From 95178837d3f91e84837628446b5fd468179af2b9 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 4 Jun 2019 15:09:28 +0200 Subject: Additional algorithm "d_c", more tests, docs The new algorithm tries direct optimization of the likelihood, as well as a three step procedure. In this way, we consistently get the model with the highest likelihood for SFO, DFOP and HS for all 12 new test datasets. --- docs/reference/mkinfit.html | 205 ++++++++++++++++++++++++++------------------ 1 file changed, 121 insertions(+), 84 deletions(-) (limited to 'docs/reference/mkinfit.html') diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html index bfca44fc..8cabcb21 100644 --- a/docs/reference/mkinfit.html +++ b/docs/reference/mkinfit.html @@ -71,7 +71,7 @@ Per default, parameters in the kinetic models are internally transformed in mkin - 0.9.49.4 + 0.9.49.5 @@ -163,6 +163,8 @@ Per default, parameters in the kinetic models are internally transformed in quiet = FALSE, atol = 1e-8, rtol = 1e-10, n.outtimes = 100, error_model = c("const", "obs", "tc"), + error_model_algorithm = c("d_3", "direct", "twostep", "threestep", "fourstep", "IRLS"), + reweight.tol = 1e-8, reweight.max.iter = 10, trace_parms = FALSE, ...)

Arguments

@@ -321,6 +323,41 @@ Per default, parameters in the kinetic models are internally transformed in errors follow a lognormal distribution for large values, not a normal distribution as assumed by this method.

+ + error_model_algorithm +

If the error model is "const", the error model algorithm is ignored, + because no special algorithm is needed and unweighted (also known as + ordinary) least squares fitting can be applied.

+

The default 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 leas squares optimisation step.

+

The algorithm "threestep" starts with unweighted least squares, + then optimizes only the error model using the degradation model + parameters found, and then minimizes the negative log-likelihood + with free degradation and error model parameters.

+

The algorithm "fourstep" starts with unweighted least squares, + then optimizes only the error model using the degradation model + parameters found, then optimizes the degradation model again + with fixed error model parameters, and finally minimizes the negative + log-likelihood with free degradation and error model parameters.

+

The algorithm "IRLS" starts with unweighted least squares, + and then iterates optimization of the error model parameters and subsequent + optimization of the degradation model using those error model parameters, + until the error model parameters converge.

+ + + reweight.tol +

Tolerance for the convergence criterion calculated from the error model + parameters in IRLS fits.

+ + + reweight.max.iter +

Maximum number of iterations in IRLS fits.

+ trace_parms

Should a trace of the parameter values be listed?

@@ -360,17 +397,17 @@ Per default, parameters in the kinetic models are internally transformed in

Examples

# Use shorthand notation for parent only degradation
 fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-summary(fit)
#> mkin version used for fitting:    0.9.49.4 
+summary(fit)
#> mkin version used for fitting:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Wed May  8 20:50:50 2019 
-#> Date of summary: Wed May  8 20:50:50 2019 
+#> Date of fit:     Tue Jun  4 15:01:15 2019 
+#> Date of summary: Tue Jun  4 15:01:15 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 222 model solutions performed in 0.456 s
+#> Fitted using 222 model solutions performed in 0.461 s
 #> 
 #> Error model:
 #> Constant variance 
@@ -443,7 +480,7 @@ Per default, parameters in the kinetic models are internally transformed in
   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 verstrichen 
-#>       1.488       0.000       1.488 
coef(fit)
#> NULL
endpoints(fit)
#> $ff
+#>       1.521       0.000       1.526 
coef(fit)
#> NULL
endpoints(fit)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -515,7 +552,7 @@ Per default, parameters in the kinetic models are internally transformed in
 #> Sum of squared residuals at call 126: 371.2134
 #> Sum of squared residuals at call 135: 371.2134
 #> Negative log-likelihood at call 145: 97.22429
#> Optimisation successfully terminated.
#>        User      System verstrichen 
-#>       1.086       0.000       1.087 
coef(fit.deSolve)
#> NULL
endpoints(fit.deSolve)
#> $ff
+#>       1.093       0.000       1.093 
coef(fit.deSolve)
#> NULL
endpoints(fit.deSolve)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -545,10 +582,10 @@ Per default, parameters in the kinetic models are internally transformed in
 fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data

