From 5b1179f0daff9077e1370065f9b9f03a2d0e0450 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 15 Oct 2020 12:53:23 +0200 Subject: Update online docs for release version --- docs/reference/AIC.mmkin.html | 50 ++- docs/reference/DFOP.solution-1.png | Bin 19369 -> 31078 bytes docs/reference/DFOP.solution.html | 16 +- docs/reference/FOMC.solution-1.png | Bin 18630 -> 28900 bytes docs/reference/FOMC.solution.html | 16 +- docs/reference/HS.solution-1.png | Bin 18893 -> 29354 bytes docs/reference/HS.solution.html | 16 +- docs/reference/IORE.solution-1.png | Bin 18668 -> 30028 bytes docs/reference/IORE.solution.html | 36 +- docs/reference/Rplot001.png | Bin 0 -> 1011 bytes docs/reference/Rplot002.png | Bin 0 -> 17401 bytes docs/reference/Rplot003.png | Bin 0 -> 15601 bytes docs/reference/Rplot004.png | Bin 0 -> 10728 bytes docs/reference/Rplot005.png | Bin 0 -> 19636 bytes docs/reference/Rplot006.png | Bin 0 -> 24813 bytes docs/reference/Rplot007.png | Bin 0 -> 25161 bytes docs/reference/SFO.solution-1.png | Bin 18661 -> 29201 bytes docs/reference/SFO.solution.html | 16 +- docs/reference/SFORB.solution-1.png | Bin 20190 -> 31408 bytes docs/reference/SFORB.solution.html | 16 +- docs/reference/add_err-1.png | Bin 111278 -> 111359 bytes docs/reference/add_err-2.png | Bin 63031 -> 64003 bytes docs/reference/add_err-3.png | Bin 60608 -> 60019 bytes docs/reference/add_err.html | 70 +-- docs/reference/confint.mkinfit.html | 219 ++++++---- docs/reference/create_deg_func.html | 61 +-- docs/reference/endpoints.html | 41 +- docs/reference/get_deg_func.html | 13 +- docs/reference/ilr.html | 47 +- docs/reference/index.html | 9 +- docs/reference/logLik.mkinfit.html | 33 +- docs/reference/logistic.solution-1.png | Bin 80598 -> 80293 bytes docs/reference/logistic.solution-2.png | Bin 29336 -> 43807 bytes docs/reference/logistic.solution.html | 84 ++-- docs/reference/max_twa_parent.html | 29 +- docs/reference/mccall81_245T.html | 55 ++- docs/reference/mkinds.html | 40 +- docs/reference/mkinerrplot-1.png | Bin 41458 -> 41095 bytes docs/reference/mkinerrplot.html | 49 ++- docs/reference/mkinfit.html | 776 ++++++++++----------------------- docs/reference/mkinmod.html | 146 ++++--- docs/reference/mkinparplot-1.png | Bin 16468 -> 25704 bytes docs/reference/mkinparplot.html | 27 +- docs/reference/mkinpredict.html | 203 +++++---- docs/reference/mkinresplot-1.png | Bin 14861 -> 23814 bytes docs/reference/mkinresplot.html | 51 ++- docs/reference/mkinsub.html | 37 +- docs/reference/mmkin-1.png | Bin 115683 -> 114052 bytes docs/reference/mmkin-2.png | Bin 113464 -> 110390 bytes docs/reference/mmkin-3.png | Bin 100817 -> 97548 bytes docs/reference/mmkin-4.png | Bin 70430 -> 70005 bytes docs/reference/mmkin-5.png | Bin 66959 -> 66093 bytes docs/reference/mmkin.html | 95 ++-- docs/reference/nafta-1.png | Bin 41379 -> 64837 bytes docs/reference/nafta.html | 43 +- docs/reference/nlme-1.png | Bin 71631 -> 69449 bytes docs/reference/nlme.html | 109 ++--- docs/reference/nlme.mmkin-1.png | Bin 81816 -> 79142 bytes docs/reference/nlme.mmkin-2.png | Bin 82384 -> 79539 bytes docs/reference/nlme.mmkin-3.png | Bin 82591 -> 79703 bytes docs/reference/nlme.mmkin-4.png | Bin 86006 -> 82209 bytes docs/reference/nlme.mmkin-5.png | Bin 85418 -> 81513 bytes docs/reference/nlme.mmkin-6.png | Bin 84796 -> 80989 bytes docs/reference/nlme.mmkin-7.png | Bin 84926 -> 81584 bytes docs/reference/nlme.mmkin.html | 240 +++++----- docs/reference/parms.html | 123 +++++- docs/reference/plot.mkinfit-1.png | Bin 53973 -> 53731 bytes docs/reference/plot.mkinfit-2.png | Bin 75079 -> 73822 bytes docs/reference/plot.mkinfit-3.png | Bin 70266 -> 69215 bytes docs/reference/plot.mkinfit-4.png | Bin 74166 -> 73285 bytes docs/reference/plot.mkinfit-5.png | Bin 68692 -> 68646 bytes docs/reference/plot.mkinfit-6.png | Bin 75012 -> 74041 bytes docs/reference/plot.mkinfit-7.png | Bin 75692 -> 75295 bytes docs/reference/plot.mkinfit.html | 126 +++--- docs/reference/plot.mmkin-1.png | Bin 34273 -> 50628 bytes docs/reference/plot.mmkin-2.png | Bin 34629 -> 50911 bytes docs/reference/plot.mmkin-3.png | Bin 32259 -> 47267 bytes docs/reference/plot.mmkin-4.png | Bin 25550 -> 34181 bytes docs/reference/plot.mmkin-5.png | Bin 38129 -> 58620 bytes docs/reference/plot.mmkin.html | 62 +-- docs/reference/plot.nlme.mmkin-1.png | Bin 35382 -> 52802 bytes docs/reference/plot.nlme.mmkin-2.png | Bin 35346 -> 52826 bytes docs/reference/plot.nlme.mmkin.html | 60 +-- docs/reference/print.mkinds.html | 13 +- docs/reference/sigma_twocomp.html | 13 +- docs/reference/summary.mkinfit.html | 35 +- 86 files changed, 1596 insertions(+), 1479 deletions(-) create mode 100644 docs/reference/Rplot001.png create mode 100644 docs/reference/Rplot002.png create mode 100644 docs/reference/Rplot003.png create mode 100644 docs/reference/Rplot004.png create mode 100644 docs/reference/Rplot005.png create mode 100644 docs/reference/Rplot006.png create mode 100644 docs/reference/Rplot007.png (limited to 'docs/reference') diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 58590f52..26a880e3 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -73,7 +73,7 @@ same dataset." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -110,6 +110,9 @@ same dataset." />
  • Example evaluation of NAFTA SOP Attachment examples
    • +
  • + Some benchmark timings +
  • @@ -118,7 +121,7 @@ same dataset." />
  • @@ -123,7 +128,7 @@ likelihood function." />
    • - + @@ -142,53 +147,55 @@ likelihood function." />

      Fit a kinetic model to data with one or more state variables

      - Source: R/mkinfit.R + Source: R/mkinfit.R

      This function maximises the likelihood of the observed data using the Port -algorithm nlminb, and the specified initial or fixed +algorithm stats::nlminb(), and the specified initial or fixed parameters and starting values. In each step of the optimisation, the -kinetic model is solved using the function mkinpredict. The +kinetic model is solved using the function mkinpredict(), except +if an analytical solution is implemented, in which case the model is solved +using the degradation function in the mkinmod object. The parameters of the selected error model are fitted simultaneously with the degradation model parameters, as both of them are arguments of the likelihood function.

      - 
      mkinfit(
-  mkinmod,
-  observed,
-  parms.ini = "auto",
-  state.ini = "auto",
-  err.ini = "auto",
-  fixed_parms = NULL,
-  fixed_initials = names(mkinmod$diffs)[-1],
-  from_max_mean = FALSE,
-  solution_type = c("auto", "analytical", "eigen", "deSolve"),
-  method.ode = "lsoda",
-  use_compiled = "auto",
-  control = list(eval.max = 300, iter.max = 200),
-  transform_rates = TRUE,
-  transform_fractions = TRUE,
-  quiet = FALSE,
-  atol = 1e-08,
-  rtol = 1e-10,
-  error_model = c("const", "obs", "tc"),
-  error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep",
-    "fourstep", "IRLS", "OLS"),
-  reweight.tol = 1e-08,
-  reweight.max.iter = 10,
-  trace_parms = FALSE,
-  ...
-)
      + 
      mkinfit(
+  mkinmod,
+  observed,
+  parms.ini = "auto",
+  state.ini = "auto",
+  err.ini = "auto",
+  fixed_parms = NULL,
+  fixed_initials = names(mkinmod$diffs)[-1],
+  from_max_mean = FALSE,
+  solution_type = c("auto", "analytical", "eigen", "deSolve"),
+  method.ode = "lsoda",
+  use_compiled = "auto",
+  control = list(eval.max = 300, iter.max = 200),
+  transform_rates = TRUE,
+  transform_fractions = TRUE,
+  quiet = FALSE,
+  atol = 1e-08,
+  rtol = 1e-10,
+  error_model = c("const", "obs", "tc"),
+  error_model_algorithm = c("auto", "d_3", "direct", "twostep", "threestep",
+    "fourstep", "IRLS", "OLS"),
+  reweight.tol = 1e-08,
+  reweight.max.iter = 10,
+  trace_parms = FALSE,
+  ...
+)

      Arguments

      - +numerical ode solver from package deSolve is used. If +set to "analytical", an analytical solution of the model is used. This is +only implemented for relatively simple degradation models. The default is +"auto", which uses "analytical" if possible, otherwise "deSolve" if a +compiler is present, and "eigen" if no compiler is present and the model +can be expressed using eigenvalues and eigenvectors.

      - +mkinmod model is used in the calls to mkinpredict() even if a compiled +version is present.

      - + @@ -306,7 +311,7 @@ fitting for better compliance with the assumption of normal distribution of the estimator. The default (TRUE) is to do transformations. If TRUE, the g parameter of the DFOP and HS models are also transformed, as they can also be seen as compositional data. The transformation used for these -transformations is the ilr transformation.

      +transformations is the ilr() transformation.

      @@ -315,13 +320,14 @@ log-likelihood after each improvement?

      - + - + @@ -342,11 +348,9 @@ normal distribution as assumed by this method.

      the error model. If the error model is "const", unweighted nonlinear least squares fitting ("OLS") is selected. If the error model is "obs", or "tc", the "d_3" algorithm is selected.

      -

      The algorithm "d_3" will directly minimize the negative log-likelihood and

        -

      • independently - also use the three step algorithm described below. The -fit with the higher likelihood is returned.

        • -
      - +

      The algorithm "d_3" will directly minimize the negative log-likelihood +and independently also use the three step algorithm described below. +The fit with the higher likelihood is returned.

      The algorithm "direct" will directly minimize the negative log-likelihood.

      The algorithm "twostep" will minimize the negative log-likelihood after an initial unweighted least squares optimisation step.

      @@ -381,14 +385,13 @@ the error model parameters in IRLS fits.

      +deSolve::ode().

      mkinmod

      A list of class mkinmod, containing the kinetic +

      A list of class mkinmod, containing the kinetic model to be fitted to the data, or one of the shorthand names ("SFO", "FOMC", "DFOP", "HS", "SFORB", "IORE"). If a shorthand name is given, a parent only degradation model is generated for the variable with the @@ -224,7 +231,7 @@ given below.

      A named vector of initial values for the state variables of the model. In case the observed variables are represented by more than one model variable, the names will differ from the names of the observed -variables (see map component of mkinmod). The default +variables (see map component of mkinmod). The default is to set the initial value of the first model variable to the mean of the time zero values for the variable with the maximum observed value, and all others to 0. If this variable has no time zero observations, its initial @@ -263,30 +270,28 @@ observed mean value is the new time zero.

      If set to "eigen", the solution of the system of differential equations is based on the spectral decomposition of the coefficient matrix in cases that this is possible. If set to "deSolve", a -numerical ode solver from package deSolve is used. If set to -"analytical", an analytical solution of the model is used. This is only -implemented for simple degradation experiments with only one state -variable, i.e. with no metabolites. The default is "auto", which uses -"analytical" if possible, otherwise "deSolve" if a compiler is present, -and "eigen" if no compiler is present and the model can be expressed using -eigenvalues and eigenvectors. This argument is passed on to the helper -function mkinpredict.
      method.ode

      The solution method passed via mkinpredict -to ode in case the solution type is "deSolve". The default +

      The solution method passed via mkinpredict() +to deSolve::ode() in case the solution type is "deSolve". The default "lsoda" is performant, but sometimes fails to converge.
      use_compiled

      If set to FALSE, no compiled version of the -mkinmod model is used in the calls to -mkinpredict even if a compiled version is present.
      control

      A list of control arguments passed to nlminb.

      A list of control arguments passed to stats::nlminb().
      transform_rates
      quiet
      atol

      Absolute error tolerance, passed to ode. Default -is 1e-8, lower than in lsoda.

      Absolute error tolerance, passed to deSolve::ode(). Default +is 1e-8, which is lower than the default in the deSolve::lsoda() +function which is used per default.
      rtol

      Absolute error tolerance, passed to ode. Default -is 1e-10, much lower than in lsoda.

      Absolute error tolerance, passed to deSolve::ode(). Default +is 1e-10, much lower than in deSolve::lsoda().
      error_model
      ...

      Further arguments that will be passed on to -deSolve.

      Value

      -

      A list with "mkinfit" in the class attribute. A summary can be -obtained by summary.mkinfit.

      +

      A list with "mkinfit" in the class attribute.

      Details

      Per default, parameters in the kinetic models are internally transformed in @@ -409,28 +412,31 @@ Degradation Data. Environments 6(12) 124 doi:10.3390/environments6120124.

      See also

      -

      Plotting methods plot.mkinfit and -mkinparplot.

      +

      summary.mkinfit, plot.mkinfit, parms and lrtest.

      Comparisons of models fitted to the same data can be made using AIC by virtue of the method logLik.mkinfit.

