.frame( t = rep(c(0, 1, 2, 3, 5, 7, 14, 21, 30), each = 2), parent = c(88.3, 91.4, 85.6, 84.5, 78.9, 77.6, 72.0, 71.9, 50.3, 59.4, 47.0, 45.1, @@ -236,17 +242,17 @@ given in the FOCUS report. </p> summary(m.L1.SFO) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:43 2014 -## Date of summary: Sat Oct 11 11:06:43 2014 +## Date of fit: Tue Oct 14 22:03:33 2014 +## Date of summary: Tue Oct 14 22:03:33 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 14 model solutions performed in 0.083 s +## Fitted with method Marq using 14 model solutions performed in 0.081 s ## ## Weighting: none ## @@ -338,17 +344,17 @@ is checked.</p> summary(m.L1.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:44 2014 -## Date of summary: Sat Oct 11 11:06:44 2014 +## Date of fit: Tue Oct 14 22:03:34 2014 +## Date of summary: Tue Oct 14 22:03:34 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.314 s +## Fitted with method Marq using 53 model solutions performed in 0.289 s ## ## Weighting: none ## @@ -432,17 +438,17 @@ FOCUS_2006_L2_mkin <- mkin_wide_to_long(FOCUS_2006_L2) summary(m.L2.SFO) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:44 2014 -## Date of summary: Sat Oct 11 11:06:44 2014 +## Date of fit: Tue Oct 14 22:03:35 2014 +## Date of summary: Tue Oct 14 22:03:35 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 29 model solutions performed in 0.173 s +## Fitted with method Marq using 29 model solutions performed in 0.154 s ## ## Weighting: none ## @@ -542,17 +548,17 @@ mkinresplot(m.L2.FOMC) <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:46 2014 -## Date of summary: Sat Oct 11 11:06:47 2014 +## Date of fit: Tue Oct 14 22:03:36 2014 +## Date of summary: Tue Oct 14 22:03:36 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 35 model solutions performed in 0.206 s +## Fitted with method Marq using 35 model solutions performed in 0.192 s ## ## Weighting: none ## @@ -628,19 +634,19 @@ plot(m.L2.DFOP) <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:47 2014 -## Date of summary: Sat Oct 11 11:06:47 2014 +## Date of fit: Tue Oct 14 22:03:36 2014 +## Date of summary: Tue Oct 14 22:03:36 2014 ## ## Equations: -## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * ## time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 43 model solutions performed in 0.265 s +## Fitted with method Marq using 43 model solutions performed in 0.24 s ## ## Weighting: none ## @@ -717,17 +723,17 @@ plot(m.L3.SFO) <pre><code class="r">summary(m.L3.SFO) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:48 2014 -## Date of summary: Sat Oct 11 11:06:48 2014 +## Date of fit: Tue Oct 14 22:03:37 2014 +## Date of summary: Tue Oct 14 22:03:37 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.261 s +## Fitted with method Marq using 44 model solutions performed in 0.237 s ## ## Weighting: none ## @@ -803,17 +809,17 @@ plot(m.L3.FOMC) <pre><code class="r">summary(m.L3.