 
# Weighted fits, including IRLS
 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:    0.9.49.4 
+                      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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Wed May  8 20:51:06 2019 
-#> Date of summary: Wed May  8 20:51:06 2019 
+#> Date of fit:     Tue Jun  4 15:01:31 2019 
+#> Date of summary: Tue Jun  4 15:01:31 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -556,7 +593,7 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 421 model solutions performed in 1.082 s
+#> Fitted using 421 model solutions performed in 1.096 s
 #> 
 #> Error model:
 #> Constant variance 
@@ -663,10 +700,10 @@ Per default, parameters in the kinetic models are internally transformed in
 #>   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:    0.9.49.4 
+#>   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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Wed May  8 20:51:08 2019 
-#> Date of summary: Wed May  8 20:51:08 2019 
+#> Date of fit:     Tue Jun  4 15:01:34 2019 
+#> Date of summary: Tue Jun  4 15:01:34 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -674,19 +711,19 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 758 model solutions performed in 1.971 s
+#> Fitted using 979 model solutions performed in 2.603 s
 #> 
 #> Error model:
 #> Variance unique to each observed variable 
 #> 
 #> 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
+#>                     value   type
+#> parent_0       100.750000  state
+#> k_parent         0.100000 deparm
+#> k_m1             0.100100 deparm
+#> f_parent_to_m1   0.500000 deparm
+#> sigma_parent     3.398909  error
+#> sigma_m1         2.857157  error
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>                     value lower upper
@@ -694,8 +731,8 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 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
+#> sigma_parent     3.398909     0   Inf
+#> sigma_m1         2.857157     0   Inf
 #> 
 #> Fixed parameter values:
 #>      value  type
@@ -715,14 +752,14 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 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    -0.041368
+#> 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        0.03925    -0.004628
+#> 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        0.024821
-#> f_parent_ilr_1  0.039255
+#> log_k_m1        0.024823
+#> f_parent_ilr_1  0.039256
 #> sigma_parent   -0.004628
 #> sigma_m1        1.000000
 #> 
@@ -786,17 +823,17 @@ Per default, parameters in the kinetic models are internally transformed in
 #>    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 -9.481e-03
+#>    50       m1    41.19  41.19948 -9.479e-03
 #>    50       m1    40.01  41.19948 -1.189e+00
-#>    75       m1    40.09  36.44036  3.650e+00
-#>    75       m1    33.85  36.44036 -2.590e+00
-#>   100       m1    31.04  31.98774 -9.477e-01
-#>   100       m1    33.13  31.98774  1.142e+00
-#>   120       m1    25.15  28.80430 -3.654e+00
-#>   120       m1    33.31  28.80430  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:    0.9.49.4 
+#>    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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Wed May  8 20:51:11 2019 
-#> Date of summary: Wed May  8 20:51:11 2019 
+#> Date of fit:     Tue Jun  4 15:01:43 2019 
+#> Date of summary: Tue Jun  4 15:01:43 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -804,28 +841,28 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 821 model solutions performed in 3.29 s
+#> Fitted using 2289 model solutions performed in 9.499 s
 #> 
 #> Error model:
 #> Two-component variance function 
 #> 
 #> 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       1.007500e+02  state
+#> k_parent       1.000000e-01 deparm
+#> k_m1           1.001000e-01 deparm
+#> f_parent_to_m1 5.000000e-01 deparm
+#> sigma_low      5.641148e-03  error
+#> rsd_high       8.430766e-02  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_low        0.100000     0   Inf
-#> rsd_high         0.100000     0   Inf
+#>                        value lower upper
+#> parent_0       100.750000000  -Inf   Inf
+#> log_k_parent    -2.302585093  -Inf   Inf
+#> log_k_m1        -2.301585593  -Inf   Inf
+#> f_parent_ilr_1   0.000000000  -Inf   Inf
+#> sigma_low        0.005641148     0   Inf
+#> rsd_high         0.084307660     0   Inf
 #> 
 #> Fixed parameter values:
 #>      value  type
@@ -856,7 +893,7 @@ Per default, parameters in the kinetic models are internally transformed in
 #>                 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 1.172e-12  0.004290 6.223e-03
+#> 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
@@ -879,18 +916,18 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Data:
 #>  time variable observed predicted   residual
-#>     0   parent    99.46 100.73433  -1.274329
-#>     0   parent   102.04 100.73433   1.305671
-#>     1   parent    93.50  91.09750   2.402495
-#>     1   parent    92.50  91.09750   1.402495
-#>     3   parent    63.23  74.50140 -11.271403
-#>     3   parent    68.99  74.50140  -5.511403
-#>     7   parent    52.32  49.82880   2.491205
-#>     7   parent    55.13  49.82880   5.301205
-#>    14   parent    27.27  24.64809   2.621909
-#>    14   parent    26.64  24.64809   1.991909
-#>    21   parent    11.50  12.19231  -0.692315
-#>    21   parent    11.64  12.19231  -0.552315
+#>     0   parent    99.46 100.73434  -1.274339
+#>     0   parent   102.04 100.73434   1.305661
+#>     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   2.491201
+#>     7   parent    55.13  49.82880   5.301201
+#>    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
@@ -899,24 +936,24 @@ Per default, parameters in the kinetic models are internally transformed in
 #>    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  -0.318668
-#>     3       m1    12.96  13.22867  -0.268668
-#>     7       m1    22.97  25.36416  -2.394164
-#>     7       m1    24.47  25.36416  -0.894164
-#>    14       m1    41.69  37.00974   4.680265
-#>    14       m1    33.21  37.00974  -3.799735
-#>    21       m1    44.37  41.90133   2.468670
-#>    21       m1    46.44  41.90133   4.538670
-#>    35       m1    41.22  43.45691  -2.236914
-#>    35       m1    37.95  43.45691  -5.506914
-#>    50       m1    41.19  41.34199  -0.151988
-#>    50       m1    40.01  41.34199  -1.331988
-#>    75       m1    40.09  36.61471   3.475290
-#>    75       m1    33.85  36.61471  -2.764710
-#>   100       m1    31.04  32.20083  -1.160830
-#>   100       m1    33.13  32.20083   0.929170
-#>   120       m1    25.15  29.04131  -3.891312
-#>   120       m1    33.31  29.04131   4.268688