      Fitting of several models to several datasets in a single call to mmkin.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # Use shorthand notation for parent only degradation
-fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-summary(fit)
      #> mkin version used for fitting:    0.9.50.2 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 07:03:45 2020 
-#> Date of summary: Wed May 27 07:03:45 2020 
+fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+summary(fit)
+
      #> mkin version used for fitting:    0.9.50.3 
+#> R version used for fitting:       4.0.3 
+#> Date of fit:     Thu Oct 15 12:40:10 2020 
+#> Date of summary: Thu Oct 15 12:40:10 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 222 model solutions performed in 0.043 s
+#> Fitted using 222 model solutions performed in 0.045 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -465,10 +471,10 @@ Degradation Data. Environments 6(12) 124
 #> 
 #> Parameter correlation:
 #>             parent_0  log_alpha   log_beta     sigma
-#> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.770e-08
-#> log_alpha -1.565e-01  1.000e+00  9.564e-01 9.974e-08
-#> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.468e-08
-#> sigma      4.770e-08  9.974e-08  8.468e-08 1.000e+00
+#> parent_0   1.000e+00 -1.565e-01 -3.142e-01 4.758e-08
+#> log_alpha -1.565e-01  1.000e+00  9.564e-01 1.007e-07
+#> log_beta  -3.142e-01  9.564e-01  1.000e+00 8.568e-08
+#> sigma      4.758e-08  1.007e-07  8.568e-08 1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
@@ -501,426 +507,129 @@ Degradation Data. Environments 6(12) 124
 #>    91   parent      3.9     1.441   2.4590
 #>   119   parent      0.6     1.092  -0.4919
      
 # One parent compound, one metabolite, both single first order.
+# We remove zero values from FOCUS dataset D in order to avoid warnings
+FOCUS_D <- subset(FOCUS_2006_D, value != 0)
 # Use mkinsub for convenience in model formulation. Pathway to sink included per default.
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      # Fit the model to the FOCUS example dataset D using defaults
-print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D,
-                           solution_type = "eigen", quiet = TRUE)))
      #> Warning: Observations with value of zero were removed from the data
      #>    user  system elapsed 
-#>   0.400   0.004   0.404 
      parms(fit)
      #>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
-#>   99.598481046    0.098697740    0.005260651    0.514475962    3.125503875 
      endpoints(fit)
      #> $ff
-#>   parent_m1 parent_sink 
-#>    0.514476    0.485524 
-#> 
-#> $distimes
-#>              DT50      DT90
-#> parent   7.022929  23.32966
-#> m1     131.760724 437.69965
-#> 
      # \dontrun{
-# deSolve is slower when no C compiler (gcc) was available during model generation
-print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D,
-                           solution_type = "deSolve")))
      #> Warning: Observations with value of zero were removed from the data
      #> Ordinary least squares optimisation
      #> Sum of squared residuals at call 1: 15156.12
-#> Sum of squared residuals at call 2: 15156.12
-#> Sum of squared residuals at call 6: 8243.645
-#> Sum of squared residuals at call 12: 6290.712
-#> Sum of squared residuals at call 13: 6290.683
-#> Sum of squared residuals at call 15: 6290.452
-#> Sum of squared residuals at call 18: 1700.749
-#> Sum of squared residuals at call 20: 1700.611
-#> Sum of squared residuals at call 24: 1190.923
-#> Sum of squared residuals at call 26: 1190.922
-#> Sum of squared residuals at call 29: 1017.417
-#> Sum of squared residuals at call 31: 1017.417
-#> Sum of squared residuals at call 33: 1017.416
-#> Sum of squared residuals at call 34: 644.0472
-#> Sum of squared residuals at call 36: 644.047
-#> Sum of squared residuals at call 38: 644.047
-#> Sum of squared residuals at call 39: 590.5025
-#> Sum of squared residuals at call 41: 590.5022
-#> Sum of squared residuals at call 43: 590.5016
-#> Sum of squared residuals at call 44: 543.2196
-#> Sum of squared residuals at call 45: 543.2193
-#> Sum of squared residuals at call 46: 543.2192
-#> Sum of squared residuals at call 50: 391.348
-#> Sum of squared residuals at call 51: 391.3479
-#> Sum of squared residuals at call 56: 386.479
-#> Sum of squared residuals at call 58: 386.479
-#> Sum of squared residuals at call 60: 386.4779
-#> Sum of squared residuals at call 61: 384.0686
-#> Sum of squared residuals at call 63: 384.0686
-#> Sum of squared residuals at call 66: 382.7813
-#> Sum of squared residuals at call 68: 382.7813
-#> Sum of squared residuals at call 70: 382.7813
-#> Sum of squared residuals at call 71: 378.9273
-#> Sum of squared residuals at call 73: 378.9273
-#> Sum of squared residuals at call 75: 378.9272
-#> Sum of squared residuals at call 76: 377.4847
-#> Sum of squared residuals at call 78: 377.4846
-#> Sum of squared residuals at call 81: 375.9738
-#> Sum of squared residuals at call 83: 375.9738
-#> Sum of squared residuals at call 86: 375.3387
-#> Sum of squared residuals at call 88: 375.3387
-#> Sum of squared residuals at call 91: 374.5774
-#> Sum of squared residuals at call 93: 374.5774
-#> Sum of squared residuals at call 95: 374.5774
-#> Sum of squared residuals at call 96: 373.5438
-#> Sum of squared residuals at call 100: 373.5438
-#> Sum of squared residuals at call 102: 373.265
-#> Sum of squared residuals at call 104: 373.265
-#> Sum of squared residuals at call 107: 372.6825
-#> Sum of squared residuals at call 111: 372.6825
-#> Sum of squared residuals at call 114: 372.6356
-#> Sum of squared residuals at call 116: 372.6356
-#> Sum of squared residuals at call 119: 372.6199
-#> Sum of squared residuals at call 121: 372.6199
-#> Sum of squared residuals at call 123: 372.6199
-#> Sum of squared residuals at call 124: 372.5881
-#> Sum of squared residuals at call 126: 372.5881
-#> Sum of squared residuals at call 129: 372.5418
-#> Sum of squared residuals at call 130: 372.4866
-#> Sum of squared residuals at call 131: 372.2242
-#> Sum of squared residuals at call 132: 371.5237
-#> Sum of squared residuals at call 134: 371.5237
-#> Sum of squared residuals at call 137: 371.292
-#> Sum of squared residuals at call 139: 371.292
-#> Sum of squared residuals at call 143: 371.2256
-#> Sum of squared residuals at call 144: 371.2256
-#> Sum of squared residuals at call 146: 371.2256
-#> Sum of squared residuals at call 149: 371.2194
-#> Sum of squared residuals at call 150: 371.2147
-#> Sum of squared residuals at call 153: 371.2147
-#> Sum of squared residuals at call 155: 371.2137
-#> Sum of squared residuals at call 156: 371.2137
-#> Sum of squared residuals at call 157: 371.2137
-#> Sum of squared residuals at call 160: 371.2134
-#> Sum of squared residuals at call 164: 371.2134
-#> Sum of squared residuals at call 165: 371.2134
-#> Sum of squared residuals at call 167: 371.2134
-#> Negative log-likelihood at call 177: 97.22429
      #> Optimisation successfully terminated.
      #>    user  system elapsed 
-#>   0.360   0.000   0.361 
      parms(fit.deSolve)
      #>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
-#>   99.598480300    0.098697739    0.005260651    0.514475968    3.125503874 
      endpoints(fit.deSolve)
      #> $ff
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
+# Fit the model quietly to the FOCUS example dataset D using defaults
+fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
      # Since mkin 0.9.50.3, we get a warning about non-normality of residuals,
+# so we try an alternative error model
+fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc")
+# This avoids the warning, and the likelihood ratio test confirms it is preferable
+lrtest(fit.tc, fit)
+
      #> Likelihood ratio test
+#> 
+#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0
+#> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0
+#>   #Df  LogLik Df  Chisq Pr(>Chisq)    
+#> 1   6 -64.983                         
+#> 2   5 -97.224 -1 64.483  9.737e-16 ***
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
      # We can also allow for different variances of parent and metabolite as error model
+fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "obs")
+# This also avoids the warning about non-normality, but the two-component error model
+# has significantly higher likelihood
+lrtest(fit.obs, fit.tc)
+
      #> Likelihood ratio test
+#> 
+#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0
+#> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0
+#>   #Df  LogLik Df  Chisq Pr(>Chisq)    
+#> 1   6 -64.983                         
+#> 2   6 -96.936  0 63.907  < 2.2e-16 ***
+#> ---
+#> Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
      parms(fit.tc)
+
      #>       parent_0       k_parent           k_m1 f_parent_to_m1      sigma_low 
+#>   1.007343e+02   1.005562e-01   5.166712e-03   5.083933e-01   3.049891e-03 
+#>       rsd_high 
+#>   7.928117e-02 
      endpoints(fit.tc)
+
      #> $ff
 #>   parent_m1 parent_sink 
-#>    0.514476    0.485524 
+#>   0.5083933   0.4916067 
 #> 
 #> $distimes
-#>              DT50      DT90
-#> parent   7.022929  23.32966
-#> m1     131.760721 437.69964
-#> 
      # }
-
-# Use stepwise fitting, using optimised parameters from parent only fit, FOMC
+#>             DT50      DT90
+#> parent   6.89313  22.89848
+#> m1     134.15635 445.65776
+#> 
      
+# We can show a quick (only one replication) benchmark for this case, as we
+# have several alternative solution methods for the model. We skip
+# uncompiled deSolve, as it is so slow. More benchmarks are found in the
+# benchmark vignette
 # \dontrun{
-FOMC_SFO <- mkinmod(
-  parent = mkinsub("FOMC", "m1"),
-  m1 = mkinsub("SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      # Fit the model to the FOCUS example dataset D using defaults
-fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      # Use starting parameters from parent only FOMC fit
-fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE)
-fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE,
-  parms.ini = fit.FOMC$bparms.ode)
      #> Warning: Observations with value of zero were removed from the data
      