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:48 2014 -## Date of summary: Sat Oct 11 11:06:48 2014 +## Date of fit: Tue Oct 14 22:03:37 2014 +## Date of summary: Tue Oct 14 22:03:37 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.159 s +## Fitted with method Marq using 26 model solutions performed in 0.139 s ## ## Weighting: none ## @@ -876,19 +882,19 @@ plot(m.L3.DFOP) <pre><code class="r">summary(m.L3.DFOP, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:50 2014 -## Date of summary: Sat Oct 11 11:06:50 2014 +## Date of fit: Tue Oct 14 22:03:37 2014 +## Date of summary: Tue Oct 14 22:03:37 2014 ## ## Equations: -## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * -## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * +## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * +## time)) / (g * exp(-k1 * time) + (1 - g) * exp(-k2 * ## time))) * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.225 s +## Fitted with method Marq using 37 model solutions performed in 0.207 s ## ## Weighting: none ## @@ -974,17 +980,17 @@ plot(m.L4.SFO) <pre><code class="r">summary(m.L4.SFO, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:51 2014 -## Date of summary: Sat Oct 11 11:06:51 2014 +## Date of fit: Tue Oct 14 22:03:38 2014 +## Date of summary: Tue Oct 14 22:03:38 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.119 s +## Fitted with method Marq using 20 model solutions performed in 0.106 s ## ## Weighting: none ## @@ -1049,17 +1055,17 @@ plot(m.L4.FOMC) <pre><code class="r">summary(m.L4.FOMC, data = FALSE) </code></pre> -<pre><code>## mkin version: 0.9.33 +<pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Sat Oct 11 11:06:51 2014 -## Date of summary: Sat Oct 11 11:06:51 2014 +## Date of fit: Tue Oct 14 22:03:38 2014 +## Date of summary: Tue Oct 14 22:03:38 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 48 model solutions performed in 0.283 s +## Fitted with method Marq using 48 model solutions performed in 0.26 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index 426aa0df..b5898b7c 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ -- cgit v1.2.1 From 65d31e345f9e61e9d05584b24df6a01c6c6ed18d Mon Sep 17 00:00:00 2001 From: Johannes Ranke <jranke@uni-bremen.de> Date: Wed, 15 Oct 2014 01:13:48 +0200 Subject: Switch to using the Port algorithm per default --- vignettes/FOCUS_L.html | 135 ++++++++++++++++++++++++++----------------------- vignettes/FOCUS_Z.pdf | Bin 220198 -> 220189 bytes 2 files changed, 73 insertions(+), 62 deletions(-) (limited to 'vignettes') diff --git a/vignettes/FOCUS_L.html b/vignettes/FOCUS_L.html index 60c5132a..82bbd2c7 100644 --- a/vignettes/FOCUS_L.html +++ b/vignettes/FOCUS_L.html @@ -244,15 +244,15 @@ summary(m.L1.SFO) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:33 2014 -## Date of summary: Tue Oct 14 22:03:33 2014 +## Date of fit: Wed Oct 15 00:58:15 2014 +## Date of summary: Wed Oct 15 00:58:15 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 14 model solutions performed in 0.081 s +## Fitted with method Port using 37 model solutions performed in 0.203 s ## ## Weighting: none ## @@ -272,7 +272,7 @@ summary(m.L1.SFO) ## Optimised, transformed parameters: ## Estimate Std. Error Lower Upper t value Pr(>|t|) ## parent_0 92.50 1.3700 89.60 95.40 67.6 4.34e-21 -## log_k_parent_sink -2.35 0.0406 -2.43 -2.26 -57.9 5.16e-20 +## log_k_parent_sink -2.35 0.0406 -2.43 -2.26 -57.9 5.15e-20 ## Pr(>t) ## parent_0 2.17e-21 ## log_k_parent_sink 2.58e-20 @@ -341,20 +341,31 @@ The residual plot can be easily obtained by</p> is checked.