+#>     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
-

Site built with pkgdown 1.3.0.9000.

+

Site built with pkgdown 1.3.0.

-- cgit v1.2.3 From b6027bbd157734e1c7f8c3ba6373451f5c85fc38 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 5 Jun 2019 15:16:59 +0200 Subject: Add error model algorithm to output --- R/mkinfit.R | 27 ++++++--- docs/articles/FOCUS_D.html | 13 +++-- docs/articles/FOCUS_L.html | 77 ++++++++++++++------------ docs/articles/mkin.html | 2 +- docs/articles/twa.html | 2 +- docs/articles/web_only/FOCUS_Z.html | 2 +- docs/articles/web_only/NAFTA_examples.html | 2 +- docs/articles/web_only/benchmarks.html | 24 ++++---- docs/articles/web_only/compiled_models.html | 12 ++-- docs/reference/Extract.mmkin.html | 8 +-- docs/reference/experimental_data_for_UBA.html | 66 +++++++++++++++++++++- docs/reference/mkinfit.html | 50 +++++++++-------- docs/reference/mkinmod.html | 2 +- docs/reference/mkinpredict.html | 6 +- docs/reference/mmkin.html | 4 +- docs/reference/summary.mkinfit.html | 11 ++-- vignettes/mkin_benchmarks.rda | Bin 874 -> 876 bytes 17 files changed, 198 insertions(+), 110 deletions(-) (limited to 'docs/reference/mkinfit.html') diff --git a/R/mkinfit.R b/R/mkinfit.R index 60697cb1..2af4e493 100644 --- a/R/mkinfit.R +++ b/R/mkinfit.R @@ -506,18 +506,23 @@ mkinfit <- function(mkinmod, observed, control = control, ...) fit$logLik <- - nlogLik.current + d_3_messages = c( + same = "Direct fitting and three-step fitting yield approximately the same likelihood", + threestep = "Three-step fitting yielded a higher likelihood than direct fitting", + direct = "Direct fitting yielded a higher likelihood than three-step fitting") if (error_model_algorithm == "d_3") { rel_diff <- abs((fit_direct$logLik - fit$logLik))/-mean(c(fit_direct$logLik, fit$logLik)) if (rel_diff < 0.0001) { - if (!quiet) { - message("Direct fitting and three-step fitting yield approximately the same likelihood") - } + if (!quiet) message(d_3_messages["same"]) + fit$d_3_message <- d_3_messages["same"] } else { if (fit$logLik > fit_direct$logLik) { - if (!quiet) message("Three-step fitting yielded a higher likelihood than direct fitting") + if (!quiet) message(d_3_messages["threestep"]) + fit$d_3_message <- d_3_messages["threestep"] } else { - if (!quiet) message("Direct fitting yielded a higher likelihood than three-step fitting") + if (!quiet) message(d_3_messages["direct"]) fit <- fit_direct + fit$d_3_message <- d_3_messages["direct"] } } } @@ -553,6 +558,7 @@ mkinfit <- function(mkinmod, observed, } } } + fit$error_model_algorithm <- error_model_algorithm # We include the error model in the parameter uncertainty analysis, also # for constant variance, to get a confidence interval for it @@ -725,6 +731,7 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05, solution_type = object$solution_type, warning = object$warning, use_of_ff = object$mkinmod$use_of_ff, + error_model_algorithm = object$error_model_algorithm, df = c(p, rdf), cov.unscaled = covar, err_mod = object$err_mod, @@ -763,8 +770,9 @@ summary.mkinfit <- function(object, data = TRUE, distimes = TRUE, alpha = 0.05, ep <- endpoints(object) if (length(ep$ff) != 0) ans$ff <- ep$ff - if(distimes) ans$distimes <- ep$distimes - if(length(ep$SFORB) != 0) ans$SFORB <- ep$SFORB + if (distimes) ans$distimes <- ep$distimes + if (length(ep$SFORB) != 0) ans$SFORB <- ep$SFORB + if (!is.null(object$d_3_message)) ans$d_3_message <- object$d_3_message class(ans) <- c("summary.mkinfit", "summary.modFit") return(ans) } @@ -794,12 +802,15 @@ print.summary.mkinfit <- function(x, digits = max(3, getOption("digits") - 3), . cat("\nFitted using", x$calls, "model solutions performed in", x$time[["elapsed"]], "s\n") - cat("\nError model:\n") + cat("\nError model: ") cat(switch(x$err_mod, const = "Constant variance", obs = "Variance unique to each observed variable", tc = "Two-component variance function"), "\n") + cat("\nError model algorithm:", x$error_model_algorithm, "\n") + if (!is.null(x$d_3_message)) cat(x$d_3_message, "\n") + cat("\nStarting values for parameters to be optimised:\n") print(x$start) diff --git a/docs/articles/FOCUS_D.html b/docs/articles/FOCUS_D.html index 698ccebc..be1f40f5 100644 --- a/docs/articles/FOCUS_D.html +++ b/docs/articles/FOCUS_D.html @@ -88,7 +88,7 @@