-# Use stepwise fitting, using optimised parameters from parent only fit, SFORB
-SFORB_SFO <- mkinmod(
-  parent = list(type = "SFORB", to = "m1", sink = TRUE),
-  m1 = list(type = "SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      # Fit the model to the FOCUS example dataset D using defaults
-fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve",
-                                 quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      # Use starting parameters from parent only SFORB fit (not really needed in this case)
-fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE)
-fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      #> Warning: Initial parameter(s) k_parent_free_sink not used in the model
      # }
-
+if(require(rbenchmark)) {
+  benchmark(replications = 1, order = "relative", columns = c("test", "relative", "elapsed"),
+    deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",
+      solution_type = "deSolve", use_compiled = TRUE),
+    eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",
+      solution_type = "eigen"),
+    analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc",
+      solution_type = "analytical"))
+}
+
      #>               test relative elapsed
+#> 3       analytical    1.000   0.752
+#> 1 deSolve_compiled    2.294   1.725
+#> 2            eigen    2.727   2.051
      # }
+
+# Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO
 # \dontrun{
-# Weighted fits, including IRLS (error_model = "obs")
-SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"),
-                      m1 = mkinsub("SFO"), use_of_ff = "max")
      #> Successfully compiled differential equation model from auto-generated C code.
      f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      summary(f.noweight)
      #> mkin version used for fitting:    0.9.50.2 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 07:03:50 2020 
-#> Date of summary: Wed May 27 07:03:50 2020 
-#> 
-#> Equations:
-#> d_parent/dt = - k_parent * parent
-#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#> 
-#> Model predictions using solution type analytical 
-#> 
-#> Fitted using 421 model solutions performed in 0.129 s
-#> 
-#> Error model: Constant variance 
-#> 
-#> Error model algorithm: OLS 
-#> 
-#> Starting values for parameters to be optimised:
-#>                   value   type
-#> parent_0       100.7500  state
-#> k_parent         0.1000 deparm
-#> k_m1             0.1001 deparm
-#> f_parent_to_m1   0.5000 deparm
-#> 
-#> Starting values for the transformed parameters actually optimised:
-#>                     value lower upper
-#> parent_0       100.750000  -Inf   Inf
-#> log_k_parent    -2.302585  -Inf   Inf
-#> log_k_m1        -2.301586  -Inf   Inf
-#> f_parent_ilr_1   0.000000  -Inf   Inf
-#> 
-#> Fixed parameter values:
-#>      value  type
-#> m1_0     0 state
-#> 
-#> Results:
-#> 
-#>        AIC      BIC    logLik
-#>   204.4486 212.6365 -97.22429
-#> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error    Lower    Upper
-#> parent_0       99.60000    1.57000 96.40000 102.8000
-#> log_k_parent   -2.31600    0.04087 -2.39900  -2.2330
-#> log_k_m1       -5.24800    0.13320 -5.51800  -4.9770
-#> f_parent_ilr_1  0.04096    0.06312 -0.08746   0.1694
-#> sigma           3.12600    0.35850  2.39600   3.8550
-#> 
-#> Parameter correlation:
-#>                  parent_0 log_k_parent   log_k_m1 f_parent_ilr_1      sigma
-#> parent_0        1.000e+00    5.174e-01 -1.688e-01     -5.471e-01 -3.190e-07
-#> log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01  3.168e-07
-#> log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -1.406e-07
-#> f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00 -1.587e-10
-#> sigma          -3.190e-07    3.168e-07 -1.406e-07     -1.587e-10  1.000e+00
-#> 
-#> Backtransformed parameters:
-#> Confidence intervals for internally transformed parameters are asymmetric.
-#> t-test (unrealistically) based on the assumption of normal distribution
-#> for estimators of untransformed parameters.
-#>                 Estimate t value    Pr(>t)     Lower     Upper
-#> parent_0       99.600000  63.430 2.298e-36 96.400000 1.028e+02
-#> k_parent        0.098700  24.470 4.955e-23  0.090820 1.073e-01
-#> k_m1            0.005261   7.510 6.165e-09  0.004012 6.898e-03
-#> f_parent_to_m1  0.514500  23.070 3.104e-22  0.469100 5.596e-01
-#> sigma           3.126000   8.718 2.235e-10  2.396000 3.855e+00
-#> 
-#> FOCUS Chi2 error levels in percent:
-#>          err.min n.optim df
-#> All data   6.398       4 15
-#> parent     6.459       2  7
-#> m1         4.690       2  8
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.5145
-#> parent_sink 0.4855
-#> 
-#> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   7.023  23.33
-#> m1     131.761 437.70
-#> 
-#> Data:
-#>  time variable observed predicted   residual
-#>     0   parent    99.46  99.59848 -1.385e-01
-#>     0   parent   102.04  99.59848  2.442e+00
-#>     1   parent    93.50  90.23787  3.262e+00
-#>     1   parent    92.50  90.23787  2.262e+00
-#>     3   parent    63.23  74.07319 -1.084e+01
-#>     3   parent    68.99  74.07319 -5.083e+00
-#>     7   parent    52.32  49.91206  2.408e+00
-#>     7   parent    55.13  49.91206  5.218e+00
-#>    14   parent    27.27  25.01257  2.257e+00
-#>    14   parent    26.64  25.01257  1.627e+00
-#>    21   parent    11.50  12.53462 -1.035e+00
-#>    21   parent    11.64  12.53462 -8.946e-01
-#>    35   parent     2.85   3.14787 -2.979e-01
-#>    35   parent     2.91   3.14787 -2.379e-01
-#>    50   parent     0.69   0.71624 -2.624e-02
-#>    50   parent     0.63   0.71624 -8.624e-02
-#>    75   parent     0.05   0.06074 -1.074e-02
-#>    75   parent     0.06   0.06074 -7.381e-04
-#>     1       m1     4.84   4.80296  3.704e-02
-#>     1       m1     5.64   4.80296  8.370e-01
-#>     3       m1    12.91  13.02400 -1.140e-01
-#>     3       m1    12.96  13.02400 -6.400e-02
-#>     7       m1    22.97  25.04476 -2.075e+00
-#>     7       m1    24.47  25.04476 -5.748e-01
-#>    14       m1    41.69  36.69002  5.000e+00
-#>    14       m1    33.21  36.69002 -3.480e+00
-#>    21       m1    44.37  41.65310  2.717e+00
-#>    21       m1    46.44  41.65310  4.787e+00
-#>    35       m1    41.22  43.31312 -2.093e+00
-#>    35       m1    37.95  43.31312 -5.363e+00
-#>    50       m1    41.19  41.21831 -2.831e-02
-#>    50       m1    40.01  41.21831 -1.208e+00
-#>    75       m1    40.09  36.44703  3.643e+00
-#>    75       m1    33.85  36.44703 -2.597e+00
-#>   100       m1    31.04  31.98163 -9.416e-01
-#>   100       m1    33.13  31.98163  1.148e+00
-#>   120       m1    25.15  28.78984 -3.640e+00
-#>   120       m1    33.31  28.78984  4.520e+00
      f.obs <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "obs", quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      summary(f.obs)
      #> mkin version used for fitting:    0.9.50.2 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 07:03:50 2020 
-#> Date of summary: Wed May 27 07:03:50 2020 
-#> 
-#> Equations:
-#> d_parent/dt = - k_parent * parent
-#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
-#> 
-#> Model predictions using solution type analytical 
-#> 
-#> Fitted using 978 model solutions performed in 0.407 s
-#> 
-#> Error model: Variance unique to each observed variable 
-#> 
-#> Error model algorithm: d_3 
-#> Direct fitting and three-step fitting yield approximately the same likelihood 
-#> 
-#> Starting values for parameters to be optimised:
-#>                   value   type
-#> parent_0       100.7500  state
-#> k_parent         0.1000 deparm
-#> k_m1             0.1001 deparm
-#> f_parent_to_m1   0.5000 deparm
-#> sigma_parent     3.0000  error
-#> sigma_m1         3.0000  error
-#> 
-#> Starting values for the transformed parameters actually optimised:
-#>                     value lower upper
-#> parent_0       100.750000  -Inf   Inf
-#> log_k_parent    -2.302585  -Inf   Inf
-#> log_k_m1        -2.301586  -Inf   Inf
-#> f_parent_ilr_1   0.000000  -Inf   Inf
-#> sigma_parent     3.000000     0   Inf
-#> sigma_m1         3.000000     0   Inf
-#> 
-#> Fixed parameter values:
-#>      value  type
-#> m1_0     0 state
-#> 
-#> Results:
-#> 
-#>        AIC      BIC    logLik
-#>   205.8727 215.6982 -96.93634
-#> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error    Lower    Upper
-#> parent_0       99.65000    1.70200 96.19000 103.1000
-#> log_k_parent   -2.31300    0.04376 -2.40200  -2.2240
-#> log_k_m1       -5.25000    0.12430 -5.50400  -4.9970
-#> f_parent_ilr_1  0.03861    0.06171 -0.08708   0.1643
-#> sigma_parent    3.40100    0.56820  2.24400   4.5590
-#> sigma_m1        2.85500    0.45240  1.93400   3.7770
-#> 
-#> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_parent
-#> parent_0        1.00000      0.51078 -0.19133       -0.59997     0.035670
-#> log_k_parent    0.51078      1.00000 -0.37458       -0.59239     0.069833
-#> log_k_m1       -0.19133     -0.37458  1.00000        0.74398    -0.026158
-#> f_parent_ilr_1 -0.59997     -0.59239  0.74398        1.00000    -0.041369
-#> sigma_parent    0.03567      0.06983 -0.02616       -0.04137     1.000000
-#> sigma_m1       -0.03385     -0.06627  0.02482        0.03926    -0.004628
-#>                 sigma_m1
-#> parent_0       -0.033847
-#> log_k_parent   -0.066265
-#> log_k_m1        0.024823
-#> f_parent_ilr_1  0.039256
-#> sigma_parent   -0.004628
-#> sigma_m1        1.000000
-#> 
-#> Backtransformed parameters:
-#> Confidence intervals for internally transformed parameters are asymmetric.
-#> t-test (unrealistically) based on the assumption of normal distribution
-#> for estimators of untransformed parameters.
-#>                 Estimate t value    Pr(>t)     Lower     Upper
-#> parent_0       99.650000  58.560 2.004e-34 96.190000 1.031e+02
-#> k_parent        0.098970  22.850 1.099e-21  0.090530 1.082e-01
-#> k_m1            0.005245   8.046 1.732e-09  0.004072 6.756e-03
-#> f_parent_to_m1  0.513600  23.560 4.352e-22  0.469300 5.578e-01
-#> sigma_parent    3.401000   5.985 5.662e-07  2.244000 4.559e+00
-#> sigma_m1        2.855000   6.311 2.215e-07  1.934000 3.777e+00
-#> 
-#> FOCUS Chi2 error levels in percent:
-#>          err.min n.optim df
-#> All data   6.398       4 15
-#> parent     6.464       2  7
-#> m1         4.682       2  8
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.5136
-#> parent_sink 0.4864
-#> 
-#> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   7.003  23.26
-#> m1     132.154 439.01
-#> 
-#> Data:
-#>  time variable observed predicted   residual
-#>     0   parent    99.46  99.65417 -1.942e-01
-#>     0   parent   102.04  99.65417  2.386e+00
-#>     1   parent    93.50  90.26332  3.237e+00
-#>     1   parent    92.50  90.26332  2.237e+00
-#>     3   parent    63.23  74.05306 -1.082e+01
-#>     3   parent    68.99  74.05306 -5.063e+00
-#>     7   parent    52.32  49.84325  2.477e+00
-#>     7   parent    55.13  49.84325  5.287e+00
-#>    14   parent    27.27  24.92971  2.340e+00
-#>    14   parent    26.64  24.92971  1.710e+00
-#>    21   parent    11.50  12.46890 -9.689e-01
-#>    21   parent    11.64  12.46890 -8.289e-01
-#>    35   parent     2.85   3.11925 -2.692e-01
-#>    35   parent     2.91   3.11925 -2.092e-01
-#>    50   parent     0.69   0.70679 -1.679e-02
-#>    50   parent     0.63   0.70679 -7.679e-02
-#>    75   parent     0.05   0.05952 -9.523e-03
-#>    75   parent     0.06   0.05952  4.772e-04
-#>     1       m1     4.84   4.81075  2.925e-02
-#>     1       m1     5.64   4.81075  8.292e-01
-#>     3       m1    12.91  13.04196 -1.320e-01
-#>     3       m1    12.96  13.04196 -8.196e-02
-#>     7       m1    22.97  25.06847 -2.098e+00
-#>     7       m1    24.47  25.06847 -5.985e-01
-#>    14       m1    41.69  36.70308  4.987e+00
-#>    14       m1    33.21  36.70308 -3.493e+00
-#>    21       m1    44.37  41.65115  2.719e+00
-#>    21       m1    46.44  41.65115  4.789e+00
-#>    35       m1    41.22  43.29465 -2.075e+00
-#>    35       m1    37.95  43.29465 -5.345e+00
-#>    50       m1    41.19  41.19948 -9.479e-03
-#>    50       m1    40.01  41.19948 -1.189e+00
-#>    75       m1    40.09  36.44035  3.650e+00
-#>    75       m1    33.85  36.44035 -2.590e+00
-#>   100       m1    31.04  31.98773 -9.477e-01
-#>   100       m1    33.13  31.98773  1.142e+00
-#>   120       m1    25.15  28.80429 -3.654e+00
-#>   120       m1    33.31  28.80429  4.506e+00
      f.tc <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      summary(f.tc)
      #> mkin version used for fitting:    0.9.50.2 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 07:03:51 2020 
-#> Date of summary: Wed May 27 07:03:51 2020 
+FOMC_SFO <- mkinmod(
+  parent = mkinsub("FOMC", "m1"),
+  m1 = mkinsub("SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0499
      # Again, we get a warning and try a more sophisticated error model
+fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = "tc")
+# This model has a higher likelihood, but not significantly so
+lrtest(fit.tc, fit.FOMC_SFO.tc)
+
      #> Likelihood ratio test
+#> 
+#> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0
+#> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0
+#>   #Df  LogLik Df  Chisq Pr(>Chisq)
+#> 1   7 -64.829                     
+#> 2   6 -64.983 -1 0.3075     0.5792
      # Also, the missing standard error for log_beta and the t-tests for alpha
+# and beta indicate overparameterisation
+summary(fit.FOMC_SFO.tc, data = FALSE)
+
      #> Warning: NaNs produced
      #> Warning: NaNs produced
      #> Warning: diag(.) had 0 or NA entries; non-finite result is doubtful
      #> mkin version used for fitting:    0.9.50.3 
+#> R version used for fitting:       4.0.3 
+#> Date of fit:     Thu Oct 15 12:40:24 2020 
+#> Date of summary: Thu Oct 15 12:40:24 2020 
 #> 
 #> Equations:
-#> d_parent/dt = - k_parent * parent
-#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1
+#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
+#> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) *
+#>            parent - k_m1 * m1
 #> 
-#> Model predictions using solution type analytical 
+#> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 2088 model solutions performed in 0.722 s
+#> Fitted using 3611 model solutions performed in 2.669 s
 #> 
 #> Error model: Two-component variance function 
 #> 
 #> Error model algorithm: d_3 
-#> Direct fitting and three-step fitting yield approximately the same likelihood 
+#> Three-step fitting yielded a higher likelihood than direct fitting 
 #> 
 #> Starting values for parameters to be optimised:
-#>                   value   type
-#> parent_0       100.7500  state
-#> k_parent         0.1000 deparm
-#> k_m1             0.1001 deparm
-#> f_parent_to_m1   0.5000 deparm
-#> sigma_low        0.1000  error
-#> rsd_high         0.1000  error
+#>                 value   type
+#> parent_0       100.75  state
+#> alpha            1.00 deparm
+#> beta            10.00 deparm
+#> k_m1             0.10 deparm
+#> f_parent_to_m1   0.50 deparm
+#> sigma_low        0.10  error
+#> rsd_high         0.10  error
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>                     value lower upper
 #> parent_0       100.750000  -Inf   Inf
-#> log_k_parent    -2.302585  -Inf   Inf
-#> log_k_m1        -2.301586  -Inf   Inf
+#> log_k_m1        -2.302585  -Inf   Inf
 #> f_parent_ilr_1   0.000000  -Inf   Inf
+#> log_alpha        0.000000  -Inf   Inf
+#> log_beta         2.302585  -Inf   Inf
 #> sigma_low        0.100000     0   Inf
 #> rsd_high         0.100000     0   Inf
 #> 
@@ -930,97 +639,70 @@ Degradation Data. Environments 6(12) 124
 #> 
 #> Results:
 #> 
-#>        AIC      BIC    logLik
-#>   141.9656 151.7911 -64.98278
+#>       AIC      BIC    logLik
+#>   143.658 155.1211 -64.82902
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
-#>                 Estimate Std. Error     Lower     Upper
-#> parent_0       100.70000   2.621000 95.400000 106.10000
-#> log_k_parent    -2.29700   0.008862 -2.315000  -2.27900
-#> log_k_m1        -5.26600   0.091310 -5.452000  -5.08000
-#> f_parent_ilr_1   0.02374   0.055300 -0.088900   0.13640
-#> sigma_low        0.00305   0.004829 -0.006786   0.01289
-#> rsd_high         0.07928   0.009418  0.060100   0.09847
+#>                  Estimate Std. Error     Lower      Upper
+#> parent_0       101.600000  2.6390000 96.240000 107.000000
+#> log_k_m1        -5.284000  0.0928900 -5.473000  -5.095000
+#> f_parent_ilr_1   0.001008  0.0541900 -0.109500   0.111500
+#> log_alpha        5.522000  0.0077300  5.506000   5.538000
+#> log_beta         7.806000        NaN       NaN        NaN
+#> sigma_low        0.002488  0.0002431  0.001992   0.002984
+#> rsd_high         0.079210  0.0093280  0.060180   0.098230
 #> 
 #> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_low rsd_high
-#> parent_0        1.00000      0.67644 -0.10215       -0.76822   0.14294 -0.08783
-#> log_k_parent    0.67644      1.00000 -0.15102       -0.59491   0.34611 -0.08125
-#> log_k_m1       -0.10215     -0.15102  1.00000        0.51808  -0.05236  0.01240
-#> f_parent_ilr_1 -0.76822     -0.59491  0.51808        1.00000  -0.13900  0.03248
-#> sigma_low       0.14294      0.34611 -0.05236       -0.13900   1.00000 -0.16546
-#> rsd_high       -0.08783     -0.08125  0.01240        0.03248  -0.16546  1.00000
+#>                 parent_0  log_k_m1 f_parent_ilr_1 log_alpha log_beta sigma_low
+#> parent_0        1.000000 -0.094697       -0.76654   0.70525      NaN  0.016099
+#> log_k_m1       -0.094697  1.000000        0.51404  -0.14347      NaN  0.001576
+#> f_parent_ilr_1 -0.766543  0.514038        1.00000  -0.61368      NaN  0.015465
+#> log_alpha       0.705247 -0.143468       -0.61368   1.00000      NaN  5.871780
+#> log_beta             NaN       NaN            NaN       NaN        1       NaN
+#> sigma_low       0.016099  0.001576        0.01546   5.87178      NaN  1.000000
+#> rsd_high        0.006566 -0.011662       -0.05353   0.04845      NaN -0.652554
+#>                 rsd_high
+#> parent_0        0.006566
+#> log_k_m1       -0.011662
+#> f_parent_ilr_1 -0.053525
+#> log_alpha       0.048451
+#> log_beta             NaN
+#> sigma_low      -0.652554
+#> rsd_high        1.000000
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
 #> t-test (unrealistically) based on the assumption of normal distribution
 #> for estimators of untransformed parameters.
-#>                 Estimate  t value    Pr(>t)     Lower     Upper
-#> parent_0       1.007e+02  38.4300 1.180e-28 95.400000 1.061e+02
-#> k_parent       1.006e-01 112.8000 1.718e-43  0.098760 1.024e-01
-#> k_m1           5.167e-03  10.9500 1.171e-12  0.004290 6.223e-03
-#> f_parent_to_m1 5.084e-01  26.0100 2.146e-23  0.468600 5.481e-01
-#> sigma_low      3.050e-03   0.6314 2.661e-01 -0.006786 1.289e-02
-#> rsd_high       7.928e-02   8.4170 6.418e-10  0.060100 9.847e-02
+#>                 Estimate t value    Pr(>t)     Lower     Upper
+#> parent_0       1.016e+02 32.7800 6.312e-26 9.624e+01 1.070e+02
+#> k_m1           5.072e-03 10.1200 1.216e-11 4.197e-03 6.130e-03
+#> f_parent_to_m1 5.004e-01 20.8300 4.318e-20 4.614e-01 5.394e-01
+#> alpha          2.502e+02  0.5624 2.889e-01 2.463e+02 2.542e+02
+#> beta           2.455e+03  0.5549 2.915e-01        NA        NA
+#> sigma_low      2.488e-03  0.4843 3.158e-01 1.992e-03 2.984e-03
+#> rsd_high       7.921e-02  8.4300 8.001e-10 6.018e-02 9.823e-02
 #> 
 #> FOCUS Chi2 error levels in percent:
 #>          err.min n.optim df
-#> All data   6.475       4 15
-#> parent     6.573       2  7
-#> m1         4.671       2  8
+#> All data   6.781       5 14
+#> parent     7.141       3  6
+#> m1         4.640       2  8
 #> 
 #> Resulting formation fractions:
 #>                 ff
-#> parent_m1   0.5084
-#> parent_sink 0.4916
+#> parent_m1   0.5004
+#> parent_sink 0.4996
 #> 
 #> Estimated disappearance times:
-#>           DT50  DT90
-#> parent   6.893  22.9
-#> m1     134.156 445.7
-#> 
-#> Data:
-#>  time variable observed predicted   residual
-#>     0   parent    99.46 100.73434  -1.274340
-#>     0   parent   102.04 100.73434   1.305660
-#>     1   parent    93.50  91.09751   2.402486
-#>     1   parent    92.50  91.09751   1.402486
-#>     3   parent    63.23  74.50141 -11.271410
-#>     3   parent    68.99  74.50141  -5.511410
-#>     7   parent    52.32  49.82880   2.491200
-#>     7   parent    55.13  49.82880   5.301200
-#>    14   parent    27.27  24.64809   2.621908
-#>    14   parent    26.64  24.64809   1.991908
-#>    21   parent    11.50  12.19232  -0.692315
-#>    21   parent    11.64  12.19232  -0.552315
-#>    35   parent     2.85   2.98327  -0.133266
-#>    35   parent     2.91   2.98327  -0.073266
-#>    50   parent     0.69   0.66013   0.029874
-#>    50   parent     0.63   0.66013  -0.030126
-#>    75   parent     0.05   0.05344  -0.003438
-#>    75   parent     0.06   0.05344   0.006562
-#>     1       m1     4.84   4.88645  -0.046451
-#>     1       m1     5.64   4.88645   0.753549
-#>     3       m1    12.91  13.22867  -0.318669
-#>     3       m1    12.96  13.22867  -0.268669
-#>     7       m1    22.97  25.36417  -2.394166
-#>     7       m1    24.47  25.36417  -0.894166
-#>    14       m1    41.69  37.00974   4.680263
-#>    14       m1    33.21  37.00974  -3.799737
-#>    21       m1    44.37  41.90133   2.468669
-#>    21       m1    46.44  41.90133   4.538669
-#>    35       m1    41.22  43.45691  -2.236913
-#>    35       m1    37.95  43.45691  -5.506913
-#>    50       m1    41.19  41.34199  -0.151985
-#>    50       m1    40.01  41.34199  -1.331985
-#>    75       m1    40.09  36.61471   3.475295
-#>    75       m1    33.85  36.61471  -2.764705
-#>   100       m1    31.04  32.20082  -1.160823
-#>   100       m1    33.13  32.20082   0.929177
-#>   120       m1    25.15  29.04130  -3.891304
-#>   120       m1    33.31  29.04130   4.268696
      # }
-
-
+#>           DT50  DT90 DT50back
+#> parent   6.812  22.7    6.834
+#> m1     136.661 454.0       NA
      
+# We can easily use starting parameters from the parent only fit (only for illustration)
+fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE, error_model = "tc")
+fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE,
+  parms.ini = fit.FOMC$bparms.ode, error_model = "tc")
+# }
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html index 40cc2ef4..7d6bc77b 100644 --- a/docs/reference/mkinmod.html +++ b/docs/reference/mkinmod.html @@ -40,10 +40,9 @@ - + @@ -75,7 +74,7 @@ list of lists can be given in the speclist argument." /> mkin - 0.9.50.2 + 0.9.50.3
      @@ -112,6 +111,9 @@ list of lists can be given in the speclist argument." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
  • @@ -120,7 +122,7 @@ list of lists can be given in the speclist argument." />
    • - + @@ -139,43 +141,42 @@ list of lists can be given in the speclist argument." />

      Function to set up a kinetic model with one or more state variables

      - Source: R/mkinmod.R + Source: R/mkinmod.R
      -

      The function usually takes several expressions, each assigning a compound -name to a list, specifying the kinetic model type and reaction or transfer -to other observed compartments. Instead of specifying several expressions, a -list of lists can be given in the speclist argument.