</p> <pre><code class="r">m.L1.FOMC <- mkinfit("FOMC", FOCUS_2006_L1_mkin, quiet=TRUE) -summary(m.L1.FOMC, data = FALSE) +</code></pre> + +<pre><code>## Warning: Optimisation by method Port did not converge. +## Convergence code is 1 +</code></pre> + +<pre><code class="r">summary(m.L1.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:34 2014 -## Date of summary: Tue Oct 14 22:03:34 2014 +## Date of fit: Wed Oct 15 00:58:16 2014 +## Date of summary: Wed Oct 15 00:58:16 2014 +## +## +## Warning: Optimisation by method Port did not converge. +## Convergence code is 1 +## ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 53 model solutions performed in 0.289 s +## Fitted with method Port using 188 model solutions performed in 1.011 s ## ## Weighting: none ## @@ -375,23 +386,23 @@ summary(m.L1.FOMC, data = FALSE) ## ## Optimised, transformed parameters: ## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) -## parent_0 92.5 1.45 89.40 95.6 63.60 1.17e-19 5.85e-20 -## log_alpha 14.9 10.60 -7.75 37.5 1.40 1.82e-01 9.08e-02 -## log_beta 17.2 10.60 -5.38 39.8 1.62 1.25e-01 6.26e-02 +## parent_0 92.5 1.42 89.4 95.5 65.00 8.32e-20 4.16e-20 +## log_alpha 15.4 15.10 -16.7 47.6 1.02 3.22e-01 1.61e-01 +## log_beta 17.8 15.10 -14.4 49.9 1.18 2.57e-01 1.28e-01 ## ## Parameter correlation: ## parent_0 log_alpha log_beta -## parent_0 1.000 0.24 0.238 -## log_alpha 0.240 1.00 1.000 -## log_beta 0.238 1.00 1.000 +## parent_0 1.000 0.113 0.111 +## log_alpha 0.113 1.000 1.000 +## log_beta 0.111 1.000 1.000 ## ## Residual standard error: 3.05 on 15 degrees of freedom ## ## Backtransformed parameters: ## Estimate Lower Upper -## parent_0 9.25e+01 8.94e+01 9.56e+01 -## alpha 2.85e+06 4.32e-04 1.88e+16 -## beta 2.98e+07 4.59e-03 1.93e+17 +## parent_0 9.25e+01 8.94e+01 9.55e+01 +## alpha 5.04e+06 5.51e-08 4.62e+20 +## beta 5.28e+07 5.73e-07 4.86e+21 ## ## Chi2 error levels in percent: ## err.min n.optim df @@ -440,15 +451,15 @@ summary(m.L2.SFO) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:35 2014 -## Date of summary: Tue Oct 14 22:03:35 2014 +## Date of fit: Wed Oct 15 00:58:17 2014 +## Date of summary: Wed Oct 15 00:58:17 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 29 model solutions performed in 0.154 s +## Fitted with method Port using 41 model solutions performed in 0.22 s ## ## Weighting: none ## @@ -500,10 +511,10 @@ summary(m.L2.SFO) ## ## Data: ## time variable observed predicted residual -## 0 parent 96.1 9.15e+01 4.635 -## 0 parent 91.8 9.15e+01 0.335 -## 1 parent 41.4 4.71e+01 -5.740 -## 1 parent 38.7 4.71e+01 -8.440 +## 0 parent 96.1 9.15e+01 4.634 +## 0 parent 91.8 9.15e+01 0.334 +## 1 parent 41.4 4.71e+01 -5.739 +## 1 parent 38.7 4.71e+01 -8.439 ## 3 parent 19.3 1.25e+01 6.779 ## 3 parent 22.3 1.25e+01 9.779 ## 7 parent 4.6 8.83e-01 3.717 @@ -522,7 +533,7 @@ plot(m.L2.SFO) mkinresplot(m.L2.SFO) </code></pre> -<p><img