Example evaluation of FOCUS Example Dataset D

Johannes Ranke

-

2019-06-04

+

2019-06-05

@@ -168,8 +168,8 @@ 
summary(fit)
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:18 2019 
-## Date of summary: Tue Jun  4 15:03:18 2019 
+## Date of fit:     Wed Jun  5 15:10:49 2019 
+## Date of summary: Wed Jun  5 15:10:50 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent - k_parent_m1 * parent
@@ -177,10 +177,11 @@
 ## 
 ## Model predictions using solution type deSolve 
 ## 
-## Fitted using 389 model solutions performed in 0.978 s
+## Fitted using 389 model solutions performed in 0.984 s
 ## 
-## Error model:
-## Constant variance 
+## Error model: Constant variance 
+## 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##                    value   type
diff --git a/docs/articles/FOCUS_L.html b/docs/articles/FOCUS_L.html
index 75b2cf10..4388edd2 100644
--- a/docs/articles/FOCUS_L.html
+++ b/docs/articles/FOCUS_L.html
@@ -88,7 +88,7 @@
       
Example evaluation of FOCUS Laboratory Data L1 to L3

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
@@ -114,18 +114,19 @@
 summary(m.L1.SFO)
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:20 2019 
-## Date of summary: Tue Jun  4 15:03:20 2019 
+## Date of fit:     Wed Jun  5 15:10:52 2019 
+## Date of summary: Wed Jun  5 15:10:52 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 133 model solutions performed in 0.28 s
+## Fitted using 133 model solutions performed in 0.277 s
 ## 
-## Error model:
-## Constant variance 
+## Error model: Constant variance 
+## 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##                   value   type
@@ -215,8 +216,8 @@
 ## finite result is doubtful
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:22 2019 
-## Date of summary: Tue Jun  4 15:03:22 2019 
+## Date of fit:     Wed Jun  5 15:10:54 2019 
+## Date of summary: Wed Jun  5 15:10:54 2019 
 ## 
 ## 
 ## Warning: Optimisation did not converge:
@@ -228,10 +229,11 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 899 model solutions performed in 1.868 s
+## Fitted using 899 model solutions performed in 1.877 s
+## 
+## Error model: Constant variance 
 ## 
-## Error model:
-## Constant variance 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -319,18 +321,19 @@
 
summary(m.L2.FOMC, data = FALSE)

 
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:23 2019 
-## Date of summary: Tue Jun  4 15:03:23 2019 
+## Date of fit:     Wed Jun  5 15:10:55 2019 
+## Date of summary: Wed Jun  5 15:10:55 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 239 model solutions performed in 0.484 s
+## Fitted using 239 model solutions performed in 0.492 s
 ## 
-## Error model:
-## Constant variance 
+## Error model: Constant variance 
+## 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -394,8 +397,8 @@
 
summary(m.L2.DFOP, data = FALSE)

 
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:25 2019 
-## Date of summary: Tue Jun  4 15:03:25 2019 
+## Date of fit:     Wed Jun  5 15:10:56 2019 
+## Date of summary: Wed Jun  5 15:10:56 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -404,10 +407,11 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 572 model solutions performed in 1.193 s
+## Fitted using 572 model solutions performed in 1.183 s
+## 
+## Error model: Constant variance 
 ## 
-## Error model:
-## Constant variance 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -493,8 +497,8 @@
 
summary(mm.L3[["DFOP", 1]])

 
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:26 2019 
-## Date of summary: Tue Jun  4 15:03:27 2019 
+## Date of fit:     Wed Jun  5 15:10:58 2019 
+## Date of summary: Wed Jun  5 15:10:58 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - ((k1 * g * exp(-k1 * time) + k2 * (1 - g) *
@@ -503,10 +507,11 @@
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 373 model solutions performed in 0.767 s
+## Fitted using 373 model solutions performed in 0.773 s
 ## 
-## Error model:
-## Constant variance 
+## Error model: Constant variance 
+## 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
@@ -598,8 +603,8 @@
 
summary(mm.L4[["SFO", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:27 2019 
-## Date of summary: Tue Jun  4 15:03:28 2019 
+## Date of fit:     Wed Jun  5 15:10:59 2019 
+## Date of summary: Wed Jun  5 15:10:59 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - k_parent_sink * parent
@@ -608,8 +613,9 @@
 ## 
 ## Fitted using 142 model solutions performed in 0.288 s
 ## 
-## Error model:
-## Constant variance 
+## Error model: Constant variance 
+## 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##                  value   type
@@ -662,18 +668,19 @@
 
summary(mm.L4[["FOMC", 1]], data = FALSE)