      +

      This function is usually called using a call to mkinsub() for each observed +variable, specifying the corresponding submodel as well as outgoing pathways +(see examples).

      - 
      mkinmod(
-  ...,
-  use_of_ff = "max",
-  speclist = NULL,
-  quiet = FALSE,
-  verbose = FALSE
-)
      + 
      mkinmod(
+  ...,
+  use_of_ff = "max",
+  speclist = NULL,
+  quiet = FALSE,
+  verbose = FALSE
+)

      Arguments

      - +additional mkinsub() argument can be sink = FALSE, effectively +fixing the flux to sink to zero.

      @@ -197,14 +198,14 @@ argument. Default is NULL.

      -
      ...

      For each observed variable, a list has to be specified as an -argument, containing at least a component type, specifying the type -of kinetics to use for the variable. Currently, single first order -kinetics "SFO", indeterminate order rate equation kinetics "IORE", or -single first order with reversible binding "SFORB" are implemented for all -variables, while "FOMC", "DFOP" and "HS" can additionally be chosen for -the first variable which is assumed to be the source compartment. -Additionally, each component of the list can include a character vector -to, specifying names of variables to which a transfer is to be -assumed in the model. If the argument use_of_ff is set to "min" +

      For each observed variable, a list as obtained by mkinsub() +has to be specified as an argument (see examples). Currently, single +first order kinetics "SFO", indeterminate order rate equation kinetics +"IORE", or single first order with reversible binding "SFORB" are +implemented for all variables, while "FOMC", "DFOP", "HS" and "logistic" +can additionally be chosen for the first variable which is assumed to be +the source compartment. +Additionally, mkinsub() has an argument to, specifying names of +variables to which a transfer is to be assumed in the model. +If the argument use_of_ff is set to "min" (default) and the model for the compartment is "SFO" or "SFORB", an -additional component of the list can be "sink=FALSE" effectively fixing -the flux to sink to zero.
      use_of_ff
      verbose

      If TRUE, passed to cfunction if +

      If TRUE, passed to inline::cfunction() if applicable to give detailed information about the C function being built.

      Value

      -

      A list of class mkinmod for use with mkinfit, +

      A list of class mkinmod for use with mkinfit(), containing, among others,

      diffs

      A vector of string representations of differential equations, one for each modelling variable.

      @@ -222,11 +223,18 @@ returned by cfunction.

      For the definition of model types and their parameters, the equations given in the FOCUS and NAFTA guidance documents are used.

      +

      For kinetic models with more than one observed variable, a symbolic solution +of the system of differential equations is included in the resulting +mkinmod object in some cases, speeding up the solution.

      +

      If a C compiler is found by pkgbuild::has_compiler() and there +is more than one observed variable in the specification, C code is generated +for evaluating the differential equations, compiled using +inline::cfunction() and added to the resulting mkinmod object.

      Note

      The IORE submodel is not well tested for metabolites. When using this -model for metabolites, you may want to read the second note in the help -page to mkinfit.

      +model for metabolites, you may want to read the note in the help +page to mkinfit.

      References

      FOCUS (2006) “Guidance Document on Estimating Persistence @@ -236,27 +244,31 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

      NAFTA Technical Working Group on Pesticides (not dated) Guidance for Evaluating and Calculating Degradation Kinetics in Environmental Media

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # Specify the SFO model (this is not needed any more, as we can now mkinfit("SFO", ...)
-SFO <- mkinmod(parent = mkinsub("SFO"))
+SFO <- mkinmod(parent = mkinsub("SFO"))
 
 # One parent compound, one metabolite, both single first order
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
 # \dontrun{
 # The above model used to be specified like this, before the advent of mkinsub()
-SFO_SFO <- mkinmod(
-  parent = list(type = "SFO", to = "m1"),
-  m1 = list(type = "SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      
+SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1"),
+  m1 = list(type = "SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
 # Show details of creating the C function
-SFO_SFO <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"), verbose = TRUE)
      #> Compilation argument:
-#>  /usr/lib/R/bin/R CMD SHLIB file5d7f45129ff2.c 2> file5d7f45129ff2.c.err.txt 
-#> Program source:
+SFO_SFO <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"), verbose = TRUE)
+
      #> Program source:
 #>   1: #include <R.h>
 #>   2: 
 #>   3: 
@@ -276,17 +288,39 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media
      
 #>  17: f[0] = - k_parent * y[0];
 #>  18: f[1] = + f_parent_to_m1 * k_parent * y[0] - k_m1 * y[1];
 #>  19: }
      #> Successfully compiled differential equation model from auto-generated C code.
      
+# The symbolic solution which is available in this case is not
+# made for human reading but for speed of computation
+SFO_SFO$deg_func
+
      #> function (observed, odeini, odeparms) 
+#> {
+#>     predicted <- numeric(0)
+#>     with(as.list(odeparms), {
+#>         t <- observed[observed$name == "parent", "time"]
+#>         predicted <<- c(predicted, SFO.solution(t, odeini["parent"], 
+#>             k_parent))
+#>         t <- observed[observed$name == "m1", "time"]
+#>         predicted <<- c(predicted, (((k_m1 - k_parent) * odeini["m1"] - 
+#>             f_parent_to_m1 * k_parent * odeini["parent"]) * exp(-k_m1 * 
+#>             t) + f_parent_to_m1 * k_parent * odeini["parent"] * 
+#>             exp(-k_parent * t))/(k_m1 - k_parent))
+#>     })
+#>     return(predicted)
+#> }
+#> <environment: 0x55555b726d88>
      
 # If we have several parallel metabolites
 # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R)
-m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
-                           M1 = mkinsub("SFO"),
-                           M2 = mkinsub("SFO"),
-                           use_of_ff = "max", quiet = TRUE)
-
-fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par,
-                          synthetic_data_for_UBA_2014[[12]]$data,
-                          quiet = TRUE)
-# }
      +m_synth_DFOP_par <- mkinmod( + parent = mkinsub("DFOP", c("M1", "M2")), + M1 = mkinsub("SFO"), + M2 = mkinsub("SFO"), + use_of_ff = "max", quiet = TRUE) + +fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par, + synthetic_data_for_UBA_2014[[12]]$data, + quiet = TRUE) +
      #> Warning: Shapiro-Wilk test for standardized residuals: p = 0.000174
      # } + +
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png index 31800c09..a78bf313 100644 Binary files a/docs/reference/mkinparplot-1.png and b/docs/reference/mkinparplot-1.png differ diff --git a/docs/reference/mkinparplot.html b/docs/reference/mkinparplot.html index 4ff4970e..3aa98dbf 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -73,7 +73,7 @@ mkinfit." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -110,6 +110,9 @@ mkinfit." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
  • @@ -118,7 +121,7 @@ mkinfit." />
    • - + @@ -137,7 +140,7 @@ mkinfit." />

      Function to plot the confidence intervals obtained using mkinfit

      - Source: R/mkinparplot.R + Source: R/mkinparplot.R
      @@ -146,7 +149,7 @@ mkinfit." /> mkinfit.

      - 
      mkinparplot(object)
      + 
      mkinparplot(object)

      Arguments

      @@ -161,14 +164,20 @@ mkinfit." />

      Nothing is returned by this function, as it is called for its side effect, namely to produce a plot.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # \dontrun{
-model <- mkinmod(
-  T245 = mkinsub("SFO", to = c("phenol"), sink = FALSE),
-  phenol = mkinsub("SFO", to = c("anisole")),
-  anisole = mkinsub("SFO"), use_of_ff = "max")
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      mkinparplot(fit)
      # }
+model <- mkinmod(
+  T245 = mkinsub("SFO", to = c("phenol"), sink = FALSE),
+  phenol = mkinsub("SFO", to = c("anisole")),
+  anisole = mkinsub("SFO"), use_of_ff = "max")
+
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
+
      #> Warning: Observations with value of zero were removed from the data
      mkinparplot(fit)
+
      # }
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index e16de283..17f38938 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -111,6 +111,9 @@ kinetic parameters and initial values for the state variables." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -119,7 +122,7 @@ kinetic parameters and initial values for the state variables." />
      • - + @@ -138,7 +141,7 @@ kinetic parameters and initial values for the state variables." />

        Produce predictions from a kinetic model using specific parameters

        - Source: R/mkinpredict.R + Source: R/mkinpredict.R
        @@ -148,49 +151,49 @@ kinetic model as specified by mkinmod, u kinetic parameters and initial values for the state variables.

        - 
        mkinpredict(
-  x,
-  odeparms,
-  odeini,
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  map_output = TRUE,
-  ...
-)
+    
        mkinpredict(
+  x,
+  odeparms,
+  odeini,
+  outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve",
+  use_compiled = "auto",
+  method.ode = "lsoda",
+  atol = 1e-08,
+  rtol = 1e-10,
+  map_output = TRUE,
+  ...
+)
 
 # S3 method for mkinmod
-mkinpredict(
-  x,
-  odeparms = c(k_parent_sink = 0.1),
-  odeini = c(parent = 100),
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  map_output = TRUE,
-  ...
-)
+mkinpredict(
+  x,
+  odeparms = c(k_parent_sink = 0.1),
+  odeini = c(parent = 100),
+  outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve",
+  use_compiled = "auto",
+  method.ode = "lsoda",
+  atol = 1e-08,
+  rtol = 1e-10,
+  map_output = TRUE,
+  ...
+)
 
 # S3 method for mkinfit
-mkinpredict(
-  x,
-  odeparms = x$bparms.ode,
-  odeini = x$bparms.state,
-  outtimes = seq(0, 120, by = 0.1),
-  solution_type = "deSolve",
-  use_compiled = "auto",
-  method.ode = "lsoda",
-  atol = 1e-08,
-  rtol = 1e-10,
-  map_output = TRUE,
-  ...
-)
        
+mkinpredict(
+  x,
+  odeparms = x$bparms.ode,
+  odeini = x$bparms.state,
+  outtimes = seq(0, 120, by = 0.1),
+  solution_type = "deSolve",
+  use_compiled = "auto",
+  method.ode = "lsoda",
+  atol = 1e-08,
+  rtol = 1e-10,
+  map_output = TRUE,
+  ...
+)

        Arguments

      • @@ -264,13 +267,17 @@ solver is used.

      Value

      A matrix with the numeric solution in wide format

      +

      Author

      + +

      Johannes Ranke

      Examples

      
-SFO <- mkinmod(degradinol = mkinsub("SFO"))
+SFO <- mkinmod(degradinol = mkinsub("SFO"))
 # Compare solution types
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")
      #>    time  degradinol
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "analytical")
+
      #>    time  degradinol
 #> 0     0 100.0000000
 #> 1     1  74.0818221
 #> 2     2  54.8811636
@@ -291,8 +298,9 @@ solver is used.
      
 #> 17   17   0.6096747
 #> 18   18   0.4516581
 #> 19   19   0.3345965
-#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve")
      #>    time  degradinol
+#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "deSolve")
+
      #>    time  degradinol
 #> 0     0 100.0000000
 #> 1     1  74.0818221
 #> 2     2  54.8811636
@@ -313,8 +321,9 @@ solver is used.
      
 #> 17   17   0.6096747
 #> 18   18   0.4516581
 #> 19   19   0.3345965
-#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "deSolve", use_compiled = FALSE)
      #>    time  degradinol
+#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "deSolve", use_compiled = FALSE)
+
      #>    time  degradinol
 #> 0     0 100.0000000
 #> 1     1  74.0818221
 #> 2     2  54.8811636
@@ -335,8 +344,9 @@ solver is used.
      
 #> 17   17   0.6096747
 #> 18   18   0.4516581
 #> 19   19   0.3345965
-#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "eigen")
      #>    time  degradinol
+#> 20   20   0.2478752
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "eigen")
+
      #>    time  degradinol
 #> 0     0 100.0000000
 #> 1     1  74.0818221
 #> 2     2  54.8811636
@@ -359,59 +369,68 @@ solver is used.
      