src="data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAfgAAAJACAMAAABWh4TIAAAC/VBMVEUAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAADmnzsbAAAA/3RSTlMAAQIDBAUGBwgJCgsMDQ4PEBESExQVFhcYGRobHB0eHyAhIiMkJSYnKCkqKywtLi8wMTIzNDU2Nzg5Ojs8PT4/QEFCQ0RFRkdISUpLTE1OT1BRUlNUVVZXWFlaW1xdXl9gYWJjZGVmZ2hpamtsbW5vcHFyc3R1dnd4eXp7fH1+f4CBgoOEhYaHiImKi4yNjo+QkZKTlJWWl5iZmpucnZ6foKGio6SlpqeoqaqrrK2ur7CxsrO0tba3uLm6u7y9vr/AwcLDxMXGx8jJysvMzc7P0NHS09TV1tfY2drb3N3e3+Dh4uPk5ebn6Onq6+zt7u/w8fLz9PX29/j5+vv8/v+3IrpgAAAACXBIWXMAAAsSAAALEgHS3X78AAAdcUlEQVR4nO3dCVxUVd8H8DMgiIgLT+4aAo+5bwmaJj0ikJTbi0pWimE9LrlkWk/aa/hIlqj4PGmaFmbmlluZuOHC4gZaRuprpb6+rrjUowa4Bprn884MizBzB8695565d2Z+38+ni86d8z/H+cXMnXvvuZcQAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAABweI+NHAX6FeshKvjBX2n9b4MKZP5VWPBjRVUGFSy1V/D+ofVE9QQK2Cl4w6eb5qaPF9UVyGen4F9917j4up2ovkA2OwVv7ual10X1BbLZKfjELsbF2BdE9QWy2Sn4NjsbkLZ7a4jqywl1TlJPkER9e23Vd9/5w5oAUV05ozFvBKpl4iiJ+vYKvt3a3YsaiOrKGY15UbVSQzQMvlVGY9JpP97q2TlJ8HO6GxdvRovqywk5SfBfBJLxZPAYUX05IScJ/rW3yQGypr2ovpyQkwTvtmrlid2TRXXljJwkeEICVvcX1ZNTcprgScxoUT05JecJvtPHonpySs4TvPcuUT05JVWC96fNiObBGzfrgV3Z4KtMSU+fXIWhkcGv/N/1EfyWv4jqyhmVDX7WJDf39xIknhR3YGN+ZmtCxubcM/6IurztTxJ+7E6KH4m6kvDbuefIn5S21T742d1FdeWMjMG7dQoqkm1eBBf9pZPboyfF0YkNFp71bEvfiTi4nUTRzf38C6eHpx53i6LxNRblkM60m4f2wb8i1T/YYAy+9uwic8+ZlufmFv/V99GT4n41kFq0p99gQ9P12cbg/0Liz9etE0KbRFEf0r9A8Fu9e8P2jdytH7YKPmged1cupOxb/e6mhDSV2jaOO2pc3HnFZ/nvN68ag79PyEpqEhJVSEhfscFXm59n7Cl/npflCqvgvXdyduVSygbf4sC//r2/ucST4q65k/o09N2cjm4fGoMvICQxgxDfaG/THwUHv+rUsOCWQUOOfWm5wvq8emzWy1Du65xn586eUk+Ko/PDtpz1nHsm+MWcMwGmtDs+GNcj+WdDafD9vEUFn9fH/KNbruUK6+C31ubsy5UwfY+Pu5hy62AbUj/97o/jjrxlSpsMPHFnV6D5l7/3ceK2/Y9WooI/usD0+e4Wf8RyhXXwc57m7MuVsAX/HUspMcF3y8s9lJZ1I+8pyxXWwceO4OzLleg+eOITm7AkIdbn0QNdpphtWmT5zM4f8fblQhxjX733U9Uf/aVphNnGZMtn+exQoy8XofvgG23L3d7zJr3S1nLFvHVWz8VmPTvdB7/l+OTDND7gm1TLFRLBb6vF2ZkL0X3wt6NIT1qP9LltuUIi+MSunJ25EN0H/7/zfN6n3cnEM5YrJIIf/nfOzlyI7oMf9IDe//q3bffesFwhEXyXf3N25kJ0HzxpPrSlYdTqoQbLxyWC90nh7cx1xGSnquXHIRL1xR2WlQieHLD63wM0Yt/gV2LCrF7YN3jMntMN+wYfMktUbyCTfYPHTlvdsG/wJBNbdzph5+BXYetOJ+wc/ERs3emEnYN/Blt3OmHn4LF1pxd2Dp5kYetOH+wd/Gps3emDvYOfhK07fbB38H/D1p0+sARfu1hNWZWlg8fWnU6wBE+L/SyrsnTw5CC27nSBJfjg4NUXhoW8fO5D6dU2ZsvaCP4rf/bBgThsn/GX+hkXz1+WWmVztqyN4N/C1p0usAV/eZxxMSZHapXN2bI2gg+VuqQH2B1b8O8Urv5gVeHbUqtszpa1EbxXGvvgQBy24A0Dk3/YNEBys8zmbFkbwZO0ajKGB6Iwfo93axLiLb05bnO2rK3gZ/ZgHx0Iwxa830+URv8UKLnOarZs16Jr9PywXbrWc9NkDxLUxxb8jtTu1DdZ+ho2Bk9SNbhtmcvvNS6aLfvNRulaNXFyvR6wBX83rA4lPaymx5k0PxrT/jyl/2N18MXWWz3ZJ+w+xsCOLfjj0+pSMl5yz92BtCYHNzZvnWL1e2wz+I86yxgfCMIW/LOFF2jGgwFSq24/T26HE9LzluUKm8EPkPxaCPbF+HUuIH5lYgfJVelfeOx6nxgSmL/OkTqb2IcHorAFf+K/m9paFXDu94v09PnbVle0shk8OeBmaw3YDVvwH12k+0b6Sq/zGDhjyZwR1te6sB38p20YBwfiMO7AMXRJPPvgW1mVbQc/BLch0x7rGThNxu6m12VVth184zWyCoEIbMHH/UCvfRbBco+ER2wHT/bLKgQisAX/2+Ke8lInFQa/XHrnL9gRU/B+tI/8yhUEPzJWfjlQF1Pwhm2b5d9PpoLg/b6WXQ1UxngGDqX3CwoKZFWuIHhyELvrtcYWfFQRWZUrCj4xVFYpUB/3iRg2VRR8aKK8WqA6/hMxbKko+CqZskqB+vhPxLClouDJBn9ZtUB13Cdi2FRh8MOx11Zj3Cdi2FRh8PWs7mIA9sV9IoZNFQZP9lrNvAG7Ytyqt30ihk0VBx8fKa8aqIwx+KfrPzZ3qrxf0oqD7zJfVjFQG1vw8bT7p5f+86n0SnmzZYu54f402mIL/sbr1QsHPH9NapXc2bJmbk92WyvsUhzAgi343L6979WKtDqR1kTubFmTxw/MmX7yc+YxggBswX+We21h2HXJU6/kzpY1WduekPq3cGkMLbEF7zlhWq1eiZJnW8qeLWu0x7Q43ZtthCAE41Z9tWdf+Zv0OTiyZ8sa7fFs3a7aYWzXa4kt+I6X6B16UvqpVrNl/1Y8W9b2rv23Ly+df2bLIZxeryG24L/bVp/U37rH5vq6MWX+Ui/IbI2N2bJGK6Zs2zFt/7y/MY8SVMcW/K1exkX4TalVTUz60yZNLFdU8FafYVqs6GdjvwDYA1vwmz9yJ+4LJN+7H5ZcBc9yRUXBm97kt9T8zpN1lKA6luAzM3+hv/7wG5WcBxFy9t6QriNoV6vbxlYQ/Lg5nmTIl2SGgnN3QSUswccbzTD+N1ZyrU/S7ZHdrH7fKwzeMDrru5nepOVq1lGC6pje6r0//OXBuQU27wHe59df5AVfIhMXwNIMS/DVf/nP7NHvnz9j8yLGdVYds36QIfiJMZU+BQRhCf7fZ2sbl96/yNvjwhB8jb2yKoKKWII/Md78Y9QJWZUZgiefWG0Sgp2wBH+r6JyrfpJH52xiCb7VSlklQT0swWcV3VVi+kFZlVmCJyn1ZdUE1bAEP6Sgv4GQnndelVWZKfgB78mqCaph+jo36+G5jFN0kbwD6EzBu2dh+qQ22HbZdpr+1cwQmZWZgidTB8ksC+qw912oLNXBlW21oXXwZDUucKoJzYN/Ar/ymtA8eLIc52NoQfvg/XELQi1oHzz5PFTUEMA2HQTvly5qCGCbDoIni8NFjQFs0kPwfqmYVGN3KgSvaLZsOQllTsjwmbom3sYF0kFF3MErmi1roerBOiV/rL7nxXoRB+RfRxNk4g5eyWxZK72WlPxpmOkuti9N4hwUVIo7eCWzZa2tL7mzyXsRxkWHeZyDgkpxB69ktqy1hpnFh2d7z/QOrDHpFc5BQaW4g1cyW1bC5KLz+ojh+yvpOb9IbCuCuvi36q1my/ZMMjsu60KYHvtamX8++VW31575pCfvoKAy/MF7BZtC9w0ufeCxotmyX8m7KP3jh2qYfvzDtMnQZTbvoKAy3MG3uUgLphASJWfSpKQ+X5iWYwcbF2HTOQcFleIOfk9KixEPhqoQPJln2o3TdG9DUmcXbkwnHHfwd583fge7VkuF4D12tzAug3dmpD3DOSaoHHfwp2ZUJV4n1wzkD548kVWbczDAjDv4Qffv9SAdruWrEDzpsa8652iAFf9WfbPxLQlpNN3qOuQKgieDN8u+vR0oo4fDsmWMXVL5c0ANOgueLJiq9jhAkt6CN8z7UO2BgBS9BU/IhBX4nLcD/QVPhm3EbUvE02HwZOiOemoOBKToMXjy5KHnVBwISNFl8KTmutk4JC+WPoMnhsnbm6k2EpCg0+AJCdgxu6pKIwEJug2euL2ZJfciHMBOv8ET4vf5t+3UGAlI0HPwhDRbvqIL41N9umK/jxz6Dp6Q9qvTB7DcwuTA7/+bv1CNDl2F3oMnpEH8gff8K3vS3KOEeN/ArFt2+g+eEPfIlSmjG1b4lBOmPT6LV6jVowRPP+e6d5IjBG9U/aW1GTO62t6pcyTauFi+WMUeLXyQtnT/y+LK25+DBG/k2Wt+5uaJT0pvwo2+Wo8E5bdWt8cyBs0nNTy2OdM+JccJ3qTe4AUZGQuGB3tbrUm8fuPqMPU7LJE0LTvj4MwR4joQzvKjShcXRpDF48lX/7Vj39b5b/RubZ2/KJuS3YnPyQl26091Cy+du1ju/1tdXBhBAZ8OA9/5ZGtGxtZlsybFRHZoJPgQ/jd7qpPG//eG2E4EGvVrSJ0Xf29V5hF9XBhBueotQ2MmTP945bY0o9QNyz6eOeX1UYNfiAgPejIwMNDP16fyEiwWTdi5e9P04eoU08CPscbF2rI7OgRcGCGyeLbsLs7K8hl8/Vp3iRj0wshRU96dnZi0dMO3qY9kZWdn7zX+/N74M63ooZ0bKrQ2qdTWk0uSll1aleSocnckjSOfLS/zWgm4MELNQLNJkzkri1fF11rTQElTfzp0ZKj0Kkfw5Ym29Rr/VvbG7eIujDBY+v6EDquG1gPg4fn9+Z9yyt0ASMCFEYo5W/COzRD2WsdyD4j7Ho/gdQ3Buyhxwfc6nV3OH3f5FahR5L4KNf4oUKGIGgO5l5utzOlGooK3tEeFGqPVOE6ixkDCp6lQZJMKlwPwE3lEUh0I3hKCZ4bgLSF4dgjevhC8JQTPDMFbcoDgd6tQY8RgFYqoMZBQNS7csbEmf43GVkdFdUeNCVFV1JhKqcZA3DxVKKLKFDHMMwMAAAAAAAAAtTx1JHcF76SHHErpKb4ShqN9uQdTVINvMANP3klvwzmQ4hoqvCoC+eQt7HuK8zaCXg/joqMjuUoMWkX78g6mqAbfYJrd/6D3jgteXAMprqHCqyJSbJ47GZbHt8O1NQ1o7sE3jPQjptD4BlNUg28w4+9VJS1oENdAimuo8KqIlHCIkGDagKtGP3qXXu/FNw4vU2icgzHX4BtMyx6EjCjw4RpIcQ01XhWBlqQZR0rbc9UYerhLraQbfDcvMYfGORhzDd7BeE19MIV7IKYaarwqAs3KIiSI8p/Y2Yx25WpvDo1zMOYanINpf+LaUN6BFNXgHIhow6+7kSE3+T7jJ84gpA1twVXDHBrnYMw1+AbT8Npa00kYXAMprqHGqyKQT35816OcW/X9H04I23mI7xJE5tA4B2OuwTeYf+YPjIqK8uUaSHENNV4Vkbody1vO+z1+0pWbGzk/LYrepvkGU1SDazDJ1CSYayAlNVR4VQAAAAAAAAAAAAAAAAAAAAAAAAAAFLlsPp+RruOcAgSO5pmISDo3IqL3bIe+LQkoUIUOJySqgPS9/nne4ZeP315oIOHH7qT4aT0uEKw0ePpG3dP5Af1oO//C6eGpx/U7gQFU8Sj4GmTD18Sbhsafr1snhDbRemAgVmnwfxKybinxoqErzdt7IVoPDMSyDj4xgxDfaPvdzhg0YR18xwfjeiT/bNB6YCCWZfBXO5GBJ+7sCtR6XAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAAACDAYyNHgX7FeogKfvBXWv/boAKZfxUW/FhRlUEFSxG8a0LwLgrBuygE76IQvItC8A6hQxKPIImKCN4hxE4OVG7iKImKKgTv3rB9I3frhxG8imJf5Wg8REjw1ebnUUrz53lZrkDwKtJh8KtODQtuGTTk2JeWKxC8inQYfF4f849uuZYrELyKdBj80QWmz3e3+COWKxC8inQYfLe83ENpWTfynrJcoST4GiHtOIfjpHQYPPGJTViSEOtj9biC4CMzP1q+vQbvgJyRHoO3Ep1tdnWr3IbeB41fDSIXqD4gJ+AQwRebt05ui86JxoVhj4CxODwdBt+3hOUK+cE3W2pcVNvBOSCnpDB4f9qMCNuqp8UsV8gP3rAlklT/fBDngJxSafC1P05Pe9Hm0wx+5f8uMnjDqiNeZpYr5AdPai84vN/2v8qVlQRv2Pwc8fmq9Hcj7sDG/MzWhIzNuWf8EXV5258k/NidFD8SdSXht3PPkT8pbSvsM35QuvTjCoIHW0qCD1hmXHinlDwcRyc2WHjWsy19J+LgdhJFN/fzL5wennrcLYrG11iUQzrTbh4OsHEHNpUEb7H9G/ergdSiPf0GG5quzzYG/xcSf75unRDaJIr6kP4FQt/qbULwKioJ3vuQ8SO1V+k33rijxsWdV3yW/37zqjH4+4SsNG9vhUQVGje7EbzjK92465X5wdJtpfu44q65k/o09N2cjm4fGoMvICQxgxDfaG/THxG8E3j0da7cXu04Oj9sy1nPuWeCX8w5E2BKu+ODcT2SfzaUBt/PG