 
## mkin version used for fitting:    0.9.49.5 
 ## R version used for fitting:       3.6.0 
-## Date of fit:     Tue Jun  4 15:03:28 2019 
-## Date of summary: Tue Jun  4 15:03:28 2019 
+## Date of fit:     Wed Jun  5 15:10:59 2019 
+## Date of summary: Wed Jun  5 15:10:59 2019 
 ## 
 ## Equations:
 ## d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 ## 
 ## Model predictions using solution type analytical 
 ## 
-## Fitted using 224 model solutions performed in 0.449 s
+## Fitted using 224 model solutions performed in 0.453 s
+## 
+## Error model: Constant variance 
 ## 
-## Error model:
-## Constant variance 
+## Error model algorithm: d_3 
 ## 
 ## Starting values for parameters to be optimised:
 ##              value   type
diff --git a/docs/articles/mkin.html b/docs/articles/mkin.html
index 09b1b8ea..5977d642 100644
--- a/docs/articles/mkin.html
+++ b/docs/articles/mkin.html
@@ -88,7 +88,7 @@
       
Introduction to mkin

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
diff --git a/docs/articles/twa.html b/docs/articles/twa.html
index ede27942..f98026e7 100644
--- a/docs/articles/twa.html
+++ b/docs/articles/twa.html
@@ -88,7 +88,7 @@
       
Calculation of time weighted average concentrations with mkin

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
diff --git a/docs/articles/web_only/FOCUS_Z.html b/docs/articles/web_only/FOCUS_Z.html
index 555400df..542deb14 100644
--- a/docs/articles/web_only/FOCUS_Z.html
+++ b/docs/articles/web_only/FOCUS_Z.html
@@ -88,7 +88,7 @@
       
Example evaluation of FOCUS dataset Z

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
diff --git a/docs/articles/web_only/NAFTA_examples.html b/docs/articles/web_only/NAFTA_examples.html
index 7f3cc76b..4cd16437 100644
--- a/docs/articles/web_only/NAFTA_examples.html
+++ b/docs/articles/web_only/NAFTA_examples.html
@@ -88,7 +88,7 @@
       
Evaluation of example datasets from Attachment 1 to the US EPA SOP for the NAFTA guidance

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
diff --git a/docs/articles/web_only/benchmarks.html b/docs/articles/web_only/benchmarks.html
index 43f8d238..507bdb61 100644
--- a/docs/articles/web_only/benchmarks.html
+++ b/docs/articles/web_only/benchmarks.html
@@ -88,7 +88,7 @@
       
Benchmark timings for mkin on various systems

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
@@ -202,77 +202,77 @@
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 7.064
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 7.296
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 5.936
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 5.805
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 5.828
 ##                                                                         t2
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 11.019
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 22.889
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 12.558
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 21.239
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 20.545
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 35.748
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 35.869
 ##                                                                        t3
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 3.764
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.649
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.786
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.510
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.446
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 4.403
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 4.412
 ##                                                                         t4
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 14.347
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 13.789
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  8.461
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 13.805
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 15.335
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 30.613
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 30.497
 ##                                                                         t5
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1  9.495
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  6.395
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.675
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.386
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  6.002
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 10.309
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 10.329
 ##                                                                        t6
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 2.623
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 2.542
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 2.723
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 2.643
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 2.635
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 2.546
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 2.548
 ##                                                                        t7
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 4.587
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.128
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.478
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 4.374
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.259
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 4.214
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 4.192
 ##                                                                        t8
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 7.525
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 4.632
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 4.862
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.02
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 4.737
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 7.871
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 7.827
 ##                                                                         t9
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 16.621
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  8.171
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  7.618
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 11.124
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  7.763
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 15.738
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 15.653
 ##                                                                       t10
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 8.576
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1 3.676
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2 3.579
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3 5.388
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4 3.427
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 7.763
+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 7.762
 ##                                                                        t11
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.48.1 31.267
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.1  5.636
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.2  5.574
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.3  7.365
 ## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.4  5.626
-## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 10.527

+## Linux, AMD Ryzen 7 1700 Eight-Core Processor, mkin version 0.9.49.5 10.512

 
save(mkin_benchmarks, file = "~/git/mkin/vignettes/mkin_benchmarks.rda")

 
   
diff --git a/docs/articles/web_only/compiled_models.html b/docs/articles/web_only/compiled_models.html
index f3062c66..5f0301fc 100644
--- a/docs/articles/web_only/compiled_models.html
+++ b/docs/articles/web_only/compiled_models.html
@@ -88,7 +88,7 @@
       