 #> 19   19   0.3345965
 #> 20   20   0.2478752
      
 # Compare integration methods to analytical solution
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      solution_type = "analytical")[21,]
      #>       time degradinol 
-#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "lsoda")[21,]
      #>       time degradinol 
-#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "ode45")[21,]
      #>       time degradinol 
-#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
-      method = "rk4")[21,]
      #>       time degradinol 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      solution_type = "analytical")[21,]
+
      #>       time degradinol 
+#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "lsoda")[21,]
+
      #>       time degradinol 
+#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "ode45")[21,]
+
      #>       time degradinol 
+#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100), 0:20,
+      method = "rk4")[21,]
+
      #>       time degradinol 
 #> 20.0000000  0.2480043 
      # rk4 is not as precise here
 
 # The number of output times used to make a lot of difference until the
 # default for atol was adjusted
-mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.1))[201,]
      #>       time degradinol 
-#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
-      seq(0, 20, by = 0.01))[2001,]
      #>       time degradinol 
+mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
+      seq(0, 20, by = 0.1))[201,]
+
      #>       time degradinol 
+#> 20.0000000  0.2478752 
      mkinpredict(SFO, c(k_degradinol = 0.3), c(degradinol = 100),
+      seq(0, 20, by = 0.01))[2001,]
+
      #>       time degradinol 
 #> 20.0000000  0.2478752 
      
 # Comparison of the performance of solution types
-SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
-                  m1 = list(type = "SFO"), use_of_ff = "max")
      #> Successfully compiled differential equation model from auto-generated C code.
      if(require(rbenchmark)) {
-  benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
-    eigen = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "eigen")[201,],
-    deSolve_compiled = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve")[201,],
-    deSolve = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "deSolve", use_compiled = FALSE)[201,],
-    analytical = mkinpredict(SFO_SFO,
-      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
-      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
-      solution_type = "analytical", use_compiled = FALSE)[201,])
-}
      #>               test relative elapsed
-#> 2 deSolve_compiled     1.00   0.004
-#> 4       analytical     1.00   0.004
-#> 1            eigen     4.75   0.019
-#> 3          deSolve    54.75   0.219
      
+SFO_SFO = mkinmod(parent = list(type = "SFO", to = "m1"),
+                  m1 = list(type = "SFO"), use_of_ff = "max")
+
      #> Successfully compiled differential equation model from auto-generated C code.
      if(require(rbenchmark)) {
+  benchmark(replications = 10, order = "relative", columns = c("test", "relative", "elapsed"),
+    eigen = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "eigen")[201,],
+    deSolve_compiled = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "deSolve")[201,],
+    deSolve = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "deSolve", use_compiled = FALSE)[201,],
+    analytical = mkinpredict(SFO_SFO,
+      c(k_parent = 0.15, f_parent_to_m1 = 0.5, k_m1 = 0.01),
+      c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
+      solution_type = "analytical", use_compiled = FALSE)[201,])
+}
+
      #>               test relative elapsed
+#> 4       analytical      1.0   0.005
+#> 2 deSolve_compiled      1.2   0.006
+#> 1            eigen      4.0   0.020
+#> 3          deSolve     45.6   0.228
      
 # \dontrun{
   # Predict from a fitted model
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
-  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
-  head(mkinpredict(f))
      #>     time   parent       m1
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE)
+  f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE, solution_type = "deSolve")
+  head(mkinpredict(f))
+
      #>     time   parent       m1
 #> 0    0.0 82.49216 0.000000
 #> 0.1  0.1 80.00562 1.236198
 #> 0.2  0.2 77.59404 2.422818
 #> 0.3  0.3 75.25514 3.561476
 #> 0.4  0.4 72.98675 4.653740
-#> 0.5  0.5 70.78673 5.701130
      # }
+#> 0.5  0.5 70.78673 5.701130
      # }
      @@ -429,7 +448,7 @@ solver is used.

      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png index bb9657b4..8114b6cd 100644 Binary files a/docs/reference/mkinresplot-1.png and b/docs/reference/mkinresplot-1.png differ diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html index 82afe2e2..d917860a 100644 --- a/docs/reference/mkinresplot.html +++ b/docs/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -112,6 +112,9 @@ argument show_residuals = TRUE." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -120,7 +123,7 @@ argument show_residuals = TRUE." />
      • - + @@ -139,7 +142,7 @@ argument show_residuals = TRUE." />

        Function to plot residuals stored in an mkin object

        - Source: R/mkinresplot.R + Source: R/mkinresplot.R
        @@ -150,21 +153,21 @@ the residuals can be obtained using plot.mkinf argument show_residuals = TRUE.

        - 
        mkinresplot(
-  object,
-  obs_vars = names(object$mkinmod$map),
-  xlim = c(0, 1.1 * max(object$data$time)),
-  standardized = FALSE,
-  xlab = "Time",
-  ylab = ifelse(standardized, "Standardized residual", "Residual"),
-  maxabs = "auto",
-  legend = TRUE,
-  lpos = "topright",
-  col_obs = "auto",
-  pch_obs = "auto",
-  frame = TRUE,
-  ...
-)
        + 
        mkinresplot(
+  object,
+  obs_vars = names(object$mkinmod$map),
+  xlim = c(0, 1.1 * max(object$data$time)),
+  standardized = FALSE,
+  xlab = "Time",
+  ylab = ifelse(standardized, "Standardized residual", "Residual"),
+  maxabs = "auto",
+  legend = TRUE,
+  lpos = "topright",
+  col_obs = "auto",
+  pch_obs = "auto",
+  frame = TRUE,
+  ...
+)

        Arguments

      • @@ -224,7 +227,7 @@ be passed on to legend - +
      ...

      further arguments passed to plot.

      further arguments passed to plot.
      @@ -237,10 +240,16 @@ effect, namely to produce a plot.

      mkinplot, for a way to plot the data and the fitted lines of the mkinfit object, and plot_res for a function combining the plot of the fit and the residual plot.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
-model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      mkinresplot(fit, "m1")
      
+model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
+
      #> Warning: Observations with value of zero were removed from the data
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
      mkinresplot(fit, "m1")
+
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mkinsub.html b/docs/reference/mkinsub.html index ac1220e0..77ee41f8 100644 --- a/docs/reference/mkinsub.html +++ b/docs/reference/mkinsub.html @@ -73,7 +73,7 @@ mkinmod." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -110,6 +110,9 @@ mkinmod." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
  • @@ -118,7 +121,7 @@ mkinmod." />
    • - + @@ -137,7 +140,7 @@ mkinmod." />

      Function to set up a kinetic submodel for one state variable

      - Source: R/mkinsub.R + Source: R/mkinsub.R
      @@ -146,7 +149,7 @@ mkinmod." /> mkinmod.

      - 
      mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)
      + 
      mkinsub(submodel, to = NULL, sink = TRUE, full_name = NA)

      Arguments

      @@ -178,22 +181,28 @@ results on other operating systems or system configurations.

      Value

      A list for use with mkinmod.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # One parent compound, one metabolite, both single first order.
-SFO_SFO <- mkinmod(
-  parent = list(type = "SFO", to = "m1"),
-  m1 = list(type = "SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      
+SFO_SFO <- mkinmod(
+  parent = list(type = "SFO", to = "m1"),
+  m1 = list(type = "SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
 # The same model using mkinsub
-SFO_SFO.2 <- mkinmod(
-  parent = mkinsub("SFO", "m1"),
-  m1 = mkinsub("SFO"))
      #> Successfully compiled differential equation model from auto-generated C code.
      
+SFO_SFO.2 <- mkinmod(
+  parent = mkinsub("SFO", "m1"),
+  m1 = mkinsub("SFO"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
 # \dontrun{
   # Now supplying full names
-  SFO_SFO.2 <- mkinmod(
-    parent = mkinsub("SFO", "m1", full_name = "Test compound"),
-    m1 = mkinsub("SFO", full_name = "Metabolite M1"))
      #> Successfully compiled differential equation model from auto-generated C code.
       # }
+  SFO_SFO.2 <- mkinmod(
+    parent = mkinsub("SFO", "m1", full_name = "Test compound"),
+    m1 = mkinsub("SFO", full_name = "Metabolite M1"))
+
      #> Successfully compiled differential equation model from auto-generated C code.
       # }
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png index 135d5446..c8f79dcb 100644 Binary files a/docs/reference/mmkin-1.png and b/docs/reference/mmkin-1.png differ diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png index 40109afc..5dd60e1c 100644 Binary files a/docs/reference/mmkin-2.png and b/docs/reference/mmkin-2.png differ diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png index e80448ab..9351ca24 100644 Binary files a/docs/reference/mmkin-3.png and b/docs/reference/mmkin-3.png differ diff --git a/docs/reference/mmkin-4.png b/docs/reference/mmkin-4.png index 02976ced..69bd6e50 100644 Binary files a/docs/reference/mmkin-4.png and b/docs/reference/mmkin-4.png differ diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png index 4c771bc9..c1c05eea 100644 Binary files a/docs/reference/mmkin-5.png and b/docs/reference/mmkin-5.png differ diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index 67837ea3..7a01313f 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -75,7 +75,7 @@ datasets specified in its first two arguments." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -112,6 +112,9 @@ datasets specified in its first two arguments." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -120,7 +123,7 @@ datasets specified in its first two arguments." />
      • - + @@ -140,7 +143,7 @@ datasets specified in its first two arguments." />

        Fit one or more kinetic models with one or more state variables to one or more datasets

        - Source: R/mmkin.R + Source: R/mmkin.R
        @@ -149,13 +152,13 @@ more datasets datasets specified in its first two arguments.

        - 
        mmkin(
-  models = c("SFO", "FOMC", "DFOP"),
-  datasets,
-  cores = round(detectCores()/2),
-  cluster = NULL,
-  ...
-)
        + 
        mmkin(
+  models = c("SFO", "FOMC", "DFOP"),
+  datasets,
+  cores = detectCores(),
+  cluster = NULL,
+  ...
+)

        Arguments

      • @@ -176,7 +179,8 @@ data for mkinfit.

      +argument to use multiple logical processors. Per default, all cores +detected by parallel::detectCores() are used.

      @@ -192,49 +196,64 @@ for parallel execution.

      Value

      A two-dimensional array of mkinfit -objects that can be indexed using the model names for the first index (row index) -and the dataset names for the second index (column index).

      +objects and/or try-errors that can be indexed using the model names for the +first index (row index) and the dataset names for the second index (column +index).

      See also

      -

      [.mmkin for subsetting, plot.mmkin for +

      [.mmkin for subsetting, plot.mmkin for plotting.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # \dontrun{
-m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
-                           M1 = mkinsub("SFO", "M2"),
-                           M2 = mkinsub("SFO"), use_of_ff = "max")
      #> Successfully compiled differential equation model from auto-generated C code.
      
-m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"),
-                            M1 = mkinsub("SFO", "M2"),
-                            M2 = mkinsub("SFO"), use_of_ff = "max")
      #> Successfully compiled differential equation model from auto-generated C code.
      
-models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)
-datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)
-names(datasets) <- paste("Dataset", 1:3)
-
-time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE))
-time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
      #> Warning: Optimisation did not converge:
-#> false convergence (8)
      
-time_default
      #>    user  system elapsed 
-#>   4.471   0.405   1.961 
      time_1
      #>    user  system elapsed 
-#>   5.883   0.000   5.887 
      
-endpoints(fits.0[["SFO_lin", 2]])
      #> $ff
+m_synth_SFO_lin <- mkinmod(parent = mkinsub("SFO", "M1"),
+                           M1 = mkinsub("SFO", "M2"),
+                           M2 = mkinsub("SFO"), use_of_ff = "max")
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
+m_synth_FOMC_lin <- mkinmod(parent = mkinsub("FOMC", "M1"),
+                            M1 = mkinsub("SFO", "M2"),
+                            M2 = mkinsub("SFO"), use_of_ff = "max")
+
      #> Successfully compiled differential equation model from auto-generated C code.
      
+models <- list(SFO_lin = m_synth_SFO_lin, FOMC_lin = m_synth_FOMC_lin)
+datasets <- lapply(synthetic_data_for_UBA_2014[1:3], function(x) x$data)
+names(datasets) <- paste("Dataset", 1:3)
+
+time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE))
+time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
+
      #> Warning: Optimisation did not converge:
+#> false convergence (8)
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0117
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0174
      
+time_default
+
      #>    user  system elapsed 
+#>   4.828   0.413   1.467 
      time_1
+
      #>    user  system elapsed 
+#>   5.228   0.000   5.231 
      
+endpoints(fits.0[["SFO_lin", 2]])
+
      #> $ff
 #>   parent_M1 parent_sink       M1_M2     M1_sink 
-#>   0.7340479   0.2659521   0.7505687   0.2494313 
+#>   0.7340478   0.2659522   0.7505691   0.2494309 
 #> 
 #> $distimes
 #>              DT50       DT90
 #> parent  0.8777688   2.915885
-#> M1      2.3257457   7.725960
-#> M2     33.7200848 112.015697
+#> M1      2.3257466   7.725963
+#> M2     33.7200800 112.015681
 #> 
      
 # plot.mkinfit handles rows or columns of mmkin result objects
-plot(fits.0[1, ])
      plot(fits.0[1, ], obs_var = c("M1", "M2"))
      plot(fits.0[, 1])
      # Use double brackets to extract a single mkinfit object, which will be plotted
+plot(fits.0[1, ])
+
      plot(fits.0[1, ], obs_var = c("M1", "M2"))
+
      plot(fits.0[, 1])
+
      # Use double brackets to extract a single mkinfit object, which will be plotted
 # by plot.mkinfit and can be plotted using plot_sep
-plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)
      plot_sep(fits.0[[1, 1]])
+plot(fits.0[[1, 1]], sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE)
+
      plot_sep(fits.0[[1, 1]])
 # Plotting with mmkin (single brackets, extracting an mmkin object) does not
 # allow to plot the observed variables separately
-plot(fits.0[1, 1])
      # }
+plot(fits.0[1, 1])
+
      # }
      @@ -252,7 +271,7 @@ plotting.

      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/nafta-1.png b/docs/reference/nafta-1.png index 9025f2bb..4593158c 100644 Binary files a/docs/reference/nafta-1.png and b/docs/reference/nafta-1.png differ diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html index 34cce546..9630e230 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -76,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -113,6 +113,9 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -121,7 +124,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
      • - + @@ -140,7 +143,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />

        Evaluate parent kinetics using the NAFTA guidance

        - Source: R/nafta.R + Source: R/nafta.R
        @@ -152,10 +155,10 @@ and plotting.

        order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP.

        - 
        nafta(ds, title = NA, quiet = FALSE, ...)
+    
        nafta(ds, title = NA, quiet = FALSE, ...)
 
 # S3 method for nafta
-print(x, quiet = TRUE, digits = 3, ...)
        
+print(x, quiet = TRUE, digits = 3, ...)

        Arguments

      • The number of cores to be used for multicore processing. This is only used when the cluster argument is NULL. On Windows machines, cores > 1 is not supported, you need to use the cluster -argument to use multiple logical processors.
      cluster
      @@ -208,10 +211,15 @@ Degradation mmkin object containing the fits of the three models. The list element named "title" contains the title of the dataset used. The list element "data" contains the dataset used in the fits.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
-  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
      #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
      #> The representative half-life of the IORE model is longer than the one corresponding
      #> to the terminal degradation rate found with the DFOP model.
      #> The representative half-life obtained from the DFOP model may be used
        print(nafta_evaluation)
      #> Sums of squares:
+  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00192
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00258
      #> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
      #> The representative half-life of the IORE model is longer than the one corresponding
      #> to the terminal degradation rate found with the DFOP model.
      #> The representative half-life obtained from the DFOP model may be used
        print(nafta_evaluation)
+
      #> Sums of squares:
 #>       SFO      IORE      DFOP 
 #> 1378.6832  615.7730  517.8836 
 #> 
@@ -220,17 +228,17 @@ list element "data" contains the dataset used in the fits.
      
 #> 
 #> Parameters:
 #> $SFO
-#>               Estimate   Pr(>t)    Lower   Upper
-#> parent_0       83.7558 1.80e-14 77.18268 90.3288
-#> k_parent_sink   0.0017 7.43e-05  0.00112  0.0026
-#> sigma           8.7518 1.22e-05  5.64278 11.8608
+#>          Estimate   Pr(>t)    Lower   Upper
+#> parent_0  83.7558 1.80e-14 77.18268 90.3288
+#> k_parent   0.0017 7.43e-05  0.00112  0.0026
+#> sigma      8.7518 1.22e-05  5.64278 11.8608
 #> 
 #> $IORE
-#>                     Estimate Pr(>t)    Lower    Upper
-#> parent_0            9.69e+01     NA 8.88e+01 1.05e+02
-#> k__iore_parent_sink 8.40e-14     NA 1.79e-18 3.94e-09
-#> N_parent            6.68e+00     NA 4.19e+00 9.17e+00
-#> sigma               5.85e+00     NA 3.76e+00 7.94e+00
+#>                Estimate Pr(>t)    Lower    Upper
+#> parent_0       9.69e+01     NA 8.88e+01 1.05e+02
+#> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09
+#> N_parent       6.68e+00     NA 4.19e+00 9.17e+00
+#> sigma          5.85e+00     NA 3.76e+00 7.94e+00
 #> 
 #> $DFOP
 #>          Estimate   Pr(>t)    Lower    Upper
@@ -248,7 +256,8 @@ list element "data" contains the dataset used in the fits.
      