8E7Mhvf4+Muptw62IbUT7/747gjb5nSJgNP3NkVaP7l732cuG3/oxWCd2S2gv+OpTGCd1wI3kXpcF+9TQheRQjeRSF4F+Xkwdf+GAdppDl38ObDsktxWFaCcwcf+IVxUS2l0ue5IOcOvssc48KQIWAsDm/QkVTlfhwiUVFPwVfbbzr09C8RgwFLegqe9Mr8aPk26xO1QQBdBY8JFfajr+DBbhC8i0LwLgrBuyhVgq/etYWb1YMIXte4gz8VSXrdoPRgI8sVCF7XuIOn0R7XP2vWZufXpY80jjDb+G3Zpz2esGYyJkDriArBN6c1CQnLL33k6SlmB8rOfmzyfUid6L2enH2BeviDjwu825mQNy9Zrij3Vj8jzLiY0o+zL1APd/A5Dyk9TGY8fNNyRbngl/sbF9HjOfsC9fBv1Xu17jeUTLT+ZS4X/JsjjYslT/P2Baqx0/d4r9T4lz5bJKKb+tZfJIGBvXbguIW91klAJyNv5t1NFlDX+Tn2nruWNwNIlaPzxXfkfFiCzy8hq7I9gp+1ybh48oL4jpwPS/DRJWRVtkfwH5je5oPOie/I+ch4qw94S1ZlewQfkGd8qz8yS3xHzoct+Nbbs7Ozz1hdrrZCdtlXb9q422SHfpwPW/BZWRuufpgzTlZlOx2kEfR1rq6vkLL6wRb8H+H+N91eypZV2ZGPzj2Rtmr91npaj0IotuAv/8OQ26HzXVmVHTn4nc0JeXqV1qMQii348XTwipyzabIqO3DwNc37hJx7ZgfjVn07P59359aXVdmBg/faaVru1XgUYjn2njtR1vcnZKxzT+lhC/43s32yKjty8L6Ld+5KsLo6r1NhCz4mJmbYlGv/lFXZkYN3ATLe6kN/kVUZweuajOBjb8mq7NDBh7w/5QmtxyAW+2f8DfqprMqOHPyIjc+Epz2n9SiEYv6Mj4mJlLdz1IGDN2RWIcTHxnX4nQRL8LWL1ZRV2YGDr7vBtHTuuyKxBF9y15mfZVV24OAN33kQUstxgu++O2OX1Q0fK8ESfHDw6gvDQl4+96GsyhbBtwv3l9VcU0OTn+2XGqb1KFi1SK1Xq0F6oLxGbJ/xl0xnTz9/WVblcsG7rV05NeVdWe011f69KQFaj4HZu/Ozv9n3kbzTZFiPzpkOxY/JkV7p3rB9I3frh8sF/9rbxsWa9vLGBmyWZngQz8yFMhsxBf9O4eoPVhW+LbWq2vw848d//ryKbz/2hemNaPAYeWMDNst2uBG3DJmzFtiCNwxM/mHTAIPUqlWnhgW3DBpy7EvLFeWCn9PduHhT3tmawChu8d7P9y1+R14jluAbVDS/Oa+P+Ue33NJHIpPMjl9MSgomLZPMy/VX1gU/k72szCNYqrb8+mKYX9dL6+W1OsFwfTE6u6CI1MqjC0yf727xj87ErBlotiwlMNCbVA0sWvbetm+RX2DZR7BUbTk8KyMzRmar9c0qD37801FFpFZ2y8s9lJZ1I8/qe6QDf493BYwHaZ6u/9jcqdIHqH1iE5YkxFpfjxLB6xpb8PG0+6eX/uM6B2lcAFvwN16vXjjg+WuyKiN4XWMLPrdv73u1Il3oeLzzYwv+s9xrC8Ouf1v5E8tA8LrGFrznhGm1eiXKm1WE4HWNcaverUmIt+SOO9sQvK6xBe/3E6XRP8k78IfgdY0t+B2p3alv8k5ZlRG8rrEFfzesDiU9bsuqjOB1jS3449PqUjLeZU69cgVswT9beIFmPBggqzKC1zXGrfqA+JWJHeRVRvC6Jme2bFtZlRG8rrEE32TLhWW13/k2+09ZlRG8rrEEv/X8nH15N9Z9InWLUtsQvK6xBH97DGlG/y63MoLXNaaZNM8SA42QWxnB6xpT8BFF/8mD4HWNKfjZMTGm/2JkVUbwuiZn0iSVVRnB6xpL8F4lZFVG8LqGy525KNxi1EXhFqMuSsAtRosheF0TcIvRYghe1+x0i1HQGwG3GH0h1eyic18uzNHZ6RajoDfqfI+fXcf6MdvBN01Jz/oEtxTXljrBU4lZ9raD392SkDEz1OgXFNMg+Mbmm704940/9E+d4D+TuGOTzeD9VpiWe9XoFxTTYl/9XuNbfewcUf0CEy2C90/ZcQAbdxrT5uhctSqiegVGOCzrohC8i0LwLgrBuygE76IQvItC8C4KwbsoBO+iNAm+5Rt/l3exRFCdFsHHbh748v7WovoFJhoEX/WQOyFNt4jqF5hoEHyrxaal49zH0TmpEDzLfefK8k4zLmrKu0wmqI07eLb7zpUzc36rp9J7cvYLfLiDZ7vvXHl95s3Etp3GuIO3vu9cMXyP1zXu4K3vO1cMwesad/C475xj4t+qx33nHBL21bsoAcHXizDbuFH1yqAe7uD7lih9pNsUs81JnJVBJP6telsXwRs8lrMyiMQdvGHVEemL4CF4XeP/jB9k48oXCF7XxG3VI3hdE3dFDASva+IujIDgdQ3BuyhxV8RA8LqGjTsXheBdFIJ3UQjeRSF4F4XgXRSCd1Higu91OrucP+7yKORq/UcBV/P7XK0L7/G05hz6nWwbTlvdS0YUvjlTKx/nad3vba7O+Yb+pT9P6+encHWug5lqCF4RBM/TGsFrCMErguB5WiN4DSF4RRw/+N1crZdzffvoPZGrc76hf8H1/2yvf3B1zjd0VVTVsLWbh4adO/DQAQAAAAAAAMClPHUkd4XVTGpmOZTSUwrbGo725ei/qLXS/geevJPeRnHnxa0Vdj76wq3kRrwvPDefvIV9T81T2trrYVx0dKSytoNW0b7K+y9qrbT/Zvc/6L3jgpfCzotbK+y888PR/3XyW84Xnl9snjsZlidxxVsmrWlAc6V7LtOPmKJT2n9Ra6X9j79XlbSgQQo7L26tsPP3jhMy9QrnC88v4RAhwbSBwtb96F16vZfCxl6m6BT3b26ttP+WPQgZUeCjsPPi1go7r9+0Yd/01ZwvPL8lacZ/CG2vsPXQw11qJd2orqyxOTrF/ZtbK+/fa+qDKRydm1or7jye0qc4X3h+s7IICaI8h1ab0a7KGpqjU9y/ubXi/tufuDZUeedFrRV2XsWdGMbdqML/wvMZft2NDLmp9KNm4gxC2tAWyhqbo1Pcv7m10v4bXltbmyjuvLi1ws43rDZu4NGmfC88P5/8+K5HFW9c9n84IWznITdljc3RKe7f3Fpp///MHxgVFeWrsPPi1go7j7n9cmjKTwa+F14F3Y7lLVf+dXLSlZsblb5dFb1ZK+2/qLXC/pPNFwAMVth5SWtlnRvir9zaGsj7wgMAAAAAAAAAAAAAAAAAAAAAAAAAVOBy0e2S12k4EwW08ExEJJ0bEdF7dg2tRwJ2VoUOJySqgPS9/nne4ZeP315oIOHH7qT4aT0uEKw0ePpG3dP5Af1oO//C6eGpxxVO7gBH8Sj4GmTD18Sbhsafr1snhDbRemAgVmnwfxKybinxoqErzdt7IVoPDMSyDj4xgxDfaG+tBwZiWQff8cG4Hsk/G7QeGIhlGfzVTmTgiTu7ArUeFwAAAAAAAAAAAAAAAAAAAAAAAOjP/wMUAzTtJizWfwAAAABJRU5ErkJggg==" alt="plot