Performance benefit by using compiled model definitions in mkin

                         
Johannes Ranke

             
-            
2019-06-04

+            
2019-06-05

       
       
       
@@ -163,9 +163,9 @@
 ## Warning in mkinfit(SFO_SFO, FOCUS_2006_D, solution_type = "deSolve", quiet
 ## = TRUE): Observations with value of zero were removed from the data

 
##                    test replications elapsed relative user.self sys.self
-## 3     deSolve, compiled            3   3.053    1.000     3.052        0
-## 1 deSolve, not compiled            3  28.457    9.321    28.442        0
-## 2      Eigenvalue based            3   4.296    1.407     4.293        0
+## 3     deSolve, compiled            3   3.041    1.000     3.039        0
+## 1 deSolve, not compiled            3  28.429    9.349    28.415        0
+## 2      Eigenvalue based            3   4.291    1.411     4.288        0
 ##   user.child sys.child
 ## 3          0         0
 ## 1          0         0
@@ -214,8 +214,8 @@
 ## Warning in mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE): Observations with
 ## value of zero were removed from the data

 
##                    test replications elapsed relative user.self sys.self
-## 2     deSolve, compiled            3   4.828    1.000     4.825        0
-## 1 deSolve, not compiled            3  53.153   11.009    53.125        0
+## 2     deSolve, compiled            3   4.927    1.000     4.924        0
+## 1 deSolve, not compiled            3  53.138   10.785    53.108        0
 ##   user.child sys.child
 ## 2          0         0
 ## 1          0         0

diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html
index 1d1da50e..37c0b361 100644
--- a/docs/reference/Extract.mmkin.html
+++ b/docs/reference/Extract.mmkin.html
@@ -172,16 +172,16 @@
                 cores = 1, quiet = TRUE)
   fits["FOMC", ]
#>       dataset
 #> model  B       C      
-#>   FOMC List,36 List,36
+#>   FOMC List,37 List,37
 #> attr(,"class")
 #> [1] "mmkin"
  fits[, "B"]
#>       dataset
 #> model  B      
-#>   SFO  List,36
-#>   FOMC List,36
+#>   SFO  List,37
+#>   FOMC List,37
 #> attr(,"class")
 #> [1] "mmkin"
  fits["SFO", "B"]
#>      dataset
 #> model B      
-#>   SFO List,36
+#>   SFO List,37
 #> attr(,"class")
 #> [1] "mmkin"

   head(
diff --git a/docs/reference/experimental_data_for_UBA.html b/docs/reference/experimental_data_for_UBA.html
index 3e8d20ef..bb94fd11 100644
--- a/docs/reference/experimental_data_for_UBA.html
+++ b/docs/reference/experimental_data_for_UBA.html
@@ -37,7 +37,30 @@
  and advance error model specifications. The fact that these data and some
  results are shown here do not imply a license to use them in the context of
  pesticide registrations, as the use of the data may be constrained by
- data protection regulations." />
+ data protection regulations.
+Preprocessing of data was performed based on the recommendations of the FOCUS
+ kinetics workgroup (FOCUS, 2014) as described below.
+Datasets 1 and 2 are from the Renewal Assessment Report (RAR) for imazamox
+ (France, 2015, p. 15). For setting values reported as zero, an LOQ of 0.1
+ was assumed. Metabolite residues reported for day zero were added to the
+ parent compound residues.
+Datasets 3 and 4 are from the Renewal Assessment Report (RAR) for isofetamid
+ (Belgium, 2014, p. 8) and show the data for two different radiolabels. For
+ dataset 4, the value given for the metabolite in the day zero sampling
+ in replicate B was added to the parent compound, following the respective
+ FOCUS recommendation.
+Dataset 5 is from the Renewal Assessment Report (RAR) for ethofumesate
+ (Austria, 2015, p. 16).
+Datasets 6 to 10 are from the Renewal Assessment Report (RAR) for glyphosate
+ (Germany, 2013a, pages 8, 28, 50, 51). For the initial sampling,
+ the residues given for the metabolite were added to the parent
+ value, following the recommendation of the FOCUS kinetics workgroup.
+Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
+ (Germany, 2013b, p. 644). Values reported as zero were set to NA, with
+ the exception of the day three sampling of metabolite A2, which was set
+ to one half of the LOD reported to be 1% AR.
+Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
+ (United Kingdom, 2014, p. 81)." />
 
 
 
@@ -139,6 +162,29 @@
  results are shown here do not imply a license to use them in the context of
  pesticide registrations, as the use of the data may be constrained by
  data protection regulations.

+
Preprocessing of data was performed based on the recommendations of the FOCUS
+ kinetics workgroup (FOCUS, 2014) as described below.

+
Datasets 1 and 2 are from the Renewal Assessment Report (RAR) for imazamox
+ (France, 2015, p. 15). For setting values reported as zero, an LOQ of 0.1
+ was assumed. Metabolite residues reported for day zero were added to the
+ parent compound residues.