 #> DFOP  429    2380      841
 #> 
 #> Representative half-life:
-#> [1] 841.41
        plot(nafta_evaluation)
      
+#> [1] 841.41
        plot(nafta_evaluation)
+
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/nlme-1.png b/docs/reference/nlme-1.png index bc04dea8..193722c7 100644 Binary files a/docs/reference/nlme-1.png and b/docs/reference/nlme-1.png differ diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html index 85929929..4e61a08b 100644 --- a/docs/reference/nlme.html +++ b/docs/reference/nlme.html @@ -43,7 +43,7 @@ +datasets. They are used internally by the nlme.mmkin() method." /> @@ -75,7 +75,7 @@ datasets." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -112,6 +112,9 @@ datasets." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -120,7 +123,7 @@ datasets." />
      • - + @@ -139,7 +142,7 @@ datasets." />

        Helper functions to create nlme models from mmkin row objects

        - Source: R/nlme.R + Source: R/nlme.R
        @@ -147,14 +150,14 @@ datasets." />

        These functions facilitate setting up a nonlinear mixed effects model for an mmkin row object. An mmkin row object is essentially a list of mkinfit objects that have been obtained by fitting the same model to a list of -datasets.

        +datasets. They are used internally by the nlme.mmkin() method.

        - 
        nlme_function(object)
+    
        nlme_function(object)
 
-mean_degparms(object, random = FALSE)
+mean_degparms(object, random = FALSE)
 
-nlme_data(object)
        
+nlme_data(object)

        Arguments

      • @@ -175,50 +178,51 @@ datasets.

      If random is FALSE (default), a named vector containing mean values of the fitted degradation model parameters. If random is TRUE, a list with fixed and random effects, in the format required by the start argument of -nlme for the case of a single grouping variable ds?

      +nlme for the case of a single grouping variable ds.

      A groupedData object

      See also

      nlme.mmkin

      Examples

      - 
      sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
-m_SFO <- mkinmod(parent = mkinsub("SFO"))
-d_SFO_1 <- mkinpredict(m_SFO,
-  c(k_parent = 0.1),
-  c(parent = 98), sampling_times)
-d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
-d_SFO_2 <- mkinpredict(m_SFO,
-  c(k_parent = 0.05),
-  c(parent = 102), sampling_times)
-d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
-d_SFO_3 <- mkinpredict(m_SFO,
-  c(k_parent = 0.02),
-  c(parent = 103), sampling_times)
-d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
-
-d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
-d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
-d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
-ds <- c(d1 = d1, d2 = d2, d3 = d3)
-
-f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
-mean_dp <- mean_degparms(f)
-grouped_data <- nlme_data(f)
-nlme_f <- nlme_function(f)
+    
      sampling_times = c(0, 1, 3, 7, 14, 28, 60, 90, 120)
+m_SFO <- mkinmod(parent = mkinsub("SFO"))
+d_SFO_1 <- mkinpredict(m_SFO,
+  c(k_parent = 0.1),
+  c(parent = 98), sampling_times)
+d_SFO_1_long <- mkin_wide_to_long(d_SFO_1, time = "time")
+d_SFO_2 <- mkinpredict(m_SFO,
+  c(k_parent = 0.05),
+  c(parent = 102), sampling_times)
+d_SFO_2_long <- mkin_wide_to_long(d_SFO_2, time = "time")
+d_SFO_3 <- mkinpredict(m_SFO,
+  c(k_parent = 0.02),
+  c(parent = 103), sampling_times)
+d_SFO_3_long <- mkin_wide_to_long(d_SFO_3, time = "time")
+
+d1 <- add_err(d_SFO_1, function(value) 3, n = 1)
+d2 <- add_err(d_SFO_2, function(value) 2, n = 1)
+d3 <- add_err(d_SFO_3, function(value) 4, n = 1)
+ds <- c(d1 = d1, d2 = d2, d3 = d3)
+
+f <- mmkin("SFO", ds, cores = 1, quiet = TRUE)
+mean_dp <- mean_degparms(f)
+grouped_data <- nlme_data(f)
+nlme_f <- nlme_function(f)
 # These assignments are necessary for these objects to be
 # visible to nlme and augPred when evaluation is done by
 # pkgdown to generated the html docs.
-assign("nlme_f", nlme_f, globalenv())
-assign("grouped_data", grouped_data, globalenv())
-
-library(nlme)
-m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
-  data = grouped_data,
-  fixed = parent_0 + log_k_parent_sink ~ 1,
-  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
-  start = mean_dp)
-summary(m_nlme)
      #> Nonlinear mixed-effects model fit by maximum likelihood
+assign("nlme_f", nlme_f, globalenv())
+assign("grouped_data", grouped_data, globalenv())
+
+library(nlme)
+m_nlme <- nlme(value ~ nlme_f(name, time, parent_0, log_k_parent_sink),
+  data = grouped_data,
+  fixed = parent_0 + log_k_parent_sink ~ 1,
+  random = pdDiag(parent_0 + log_k_parent_sink ~ 1),
+  start = mean_dp)
+summary(m_nlme)
+
      #> Nonlinear mixed-effects model fit by maximum likelihood
 #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
 #>  Data: grouped_data 
 #>        AIC      BIC    logLik
@@ -228,24 +232,25 @@ nlme for the case of a single grouping variable ds?
      
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>            parent_0 log_k_parent_sink Residual
-#> StdDev: 0.004139378         0.6800778 2.489396
+#>             parent_0 log_k_parent_sink Residual
+#> StdDev: 0.0006768135         0.6800777 2.489397
 #> 
 #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 
 #>                       Value Std.Error DF   t-value p-value
-#> parent_0          101.74884 0.6456057 44 157.60213       0
-#> log_k_parent_sink  -3.05575 0.4015812 44  -7.60929       0
+#> parent_0          101.74884 0.6456014 44 157.60321       0
+#> log_k_parent_sink  -3.05575 0.4015811 44  -7.60929       0
 #>  Correlation: 
 #>                   prnt_0
 #> log_k_parent_sink 0.026 
 #> 
 #> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -2.13168782 -0.68780415  0.08282907  0.85913228  2.95298904 
+#>        Min         Q1        Med         Q3        Max 
+#> -2.1317488 -0.6878121  0.0828385  0.8592270  2.9529864 
 #> 
 #> Number of Observations: 48
-#> Number of Groups: 3 
      plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
      # augPred does not seem to work on fits with more than one state
-# variable
+#> Number of Groups: 3 
      plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
+
      # augPred does not seem to work on fits with more than one state
+# variable
 
 
      
   
      
@@ -263,7 +268,7 @@ nlme for the case of a single grouping variable ds?
      
 
 
 
      
-  
      Site built with pkgdown 1.5.1.
      
+  
      Site built with pkgdown 1.6.1.
      
 
      
 
       
diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png
index 67f279da..564f7e2b 100644
Binary files a/docs/reference/nlme.mmkin-1.png and b/docs/reference/nlme.mmkin-1.png differ
diff --git a/docs/reference/nlme.mmkin-2.png b/docs/reference/nlme.mmkin-2.png
index 16a5d7bd..a9ce4636 100644
Binary files a/docs/reference/nlme.mmkin-2.png and b/docs/reference/nlme.mmkin-2.png differ
diff --git a/docs/reference/nlme.mmkin-3.png b/docs/reference/nlme.mmkin-3.png
index 73426834..0b7ce0f6 100644
Binary files a/docs/reference/nlme.mmkin-3.png and b/docs/reference/nlme.mmkin-3.png differ
diff --git a/docs/reference/nlme.mmkin-4.png b/docs/reference/nlme.mmkin-4.png
index 9b46b425..d7c68dd5 100644
Binary files a/docs/reference/nlme.mmkin-4.png and b/docs/reference/nlme.mmkin-4.png differ
diff --git a/docs/reference/nlme.mmkin-5.png b/docs/reference/nlme.mmkin-5.png
index 3398dd4a..2c299b08 100644
Binary files a/docs/reference/nlme.mmkin-5.png and b/docs/reference/nlme.mmkin-5.png differ
diff --git a/docs/reference/nlme.mmkin-6.png b/docs/reference/nlme.mmkin-6.png
index 7b663991..2bcb9cec 100644
Binary files a/docs/reference/nlme.mmkin-6.png and b/docs/reference/nlme.mmkin-6.png differ
diff --git a/docs/reference/nlme.mmkin-7.png b/docs/reference/nlme.mmkin-7.png
index d2c38a63..30e2d351 100644
Binary files a/docs/reference/nlme.mmkin-7.png and b/docs/reference/nlme.mmkin-7.png differ
diff --git a/docs/reference/nlme.mmkin.html b/docs/reference/nlme.mmkin.html
index c0fb499d..af3b712c 100644
--- a/docs/reference/nlme.mmkin.html
+++ b/docs/reference/nlme.mmkin.html
@@ -74,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
       
       
         mkin
-        0.9.50.2
+        0.9.50.3
       
     
 
@@ -111,6 +111,9 @@ have been obtained by fitting the same model to a list of datasets." />
     
    • 
       Example evaluation of NAFTA SOP Attachment examples
     
      • 
+    
    • 
+      Some benchmark timings
+    
      • 
   
 
 
    • 
@@ -119,7 +122,7 @@ have been obtained by fitting the same model to a list of datasets." />
       
       
        
         
      • 
-  
+  
     
      
   
@@ -138,7 +141,7 @@ have been obtained by fitting the same model to a list of datasets." />
   
        
     
        
     
        Create an nlme model for an mmkin row object
        
-    Source: R/nlme.mmkin.R
+    Source: R/nlme.mmkin.R
     
     
        
 
@@ -149,28 +152,28 @@ have been obtained by fitting the same model to a list of datasets.
        
     
        
 
     
        # S3 method for mmkin
-nlme(
-  model,
-  data = sys.frame(sys.parent()),
-  fixed,
-  random = fixed,
-  groups,
-  start,
-  correlation = NULL,
-  weights = NULL,
-  subset,
-  method = c("ML", "REML"),
-  na.action = na.fail,
-  naPattern,
-  control = list(),
-  verbose = FALSE
-)
+nlme(
+  model,
+  data = sys.frame(sys.parent()),
+  fixed,
+  random = fixed,
+  groups,
+  start,
+  correlation = NULL,
+  weights = NULL,
+  subset,
+  method = c("ML", "REML"),
+  na.action = na.fail,
+  naPattern,
+  control = list(),
+  verbose = FALSE
+)
 
 # S3 method for nlme.mmkin
-print(x, ...)
+print(x, ...)
 
 # S3 method for nlme.mmkin
-update(object, ...)
        
+update(object, ...)

      • Arguments

      @@ -181,7 +184,7 @@ have been obtained by fitting the same model to a list of datasets.

      - + @@ -254,165 +257,181 @@ parameters taken from the mmkin object are used

      with additional elements

      See also

      -

      nlme_function

      +

      nlme_function

      Examples

      - 
      ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
-library(nlme)
-endpoints(f[[1]])
      #> $distimes
+    
      ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
+f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
      library(nlme)
+endpoints(f[[1]])
+
      #> $distimes
 #>            DT50     DT90
 #> parent 11.96183 39.73634
-#> 
      f_nlme <- nlme(f)
-print(f_nlme)
      #> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) 
+#> 
      f_nlme <- nlme(f)
+print(f_nlme)
+
      #> Nonlinear mixed-effects model fit by maximum likelihood
+#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) 
 #>   Data: "Not shown" 
 #>   Log-likelihood: -307.5269
-#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
-#>          parent_0 log_k_parent_sink 
-#>         85.540979         -3.229602 
+#>   Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) 
+#>     parent_0 log_k_parent 
+#>    85.541149    -3.229596 
 #> 
 #> Random effects:
-#>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
+#>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0 log_k_parent_sink Residual
-#> StdDev: 1.308245          1.288586 6.304923
+#>         parent_0 log_k_parent Residual
+#> StdDev:  1.30857     1.288591 6.304906
 #> 
 #> Number of Observations: 90
-#> Number of Groups: 5 
      endpoints(f_nlme)
      #> $distimes
+#> Number of Groups: 5 
      endpoints(f_nlme)
+
      #> $distimes
 #>            DT50     DT90
-#> parent 17.51556 58.18543
+#> parent 17.51545 58.18505
 #> 
      # \dontrun{
-  f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
-  update(f_nlme_2, random = parent_0 ~ 1)
      #> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) 
+  f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
+  update(f_nlme_2, random = parent_0 ~ 1)
+
      #> Nonlinear mixed-effects model fit by maximum likelihood
+#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) 
 #>   Data: "Not shown" 
 #>   Log-likelihood: -404.3729
-#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
-#>          parent_0 log_k_parent_sink 
-#>         75.933480         -3.555983 
+#>   Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) 
+#>     parent_0 log_k_parent 
+#>    75.933480    -3.555983 
 #> 
 #> Random effects:
 #>  Formula: parent_0 ~ 1 | ds
 #>            parent_0 Residual
-#> StdDev: 0.002416802 21.63027
+#> StdDev: 0.002416792 21.63027
 #> 
 #> Number of Observations: 90
 #> Number of Groups: 5 
        # Test on some real data
-  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
-   function(x) x$data[c("name", "time", "value")])
-  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
-    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
-  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
-    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
-  m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
-    A1 = mkinsub("SFO"), quiet = TRUE)
-  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
-    A1 = mkinsub("SFO"), quiet = TRUE)
-
-  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
-   "SFO-SFO-ff" = m_sfo_sfo_ff,
-   "FOMC-SFO" = m_fomc_sfo,
-   "DFOP-SFO" = m_dfop_sfo),
-    ds_2, quiet = TRUE)
-  plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
      
-  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
+  ds_2 <- lapply(experimental_data_for_UBA_2019[6:10],
+   function(x) x$data[c("name", "time", "value")])
+  m_sfo_sfo <- mkinmod(parent = mkinsub("SFO", "A1"),
+    A1 = mkinsub("SFO"), use_of_ff = "min", quiet = TRUE)
+  m_sfo_sfo_ff <- mkinmod(parent = mkinsub("SFO", "A1"),
+    A1 = mkinsub("SFO"), use_of_ff = "max", quiet = TRUE)
+  m_fomc_sfo <- mkinmod(parent = mkinsub("FOMC", "A1"),
+    A1 = mkinsub("SFO"), quiet = TRUE)
+  m_dfop_sfo <- mkinmod(parent = mkinsub("DFOP", "A1"),
+    A1 = mkinsub("SFO"), quiet = TRUE)
+
+  f_2 <- mmkin(list("SFO-SFO" = m_sfo_sfo,
+   "SFO-SFO-ff" = m_sfo_sfo_ff,
+   "FOMC-SFO" = m_fomc_sfo,
+   "DFOP-SFO" = m_dfop_sfo),
+    ds_2, quiet = TRUE)
+  plot(f_2["SFO-SFO", 3:4]) # Separate fits for datasets 3 and 4
+
      
+  f_nlme_sfo_sfo <- nlme(f_2["SFO-SFO", ])
   # plot(f_nlme_sfo_sfo) # not feasible with pkgdown figures
-  plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
      
+  plot(f_nlme_sfo_sfo, 3:4) # Global mixed model: Fits for datasets 3 and 4
+
      
   # With formation fractions
-  f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
-  plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
      
+  f_nlme_sfo_sfo_ff <- nlme(f_2["SFO-SFO-ff", ])
+  plot(f_nlme_sfo_sfo_ff, 3:4) # chi2 different due to different df attribution
+
      