of chunk unnamed-chunk-9"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-9"/> </p> <p>In the FOCUS kinetics report, it is stated that there is no apparent systematic error observed from the residual plot up to the measured DT90 (approximately at @@ -543,22 +554,22 @@ plot(m.L2.FOMC) mkinresplot(m.L2.FOMC) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-10"/> </p> <pre><code class="r">summary(m.L2.FOMC, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:36 2014 -## Date of summary: Tue Oct 14 22:03:36 2014 +## Date of fit: Wed Oct 15 00:58:17 2014 +## Date of summary: Wed Oct 15 00:58:17 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 35 model solutions performed in 0.192 s +## Fitted with method Port using 81 model solutions performed in 0.438 s ## ## Weighting: none ## @@ -617,7 +628,7 @@ experimental error has to be assumed in order to explain the data.</p> plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-11"/> </p> <p>Here, the default starting parameters for the DFOP model obviously do not lead to a reasonable solution. Therefore the fit is repeated with different starting @@ -629,15 +640,15 @@ parameters.</p> plot(m.L2.DFOP) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-12"/> </p> <pre><code class="r">summary(m.L2.DFOP, data = FALSE) </code></pre> <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:36 2014 -## Date of summary: Tue Oct 14 22:03:36 2014 +## Date of fit: Wed Oct 15 00:58:21 2014 +## Date of summary: Wed Oct 15 00:58:21 2014 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -646,7 +657,7 @@ plot(m.L2.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 43 model solutions performed in 0.24 s +## Fitted with method Port using 336 model solutions performed in 1.844 s ## ## Weighting: none ## @@ -669,8 +680,8 @@ plot(m.L2.DFOP) ## ## Optimised, transformed parameters: ## Estimate Std. Error Lower Upper t value Pr(>|t|) Pr(>t) -## parent_0 94.000 NA NA NA NA NA NA -## log_k1 6.160 NA NA NA NA NA NA +## parent_0 93.900 NA NA NA NA NA NA +## log_k1 3.120 NA NA NA NA NA NA ## log_k2 -1.090 NA NA NA NA NA NA ## g_ilr -0.282 NA NA NA NA NA NA ## @@ -681,8 +692,8 @@ plot(m.L2.DFOP) ## ## Backtransformed parameters: ## Estimate Lower Upper -## parent_0 94.000 NA NA -## k1 476.000 NA NA +## parent_0 93.900 NA NA +## k1 22.700 NA NA ## k2 0.337 NA NA ## g 0.402 NA NA ## @@ -693,7 +704,7 @@ plot(m.L2.DFOP) ## ## Estimated disappearance times: ## DT50 DT90 DT50_k1 DT50_k2 -## parent NA NA 0.00146 2.06 +## parent NA NA 0.0306 2.06 </code></pre> <p>Here, the DFOP model is clearly the best-fit model for dataset L2 based on the @@ -718,22 +729,22 @@ FOCUS_2006_L3_mkin <- mkin_wide_to_long(FOCUS_2006_L3) plot(m.L3.SFO) </code></pre> -<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p> +<p><img src="data:image/png;base64,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" alt="plot of chunk unnamed-chunk-14"/> </p> <pre><code