+
Datasets 3 and 4 are from the Renewal Assessment Report (RAR) for isofetamid
+ (Belgium, 2014, p. 8) and show the data for two different radiolabels. For
+ dataset 4, the value given for the metabolite in the day zero sampling
+ in replicate B was added to the parent compound, following the respective
+ FOCUS recommendation.

+
Dataset 5 is from the Renewal Assessment Report (RAR) for ethofumesate
+ (Austria, 2015, p. 16).

+
Datasets 6 to 10 are from the Renewal Assessment Report (RAR) for glyphosate
+ (Germany, 2013a, pages 8, 28, 50, 51). For the initial sampling,
+ the residues given for the metabolite were added to the parent
+ value, following the recommendation of the FOCUS kinetics workgroup.

+
Dataset 11 is from the Renewal Assessment Report (RAR) for 2,4-D
+ (Germany, 2013b, p. 644). Values reported as zero were set to NA, with
+ the exception of the day three sampling of metabolite A2, which was set
+ to one half of the LOD reported to be 1% AR.

+
Dataset 12 is from the Renewal Assessment Report (RAR) for thifensulfuron-methyl
+ (United Kingdom, 2014, p. 81).

     
     

 
@@ -154,8 +200,24 @@
     
     
Source

 
-    
Ranke (2019) Documentation of results obtained for the error model expertise
+    
+    
Austria (2015). Ethofumesate Renewal Assessment Report Volume 3 Annex B.8 (AS)

+
Belgium (2014). Isofetamid (IKF-5411) Draft Assessment Report Volume 3 Annex B.8 (AS)

+
France (2015). Imazamox Draft Renewal Assessment Report Volume 3 Annex B.8 (AS)

+
FOCUS (2014) “Generic guidance for Estimating Persistence and
+  Degradation Kinetics from Environmental Fate Studies on Pesticides in EU
+  Registration” Report of the FOCUS Work Group on Degradation Kinetics,
+  Version 1.1, 18 December 2014
+  http://esdac.jrc.ec.europa.eu/projects/degradation-kinetics

+
Germany (2013a). Renewal Assessment Report Glyphosate Volume 3 Annex B.8: Environmental Fate
+  and Behaviour

+
Germany (2013b). Renewal Assessment Report 2,4-D Volume 3 Annex B.8: Fate and behaviour in the
+  environment

+
Ranke (2019) Documentation of results obtained for the error model expertise
   written for the German Umweltbundesamt.

+
United Kingdom (2014). Thifensulfuron-methyl - Annex B.8 (Volume 3) to the Report and Proposed
+  Decision of the United Kingdom made to the European Commission under Regulation (EC) No.
+  1141/2010 for renewal of an active substance

     
 