   # For more parameters, we need to increase pnlsMaxIter and the tolerance
   # to get convergence
-  f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
-    control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
      #> 
+  f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
+    control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
+
      #> 
 #> **Iteration 1
-#> LME step: Loglik: -394.1603, nlminb iterations: 2
+#> LME step: Loglik: -394.1603, nlminb iterations: 3
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5 
-#> -0.2079863  0.8563823  1.7454253  1.0917707  1.2756955 
+#> -0.2079793  0.8563830  1.7454105  1.0917354  1.2756825 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.8814 
-#>  fixed effects: 94.17379  -5.473189  -0.6970234  -0.202509  2.103883  
+#> PNLS step: RSS =  643.8803 
+#>  fixed effects: 94.17379  -5.473193  -0.6970236  -0.2025091  2.103883  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>     fixed  reStruct 
-#> 0.7959873 0.1447512 
+#> 0.7960134 0.1447728 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -396.3824, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -1.712406e-01 -2.278541e-05  1.842120e+00  1.073975e+00  1.322924e+00 
+#> -1.712404e-01 -2.432655e-05  1.842120e+00  1.073975e+00  1.322925e+00 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.8025 
-#>  fixed effects: 94.17385  -5.473491  -0.6970406  -0.2025139  2.103871  
+#> PNLS step: RSS =  643.8035 
+#>  fixed effects: 94.17385  -5.473487  -0.6970404  -0.2025137  2.103871  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
-#>       fixed    reStruct 
-#> 5.51758e-05 1.26861e-03 
+#>        fixed     reStruct 
+#> 5.382757e-05 1.236667e-03 
 #> 
 #> **Iteration 3
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -0.1712500923 -0.0001515734  1.8420972550  1.0739796967  1.3229177241 
+#> -0.1712499044 -0.0001499831  1.8420971364  1.0739799123  1.3229167796 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7941 
-#>  fixed effects: 94.17386  -5.473523  -0.6970424  -0.2025146  2.103869  
+#> PNLS step: RSS =  643.7948 
+#>  fixed effects: 94.17386  -5.473521  -0.6970422  -0.2025144  2.10387  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>        fixed     reStruct 
-#> 5.792621e-06 1.335434e-04 
+#> 6.072817e-06 1.400857e-04 
 #> 
 #> **Iteration 4
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -0.1712517206 -0.0001651603  1.8420950864  1.0739800294  1.3229173529 
+#> -0.1712529502 -0.0001641277  1.8420957542  1.0739797181  1.3229173076 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7949 
-#>  fixed effects: 94.17386  -5.473521  -0.6970423  -0.2025145  2.10387  
+#> PNLS step: RSS =  643.7936 
+#>  fixed effects: 94.17386  -5.473526  -0.6970426  -0.2025146  2.103869  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>        fixed     reStruct 
-#> 4.025781e-07 9.628656e-06 
        f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
-    control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
      #> 
+#> 1.027451e-06 2.275704e-05 
        f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
+    control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
+
      #> 
 #> **Iteration 1
-#> LME step: Loglik: -404.9583, nlminb iterations: 1
+#> LME step: Loglik: -404.9582, nlminb iterations: 1
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5        ds6 
-#> -0.4114357  0.9798641  1.6990035  0.7293314  0.3354323  1.7113047 
+#> -0.4114355  0.9798697  1.6990037  0.7293315  0.3354323  1.7113046 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.3642 
-#>  fixed effects: 93.82269  -5.455991  -0.6788957  -1.862196  -4.199671  0.0553284  
+#> PNLS step: RSS =  630.3644 
+#>  fixed effects: 93.82269  -5.455991  -0.6788957  -1.862196  -4.199671  0.05532828  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>     fixed  reStruct 
-#> 0.7879730 0.5822574 
+#> 0.7885368 0.5822683 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -407.7755, nlminb iterations: 11
 #> reStruct  parameters:
 #>          ds1          ds2          ds3          ds4          ds5          ds6 
-#> -0.371224105  0.003056163  1.789939431  0.724671132  0.301602942  1.754200482 
+#> -0.371224133  0.003056179  1.789939402  0.724671158  0.301602977  1.754200729 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.364 
-#>  fixed effects: 93.82269  -5.455991  -0.6788958  -1.862196  -4.199671  0.05532834  
+#> PNLS step: RSS =  630.3633 
+#>  fixed effects: 93.82269  -5.455992  -0.6788958  -1.862196  -4.199671  0.05532831  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>        fixed     reStruct 
-#> 9.814652e-07 1.059239e-05 
        plot(f_2["FOMC-SFO", 3:4])
        plot(f_nlme_fomc_sfo, 3:4)
      
-  plot(f_2["DFOP-SFO", 3:4])
        plot(f_nlme_dfop_sfo, 3:4)
      
-  anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
      #>                 Model df       AIC       BIC    logLik   Test   L.Ratio p-value
-#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                         
-#> f_nlme_fomc_sfo     2 11  818.5151  853.0089 -398.2576 1 vs 2  21.33957  <.0001
-#> f_nlme_sfo_sfo      3  9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66697  <.0001
        anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
      #>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
-#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                        
+#> 4.789774e-07 2.200661e-05 
        plot(f_2["FOMC-SFO", 3:4])
+
        plot(f_nlme_fomc_sfo, 3:4)
+
      
+  plot(f_2["DFOP-SFO", 3:4])
+
        plot(f_nlme_dfop_sfo, 3:4)
+
      
+  anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
+
      #>                 Model df       AIC       BIC    logLik   Test   L.Ratio p-value
+#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9273                         
+#> f_nlme_fomc_sfo     2 11  818.5149  853.0087 -398.2575 1 vs 2  21.33975  <.0001
+#> f_nlme_sfo_sfo      3  9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66716  <.0001
        anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
+
      #>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
+#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9273                        
 #> f_nlme_sfo_sfo      2  9 1085.1821 1113.4043 -533.5910 1 vs 2 249.3274  <.0001
      
-  endpoints(f_nlme_sfo_sfo)
      #> $ff
+  endpoints(f_nlme_sfo_sfo)
+
      #> $ff
 #> parent_sink   parent_A1     A1_sink 
 #>   0.5912432   0.4087568   1.0000000 
 #> 
@@ -420,15 +439,16 @@ with additional elements
      
 #>            DT50     DT90
 #> parent 19.13518  63.5657
 #> A1     66.02155 219.3189
-#> 
        endpoints(f_nlme_dfop_sfo)
      #> $ff
+#> 
        endpoints(f_nlme_dfop_sfo)
+
      #> $ff
 #>   parent_A1 parent_sink 
 #>   0.2768574   0.7231426 
 #> 
 #> $distimes
-#>             DT50     DT90  DT50_k1  DT50_k2
-#> parent  11.07091 104.6320 4.462384 46.20825
-#> A1     162.30518 539.1661       NA       NA
-#> 
      # }
+#>             DT50     DT90 DT50back  DT50_k1  DT50_k2
+#> parent  11.07091 104.6320 31.49738 4.462384 46.20825
+#> A1     162.30536 539.1667       NA       NA       NA
+#> 
      # }
 
      
   
      
   
 
 
      
-  
      Site built with pkgdown 1.5.1.
      
+  
      Site built with pkgdown 1.6.1.
      
 
      
 
       
diff --git a/docs/reference/parms.html b/docs/reference/parms.html
index f62b3898..30dc7770 100644
--- a/docs/reference/parms.html
+++ b/docs/reference/parms.html
@@ -74,7 +74,7 @@ considering the error structure that was assumed for the fit." />
       
       
         mkin
-        0.9.50.2
+        0.9.50.3
       
     
 
@@ -111,6 +111,9 @@ considering the error structure that was assumed for the fit." />
     
    • 
       Example evaluation of NAFTA SOP Attachment examples
     
      • 
+    
    • 
+      Some benchmark timings
+    
      • 
   
 
 
    • 
@@ -119,7 +122,7 @@ considering the error structure that was assumed for the fit." />
       
       
        
         
      • 
-  
+  
     
      
   
@@ -138,7 +141,7 @@ considering the error structure that was assumed for the fit." />
   
        
     
        
     
        Extract model parameters from mkinfit models
        
-    Source: R/parms.mkinfit.R
+    Source: R/parms.mkinfit.R
     
     
        
 
@@ -148,17 +151,21 @@ model parameters, in order to avoid working with a fitted model without
 considering the error structure that was assumed for the fit.
        
     
        
 
-    
        parms(object, ...)
+    
        parms(object, ...)
 
 # S3 method for mkinfit
-parms(object, transformed = FALSE, ...)
        
+parms(object, transformed = FALSE, ...)
+
+# S3 method for mmkin
+parms(object, transformed = FALSE, ...)
        
 
     
        Arguments
      • data

      Should the data be printed?

      Ignored, data are taken from the mmkin model
      fixed
      - + @@ -173,24 +180,94 @@ as used internally during the optimisation?

      Value

      -

      A numeric vector of fitted model parameters

      +

      For mkinfit objects, a numeric vector of fitted model parameters. +For mmkin row objects, a matrix with the parameters with a +row for each dataset. If the mmkin object has more than one row, a list of +such matrices is returned.

      Examples

      - 
      fit <- mkinfit("SFO", FOCUS_2006_C)
      #> Ordinary least squares optimisation
      #> Sum of squared residuals at call 1: 2388.077
-#> Sum of squared residuals at call 3: 2388.077
-#> Sum of squared residuals at call 4: 247.1962
-#> Sum of squared residuals at call 7: 200.6791
-#> Sum of squared residuals at call 10: 197.7231
-#> Sum of squared residuals at call 11: 197.0872
-#> Sum of squared residuals at call 14: 196.535
-#> Sum of squared residuals at call 15: 196.535
-#> Sum of squared residuals at call 16: 196.535
-#> Sum of squared residuals at call 17: 196.5334
-#> Sum of squared residuals at call 20: 196.5334
-#> Sum of squared residuals at call 25: 196.5334
-#> Negative log-likelihood at call 31: 26.64668
      #> Optimisation successfully terminated.
      parms(fit)
      #>      parent_0 k_parent_sink         sigma 
-#>    82.4921598     0.3060633     4.6730124 
      parms(fit, transformed = TRUE)
      #>          parent_0 log_k_parent_sink             sigma 
-#>         82.492160         -1.183963          4.673012
      + 
      # mkinfit objects
+fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
+parms(fit)
+
      #>   parent_0   k_parent      sigma 
+#> 82.4921598  0.3060633  4.6730124 
      parms(fit, transformed = TRUE)
+
      #>     parent_0 log_k_parent        sigma 
+#>    82.492160    -1.183963     4.673012 
      
+# mmkin objects
+ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")]))
+names(ds) <- paste("Dataset", 6:10)
+# \dontrun{
+fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00408
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0492
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00985
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00815
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
      parms(fits["SFO", ])
+
      #>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
+#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
+#> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
+#> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
      parms(fits[, 2])
+
      #> $SFO
+#>             Dataset 7
+#> parent_0 82.666781678
+#> k_parent  0.009647805
+#> sigma     7.040168584
+#> 
+#> $FOMC
+#>           Dataset 7
+#> parent_0 92.6837649
+#> alpha     0.4967832
+#> beta     14.1451255
+#> sigma     1.9167519
+#> 
+#> $DFOP
+#>             Dataset 7
+#> parent_0 91.058971503
+#> k1        0.044946770
+#> k2        0.002868336
+#> g         0.526942415
+#> sigma     2.221302196
+#> 
      parms(fits)
+
      #> $SFO
+#>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
+#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
+#> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
+#> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
+#> 
+#> $FOMC
+#>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10
+#> parent_0 95.558575 92.6837649 90.719787 98.383939 94.8481458
+#> alpha     1.338667  0.4967832  1.639099  1.074460  0.2805272
+#> beta     13.033315 14.1451255  5.007077  4.397126  6.9052224
+#> sigma     1.847671  1.9167519  1.066063  3.146056  1.6222778
+#> 
+#> $DFOP
+#>            Dataset 6    Dataset 7   Dataset 8   Dataset 9   Dataset 10
+#> parent_0 96.55213663 91.058971503 90.34509469 98.14858850 94.311323409
+#> k1        0.21954589  0.044946770  0.41232289  0.31697588  0.080663853
+#> k2        0.02957934  0.002868336  0.07581767  0.03260384  0.003425417
+#> g         0.44845068  0.526942415  0.66091965  0.65322767  0.342652880
+#> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831
+#> 
      parms(fits, transformed = TRUE)
+
      #> $SFO
+#>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10
+#> parent_0     88.522754 82.666782 86.854731 91.777931  82.148094
+#> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090
+#> sigma         5.152745  7.040169  3.676964  6.466923   6.504577
+#> 
+#> $FOMC
+#>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10
+#> parent_0  95.5585751 92.6837649 90.7197870 98.38393897  94.848146
+#> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085
+#> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278
+#> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278
+#> 
+#> $DFOP
+#>           Dataset 6   Dataset 7  Dataset 8  Dataset 9 Dataset 10
+#> parent_0 96.5521366 91.05897150 90.3450947 98.1485885  94.311323
+#> log_k1   -1.5161940 -3.10227638 -0.8859485 -1.1489296  -2.517465
+#> log_k2   -3.5206791 -5.85402317 -2.5794240 -3.4233253  -5.676532
+#> g_ilr    -0.1463234  0.07627854  0.4719196  0.4477805  -0.460676
+#> sigma     1.3569047  2.22130220  1.3416908  2.8715985   1.942068
+#> 
      # }
+
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/plot.mkinfit-1.png b/docs/reference/plot.mkinfit-1.png index c6a415d7..fc195031 100644 Binary files a/docs/reference/plot.mkinfit-1.png and b/docs/reference/plot.mkinfit-1.png differ diff --git a/docs/reference/plot.mkinfit-2.png b/docs/reference/plot.mkinfit-2.png index 5dd3731e..994b516e 100644 Binary files a/docs/reference/plot.mkinfit-2.png and b/docs/reference/plot.mkinfit-2.png differ diff --git a/docs/reference/plot.mkinfit-3.png b/docs/reference/plot.mkinfit-3.png index 59cf8f8d..28789544 100644 Binary files a/docs/reference/plot.mkinfit-3.png and b/docs/reference/plot.mkinfit-3.png differ diff --git a/docs/reference/plot.mkinfit-4.png b/docs/reference/plot.mkinfit-4.png index d9867952..edea6fce 100644 Binary files a/docs/reference/plot.mkinfit-4.png and b/docs/reference/plot.mkinfit-4.png differ diff --git a/docs/reference/plot.mkinfit-5.png b/docs/reference/plot.mkinfit-5.png index 1109c8df..b4b6528b 100644 Binary files a/docs/reference/plot.mkinfit-5.png and b/docs/reference/plot.mkinfit-5.png differ diff --git a/docs/reference/plot.mkinfit-6.png b/docs/reference/plot.mkinfit-6.png index 230c90e9..a939f8e9 100644 Binary files a/docs/reference/plot.mkinfit-6.png and b/docs/reference/plot.mkinfit-6.png differ diff --git a/docs/reference/plot.mkinfit-7.png b/docs/reference/plot.mkinfit-7.png index b23427b5..ae7807a8 100644 Binary files a/docs/reference/plot.mkinfit-7.png and b/docs/reference/plot.mkinfit-7.png differ diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html index f78ccf73..b82a8170 100644 --- a/docs/reference/plot.mkinfit.html +++ b/docs/reference/plot.mkinfit.html @@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -111,6 +111,9 @@ observed data together with the solution of the fitted model." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -119,7 +122,7 @@ observed data together with the solution of the fitted model." />
      • - + @@ -138,7 +141,7 @@ observed data together with the solution of the fitted model." />

        Plot the observed data and the fitted model of an mkinfit object

        - Source: R/plot.mkinfit.R + Source: R/plot.mkinfit.R
        @@ -149,49 +152,49 @@ observed data together with the solution of the fitted model.