class="r">summary(m.L3.SFO) </code></pre> <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:37 2014 -## Date of summary: Tue Oct 14 22:03:37 2014 +## Date of fit: Wed Oct 15 00:58:22 2014 +## Date of summary: Wed Oct 15 00:58:22 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 44 model solutions performed in 0.237 s +## Fitted with method Port using 43 model solutions performed in 0.232 s ## ## Weighting: none ## @@ -785,14 +796,14 @@ plot(m.L3.SFO) ## ## Data: ## time variable observed predicted residual -## 0 parent 97.8 74.87 22.9274 -## 3 parent 60.0 69.41 -9.4065 +## 0 parent 97.8 74.87 22.9281 +## 3 parent 60.0 69.41 -9.4061 ## 7 parent 51.0 62.73 -11.7340 -## 14 parent 43.0 52.56 -9.5634 -## 30 parent 35.0 35.08 -0.0828 -## 60 parent 22.0 16.44 5.5614 -## 91 parent 15.0 7.51 7.4896 -## 120 parent 12.0 3.61 8.3908 +## 14 parent 43.0 52.56 -9.5638 +## 30 parent 35.0 35.08 -0.0839 +## 60 parent 22.0 16.44 5.5602 +## 91 parent 15.0 7.51 7.4887 +## 120 parent 12.0 3.61 8.3903 </code></pre> <p>The chi<sup>2</sup> error level of 21% as well as the plot suggest that the model @@ -811,15 +822,15 @@ plot(m.L3.FOMC) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:37 2014 -## Date of summary: Tue Oct 14 22:03:37 2014 +## Date of fit: Wed Oct 15 00:58:22 2014 +## Date of summary: Wed Oct 15 00:58:22 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 26 model solutions performed in 0.139 s +## Fitted with method Port using 83 model solutions performed in 0.442 s ## ## Weighting: none ## @@ -884,8 +895,8 @@ plot(m.L3.DFOP) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:37 2014 -## Date of summary: Tue Oct 14 22:03:37 2014 +## Date of fit: Wed Oct 15 00:58:23 2014 +## Date of summary: Wed Oct 15 00:58:23 2014 ## ## Equations: ## d_parent = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) * exp(-k2 * @@ -894,7 +905,7 @@ plot(m.L3.DFOP) ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 37 model solutions performed in 0.207 s +## Fitted with method Port using 137 model solutions performed in 0.778 s ## ## Weighting: none ## @@ -982,15 +993,15 @@ plot(m.L4.SFO) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:38 2014 -## Date of summary: Tue Oct 14 22:03:38 2014 +## Date of fit: Wed Oct 15 00:58:24 2014 +## Date of summary: Wed Oct 15 00:58:24 2014 ## ## Equations: ## d_parent = - k_parent_sink * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 20 model solutions performed in 0.106 s +## Fitted with method Port using 46 model solutions performed in 0.246 s ## ## Weighting: none ## @@ -1057,15 +1068,15 @@ plot(m.L4.FOMC) <pre><code>## mkin version: 0.9.34 ## R version: 3.1.1 -## Date of fit: Tue Oct 14 22:03:38 2014 -## Date of summary: Tue Oct 14 22:03:38 2014 +## Date of fit: Wed Oct 15 00:58:24 2014 +## Date of summary: Wed Oct 15 00:58:24 2014 ## ## Equations: ## d_parent = - (alpha/beta) * ((time/beta) + 1)^-1 * parent ## ## Model predictions using solution type analytical ## -## Fitted with method Marq using 48 model solutions performed in 0.26 s +## Fitted with method Port using 66 model solutions performed in 0.359 s ## ## Weighting: none ## diff --git a/vignettes/FOCUS_Z.pdf b/vignettes/FOCUS_Z.pdf index b5898b7c..0013cd5e 100644 Binary files a/vignettes/FOCUS_Z.pdf and b/vignettes/FOCUS_Z.pdf differ -- cgit v1.2.1