     
Examples

diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 8cabcb21..700b6805 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -399,18 +399,19 @@ Per default, parameters in the kinetic models are internally transformed in
 fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
 summary(fit)
#> mkin version used for fitting:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Tue Jun  4 15:01:15 2019 
-#> Date of summary: Tue Jun  4 15:01:15 2019 
+#> Date of fit:     Wed Jun  5 15:08:20 2019 
+#> Date of summary: Wed Jun  5 15:08:20 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 222 model solutions performed in 0.461 s
+#> Fitted using 222 model solutions performed in 0.469 s
 #> 
-#> Error model:
-#> Constant variance 
+#> Error model: Constant variance 
+#> 
+#> Error model algorithm: d_3 
 #> 
 #> Starting values for parameters to be optimised:
 #>              value   type
@@ -480,7 +481,7 @@ Per default, parameters in the kinetic models are internally transformed in
   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 verstrichen 
-#>       1.521       0.000       1.526 
coef(fit)
#> NULL
endpoints(fit)
#> $ff
+#>       1.579       0.000       1.581 
coef(fit)
#> NULL
endpoints(fit)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -552,7 +553,7 @@ Per default, parameters in the kinetic models are internally transformed in
 #> Sum of squared residuals at call 126: 371.2134
 #> Sum of squared residuals at call 135: 371.2134
 #> Negative log-likelihood at call 145: 97.22429
#> Optimisation successfully terminated.
#>        User      System verstrichen 
-#>       1.093       0.000       1.093 
coef(fit.deSolve)
#> NULL
endpoints(fit.deSolve)
#> $ff
+#>       1.159       0.000       1.160 
coef(fit.deSolve)
#> NULL
endpoints(fit.deSolve)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -584,8 +585,8 @@ Per default, parameters in the kinetic models are internally transformed in
 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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Tue Jun  4 15:01:31 2019 
-#> Date of summary: Tue Jun  4 15:01:31 2019 
+#> Date of fit:     Wed Jun  5 15:08:36 2019 
+#> Date of summary: Wed Jun  5 15:08:36 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -593,10 +594,11 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 421 model solutions performed in 1.096 s
+#> Fitted using 421 model solutions performed in 1.181 s
+#> 
+#> Error model: Constant variance 
 #> 
-#> Error model:
-#> Constant variance 
+#> Error model algorithm: d_3 
 #> 
 #> Starting values for parameters to be optimised:
 #>                     value   type
@@ -702,8 +704,8 @@ Per default, parameters in the kinetic models are internally transformed in
 #>   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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Tue Jun  4 15:01:34 2019 
-#> Date of summary: Tue Jun  4 15:01:34 2019 
+#> Date of fit:     Wed Jun  5 15:08:39 2019 
+#> Date of summary: Wed Jun  5 15:08:39 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -711,10 +713,12 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 979 model solutions performed in 2.603 s
+#> Fitted using 979 model solutions performed in 2.672 s
 #> 
-#> Error model:
-#> Variance unique to each observed variable 
+#> 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
@@ -832,8 +836,8 @@ Per default, parameters in the kinetic models are internally transformed in
 #>   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:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Tue Jun  4 15:01:43 2019 
-#> Date of summary: Tue Jun  4 15:01:43 2019 
+#> Date of fit:     Wed Jun  5 15:08:50 2019 
+#> Date of summary: Wed Jun  5 15:08:50 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -841,10 +845,12 @@ Per default, parameters in the kinetic models are internally transformed in
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted using 2289 model solutions performed in 9.499 s
+#> Fitted using 2289 model solutions performed in 10.959 s
+#> 
+#> Error model: Two-component variance function 
 #> 
-#> Error model:
-#> Two-component variance function 
+#> 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
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 2c5f056e..ddab656c 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -234,7 +234,7 @@ For the definition of model types and their parameters, the equations given
 SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
   m1 = mkinsub("SFO"), verbose = TRUE)
#> Compilation argument:
-#>  /usr/lib/R/bin/R CMD SHLIB filebbe6b539c4f.c 2> filebbe6b539c4f.c.err.txt 
+#>  /usr/lib/R/bin/R CMD SHLIB file50a55c3108b2.c 2> file50a55c3108b2.c.err.txt 
 #> Program source:
 #>   1: #include <R.h>
 #>   2: 
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html
index f339b26a..7e71ec27 100644
--- a/docs/reference/mkinpredict.html
+++ b/docs/reference/mkinpredict.html
@@ -328,17 +328,17 @@
                 c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
                 solution_type = "eigen")[201,]))
#>     time   parent       m1
 #> 201   20 4.978707 27.46227
#>        User      System verstrichen 
-#>       0.003       0.000       0.003 
  system.time(
+#>       0.004       0.000       0.004 
  system.time(
     print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
                 c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
                 solution_type = "deSolve")[201,]))
#>     time   parent       m1
 #> 201   20 4.978707 27.46227
#>        User      System verstrichen 
-#>       0.002       0.000       0.002 
  system.time(
+#>       0.003       0.000       0.002 
  system.time(
     print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01),
                 c(parent = 100, m1 = 0), seq(0, 20, by = 0.1),
                 solution_type = "deSolve", use_compiled = FALSE)[201,]))
#>     time   parent       m1
 #> 201   20 4.978707 27.46227
#>        User      System verstrichen 
-#>       0.021       0.000       0.022 

+#>       0.022       0.000       0.022 

   
    # Predict from a fitted model
     f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 552.5739
 #> Sum of squared residuals at call 3: 552.5739
diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index a830646f..61a029fc 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -194,8 +194,8 @@
 time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
 
 time_default
#>        User      System verstrichen 
-#>       0.046       0.032       5.094 
time_1
#>        User      System verstrichen 
-#>      19.798       0.004      19.814 

+#>       0.038       0.050       6.779 
time_1
#>        User      System verstrichen 
+#>      27.209       0.004      27.278 

 endpoints(fits.0[["SFO_lin", 2]])
#> $ff
 #>   parent_M1 parent_sink       M1_M2     M1_sink 
 #>   0.7340481   0.2659519   0.7505684   0.2494316 
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index ffe1edb1..68b36542 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -211,18 +211,19 @@
     
Examples

     
  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.49.5 
 #> R version used for fitting:       3.6.0 
-#> Date of fit:     Tue Jun  4 15:03:02 2019 
-#> Date of summary: Tue Jun  4 15:03:02 2019 
+#> Date of fit:     Wed Jun  5 15:10:34 2019 
+#> Date of summary: Wed Jun  5 15:10:34 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent_sink * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 131 model solutions performed in 0.266 s
+#> Fitted using 131 model solutions performed in 0.268 s
 #> 
-#> Error model:
-#> Constant variance 
+#> Error model: Constant variance 
+#> 
+#> Error model algorithm: d_3 
 #> 
 #> Starting values for parameters to be optimised:
 #>                    value   type
diff --git a/vignettes/mkin_benchmarks.rda b/vignettes/mkin_benchmarks.rda
index 3e160a06..17cf86ce 100644
Binary files a/vignettes/mkin_benchmarks.rda and b/vignettes/mkin_benchmarks.rda differ
-- 
cgit v1.2.3