        # S3 method for mkinfit
-plot(
-  x,
-  fit = x,
-  obs_vars = names(fit$mkinmod$map),
-  xlab = "Time",
-  ylab = "Observed",
-  xlim = range(fit$data$time),
-  ylim = "default",
-  col_obs = 1:length(obs_vars),
-  pch_obs = col_obs,
-  lty_obs = rep(1, length(obs_vars)),
-  add = FALSE,
-  legend = !add,
-  show_residuals = FALSE,
-  show_errplot = FALSE,
-  maxabs = "auto",
-  sep_obs = FALSE,
-  rel.height.middle = 0.9,
-  row_layout = FALSE,
-  lpos = "topright",
-  inset = c(0.05, 0.05),
-  show_errmin = FALSE,
-  errmin_digits = 3,
-  frame = TRUE,
-  ...
-)
-
-plot_sep(
-  fit,
-  show_errmin = TRUE,
-  show_residuals = ifelse(identical(fit$err_mod, "const"), TRUE, "standardized"),
-  ...
-)
-
-plot_res(
-  fit,
-  sep_obs = FALSE,
-  show_errmin = sep_obs,
-  standardized = ifelse(identical(fit$err_mod, "const"), FALSE, TRUE),
-  ...
-)
-
-plot_err(fit, sep_obs = FALSE, show_errmin = sep_obs, ...)
        +plot( + x, + fit = x, + obs_vars = names(fit$mkinmod$map), + xlab = "Time", + ylab = "Observed", + xlim = range(fit$data$time), + ylim = "default", + col_obs = 1:length(obs_vars), + pch_obs = col_obs, + lty_obs = rep(1, length(obs_vars)), + add = FALSE, + legend = !add, + show_residuals = FALSE, + show_errplot = FALSE, + maxabs = "auto", + sep_obs = FALSE, + rel.height.middle = 0.9, + row_layout = FALSE, + lpos = "topright", + inset = c(0.05, 0.05), + show_errmin = FALSE, + errmin_digits = 3, + frame = TRUE, + ... +) + +plot_sep( + fit, + show_errmin = TRUE, + show_residuals = ifelse(identical(fit$err_mod, "const"), TRUE, "standardized"), + ... +) + +plot_res( + fit, + sep_obs = FALSE, + show_errmin = sep_obs, + standardized = ifelse(identical(fit$err_mod, "const"), FALSE, TRUE), + ... +) + +plot_err(fit, sep_obs = FALSE, show_errmin = sep_obs, ...)

        Arguments

      • object

      A fitted model object

      A fitted model object. Methods are implemented for +mkinfit() objects and for mmkin() objects.
      ...
      @@ -311,7 +314,7 @@ chi2 error percentage.

      - + @@ -328,22 +331,35 @@ standardized in the residual plot?

      If the current plot device is a tikz device, then latex is being used for the formatting of the chi2 error level, if show_errmin = TRUE.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
 # One parent compound, one metabolite, both single first order, path from
 # parent to sink included
 # \dontrun{
-SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
-                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
      #> Warning: Observations with value of zero were removed from the data
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
      #> Warning: Observations with value of zero were removed from the data
      plot(fit)
      plot_res(fit)
      plot_res(fit, standardized = FALSE)
      plot_err(fit)
      
+SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
+                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
+
      #> Successfully compiled differential equation model from auto-generated C code.
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
+
      #> Warning: Observations with value of zero were removed from the data
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
      fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
+
      #> Warning: Observations with value of zero were removed from the data
      plot(fit)
+
      plot_res(fit)
+
      plot_res(fit, standardized = FALSE)
+
      plot_err(fit)
+
      
 # Show the observed variables separately, with residuals
-plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
-     show_errmin = TRUE)
      
+plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
+     show_errmin = TRUE)
+
      
 # The same can be obtained with less typing, using the convenience function plot_sep
-plot_sep(fit, lpos = c("topright", "bottomright"))
      
+plot_sep(fit, lpos = c("topright", "bottomright"))
+
      
 # Show the observed variables separately, with the error model
-plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),
-     show_errmin = TRUE)
      # }
+plot(fit, sep_obs = TRUE, show_errplot = TRUE, lpos = c("topright", "bottomright"),
+     show_errmin = TRUE)
+
      # }
      @@ -361,7 +377,7 @@ latex is being used for the formatting of the chi2 error level, if
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png index 8cf969c9..438d5a53 100644 Binary files a/docs/reference/plot.mmkin-1.png and b/docs/reference/plot.mmkin-1.png differ diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png index 45d67b55..ee04b3c4 100644 Binary files a/docs/reference/plot.mmkin-2.png and b/docs/reference/plot.mmkin-2.png differ diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png index c58b371a..e06ed002 100644 Binary files a/docs/reference/plot.mmkin-3.png and b/docs/reference/plot.mmkin-3.png differ diff --git a/docs/reference/plot.mmkin-4.png b/docs/reference/plot.mmkin-4.png index 47cd7eec..5869df55 100644 Binary files a/docs/reference/plot.mmkin-4.png and b/docs/reference/plot.mmkin-4.png differ diff --git a/docs/reference/plot.mmkin-5.png b/docs/reference/plot.mmkin-5.png index 44037bb4..927c573b 100644 Binary files a/docs/reference/plot.mmkin-5.png and b/docs/reference/plot.mmkin-5.png differ diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html index 6c9f2449..2003b210 100644 --- a/docs/reference/plot.mmkin.html +++ b/docs/reference/plot.mmkin.html @@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -113,6 +113,9 @@ the fit of at least one model to the same dataset is shown." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -121,7 +124,7 @@ the fit of at least one model to the same dataset is shown." />
      • - + @@ -141,31 +144,31 @@ the fit of at least one model to the same dataset is shown." />

        Plot model fits (observed and fitted) and the residuals for a row or column of an mmkin object

        - Source: R/plot.mmkin.R + Source: R/plot.mmkin.R
        -

        When x is a row selected from an mmkin object ([.mmkin), the +

        When x is a row selected from an mmkin object ([.mmkin), the same model fitted for at least one dataset is shown. When it is a column, the fit of at least one model to the same dataset is shown.

        # S3 method for mmkin
-plot(
-  x,
-  main = "auto",
-  legends = 1,
-  resplot = c("time", "errmod"),
-  standardized = FALSE,
-  show_errmin = TRUE,
-  errmin_var = "All data",
-  errmin_digits = 3,
-  cex = 0.7,
-  rel.height.middle = 0.9,
-  ymax = "auto",
-  ...
-)
        +plot( + x, + main = "auto", + legends = 1, + resplot = c("time", "errmod"), + standardized = FALSE, + show_errmin = TRUE, + errmin_var = "All data", + errmin_digits = 3, + cex = 0.7, + rel.height.middle = 0.9, + ymax = "auto", + ... +)

        Arguments

      • ...

      Further arguments passed to plot.

      Further arguments passed to plot.
      standardized
      @@ -237,21 +240,30 @@ than two rows of plots are shown.

      If the current plot device is a tikz device, then latex is being used for the formatting of the chi2 error level.

      +

      Author

      + +

      Johannes Ranke

      Examples

      
   # \dontrun{
   # Only use one core not to offend CRAN checks
-  fits <- mmkin(c("FOMC", "HS"),
-                list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C), # named list for titles
-                cores = 1, quiet = TRUE, error_model = "tc")
      #> Warning: Optimisation did not converge:
-#> iteration limit reached without convergence (10)
        plot(fits[, "FOCUS C"])
        plot(fits["FOMC", ])
        plot(fits["FOMC", ], show_errmin = FALSE)
      
+  fits <- mmkin(c("FOMC", "HS"),
+                list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C), # named list for titles
+                cores = 1, quiet = TRUE, error_model = "tc")
+
      #> Warning: Optimisation did not converge:
+#> iteration limit reached without convergence (10)
        plot(fits[, "FOCUS C"])
+
        plot(fits["FOMC", ])
+
        plot(fits["FOMC", ], show_errmin = FALSE)
+
      
   # We can also plot a single fit, if we like the way plot.mmkin works, but then the plot
   # height should be smaller than the plot width (this is not possible for the html pages
   # generated by pkgdown, as far as I know).
-  plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
      
+  plot(fits["FOMC", "FOCUS C"]) # same as plot(fits[1, 2])
+
      
   # Show the error models
-  plot(fits["FOMC", ], resplot = "errmod")
        # }
+  plot(fits["FOMC", ], resplot = "errmod")
+
        # }
      @@ -269,7 +281,7 @@ latex is being used for the formatting of the chi2 error level.

      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/plot.nlme.mmkin-1.png b/docs/reference/plot.nlme.mmkin-1.png index fe2ef7d3..63ea381c 100644 Binary files a/docs/reference/plot.nlme.mmkin-1.png and b/docs/reference/plot.nlme.mmkin-1.png differ diff --git a/docs/reference/plot.nlme.mmkin-2.png b/docs/reference/plot.nlme.mmkin-2.png index c82d0271..df4c955e 100644 Binary files a/docs/reference/plot.nlme.mmkin-2.png and b/docs/reference/plot.nlme.mmkin-2.png differ diff --git a/docs/reference/plot.nlme.mmkin.html b/docs/reference/plot.nlme.mmkin.html index 17ec06cf..82d73dfb 100644 --- a/docs/reference/plot.nlme.mmkin.html +++ b/docs/reference/plot.nlme.mmkin.html @@ -72,7 +72,7 @@ mkin - 0.9.50.2 + 0.9.50.3 @@ -109,6 +109,9 @@
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -117,7 +120,7 @@
      • - + @@ -136,7 +139,7 @@

        Plot a fitted nonlinear mixed model obtained via an mmkin row object

        - Source: R/plot.nlme.mmkin.R + Source: R/plot.nlme.mmkin.R
        @@ -145,21 +148,21 @@ 
        # S3 method for nlme.mmkin
-plot(
-  x,
-  i = 1:ncol(x$mmkin_orig),
-  main = "auto",
-  legends = 1,
-  resplot = c("time", "errmod"),
-  standardized = FALSE,
-  show_errmin = TRUE,
-  errmin_var = "All data",
-  errmin_digits = 3,
-  cex = 0.7,
-  rel.height.middle = 0.9,
-  ymax = "auto",
-  ...
-)
        +plot( + x, + i = 1:ncol(x$mmkin_orig), + main = "auto", + legends = 1, + resplot = c("time", "errmod"), + standardized = FALSE, + show_errmin = TRUE, + errmin_var = "All data", + errmin_digits = 3, + cex = 0.7, + rel.height.middle = 0.9, + ymax = "auto", + ... +)

        Arguments

      • @@ -231,17 +234,22 @@ than two rows of plots are shown.

      Value

      The function is called for its side effect.

      +

      Author

      + +

      Johannes Ranke

      Examples

      - 
      ds <- lapply(experimental_data_for_UBA_2019[6:10],
- function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
-#plot(f) # too many panels for pkgdown
-plot(f[, 3:4])
      library(nlme)
-f_nlme <- nlme(f)
+    
      ds <- lapply(experimental_data_for_UBA_2019[6:10],
+ function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
+f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
+
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
      #> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
      #plot(f) # too many panels for pkgdown
+plot(f[, 3:4])
+
      library(nlme)
+f_nlme <- nlme(f)
 
 #plot(f_nlme) # too many panels for pkgdown
-plot(f_nlme, 3:4)
      
+plot(f_nlme, 3:4)
+
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/print.mkinds.html b/docs/reference/print.mkinds.html index af85c78f..7c7f6647 100644 --- a/docs/reference/print.mkinds.html +++ b/docs/reference/print.mkinds.html @@ -72,7 +72,7 @@ mkin - 0.9.50.2 + 0.9.50.3 @@ -109,6 +109,9 @@
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -117,7 +120,7 @@
      • - + @@ -136,7 +139,7 @@

        Print mkinds objects

        - Source: R/mkinds.R + Source: R/mkinds.R
        @@ -145,7 +148,7 @@ 
        # S3 method for mkinds
-print(x, ...)
        +print(x, ...)

        Arguments

      • @@ -176,7 +179,7 @@
      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html index 1e1f5798..26b08765 100644 --- a/docs/reference/sigma_twocomp.html +++ b/docs/reference/sigma_twocomp.html @@ -73,7 +73,7 @@ dependence of the measured value \(y\):" /> mkin - 0.9.50.2 + 0.9.50.3 @@ -110,6 +110,9 @@ dependence of the measured value \(y\):" />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -118,7 +121,7 @@ dependence of the measured value \(y\):" />
      • - + @@ -137,7 +140,7 @@ dependence of the measured value \(y\):" />

        Two-component error model

        - Source: R/sigma_twocomp.R + Source: R/sigma_twocomp.R
        @@ -146,7 +149,7 @@ dependence of the measured value \(y\):" /> dependence of the measured value \(y\):

        - 
        sigma_twocomp(y, sigma_low, rsd_high)
        + 
        sigma_twocomp(y, sigma_low, rsd_high)

        Arguments

      • @@ -201,7 +204,7 @@ measurement error in analytical chemistry. Technometrics 37(2), 176-184.

      -

      Site built with pkgdown 1.5.1.

      +

      Site built with pkgdown 1.6.1.

      diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html index ad6217e6..a2ef3b75 100644 --- a/docs/reference/summary.mkinfit.html +++ b/docs/reference/summary.mkinfit.html @@ -76,7 +76,7 @@ values." /> mkin - 0.9.50.2 + 0.9.50.3 @@ -113,6 +113,9 @@ values." />
    • Example evaluation of NAFTA SOP Attachment examples
      • +
    • + Some benchmark timings +
    • @@ -121,7 +124,7 @@ values." />
      • - + @@ -140,7 +143,7 @@ values." />

        Summary method for class "mkinfit"

        - Source: R/summary.mkinfit.R + Source: R/summary.mkinfit.R
        @@ -153,10 +156,10 @@ values.

        # S3 method for mkinfit
-summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)
+summary(object, data = TRUE, distimes = TRUE, alpha = 0.05, ...)
 
 # S3 method for summary.mkinfit
-print(x, digits = max(3, getOption("digits") - 3), ...)
        +print(x, digits = max(3, getOption("digits") - 3), ...)

        Arguments

      • @@ -226,20 +229,24 @@ and Degradation Kinetics from Environmental Fate Studies on Pesticides in EU Registration” Report of the FOCUS Work Group on Degradation Kinetics, EC Document Reference Sanco/10058/2005 version 2.0, 434 pp, http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

      +

      Author

      + +

      Johannes Ranke

      Examples

      
-  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
      #> mkin version used for fitting:    0.9.50.2 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 07:05:18 2020 
-#> Date of summary: Wed May 27 07:05:18 2020 
+  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
+
      #> mkin version used for fitting:    0.9.50.3 
+#> R version used for fitting:       4.0.3 
+#> Date of fit:     Thu Oct 15 12:41:58 2020 
+#> Date of summary: Thu Oct 15 12:41:58 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 131 model solutions performed in 0.026 s
+#> Fitted using 131 model solutions performed in 0.029 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -271,9 +278,9 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 #> 
 #> Parameter correlation:
 #>               parent_0 log_k_parent     sigma
-#> parent_0     1.000e+00    5.428e-01 1.648e-07
-#> log_k_parent 5.428e-01    1.000e+00 2.513e-07
-#> sigma        1.648e-07    2.513e-07 1.000e+00
+#> parent_0     1.000e+00    5.428e-01 1.642e-07
+#> log_k_parent 5.428e-01    1.000e+00 2.507e-07
+#> sigma        1.642e-07    2.507e-07 1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
@@ -319,7 +326,7 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 
      
 
 
      
-  
      Site built with pkgdown 1.5.1.
      
+  
      Site built with pkgdown 1.6.1.
      
 
      
 
       
-- 
cgit v1.2.1