Calculate the AIC for a column of an mmkin object

From 9ac853c7ceece333099021974025d07e75be2b33 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Tue, 12 May 2020 08:07:07 +0200 Subject: Documentation improvements, rebuild static docs --- docs/reference/AIC.mmkin.html | 58 ++++++------ docs/reference/DFOP.solution.html | 18 ++-- docs/reference/FOMC.solution.html | 26 +++--- docs/reference/HS.solution.html | 18 ++-- docs/reference/IORE.solution.html | 10 +- docs/reference/SFO.solution.html | 18 ++-- docs/reference/SFORB.solution.html | 20 ++-- docs/reference/add_err-1.png | Bin 89498 -> 111278 bytes docs/reference/add_err-2.png | Bin 50582 -> 63031 bytes docs/reference/add_err-3.png | Bin 52523 -> 60608 bytes docs/reference/add_err.html | 59 ++++++------ docs/reference/confint.mkinfit.html | 164 +++++++++++++++++++-------------- docs/reference/endpoints.html | 57 ++++++------ docs/reference/get_deg_func.html | 48 +++++----- docs/reference/ilr.html | 60 ++++++------ docs/reference/logLik.mkinfit.html | 58 ++++++------ docs/reference/logistic.solution.html | 20 ++-- docs/reference/max_twa_parent.html | 63 ++++++------- docs/reference/mkinerrplot-1.png | Bin 35440 -> 41458 bytes docs/reference/mkinerrplot.html | 57 ++++++------ docs/reference/mkinfit.html | 133 +++++++++++++------------- docs/reference/mkinmod.html | 37 ++++---- docs/reference/mkinparplot-1.png | Bin 16467 -> 16468 bytes docs/reference/mkinparplot.html | 52 ++++++----- docs/reference/mkinresplot-1.png | Bin 14863 -> 14861 bytes docs/reference/mkinresplot.html | 59 ++++++------ docs/reference/mmkin-1.png | Bin 97412 -> 115683 bytes docs/reference/mmkin-2.png | Bin 88002 -> 113464 bytes docs/reference/mmkin-3.png | Bin 85375 -> 100799 bytes docs/reference/mmkin-4.png | Bin 62550 -> 70430 bytes docs/reference/mmkin-5.png | Bin 57465 -> 66958 bytes docs/reference/mmkin.html | 79 ++++++++-------- docs/reference/nafta.html | 73 +++++++-------- docs/reference/nlme.html | 8 +- docs/reference/nlme.mmkin-1.png | Bin 69863 -> 81816 bytes docs/reference/nlme.mmkin-2.png | Bin 70426 -> 82384 bytes docs/reference/nlme.mmkin-3.png | Bin 70519 -> 82591 bytes docs/reference/nlme.mmkin-4.png | Bin 72803 -> 86006 bytes docs/reference/nlme.mmkin-5.png | Bin 72587 -> 85418 bytes docs/reference/nlme.mmkin-6.png | Bin 71892 -> 84796 bytes docs/reference/nlme.mmkin-7.png | Bin 72316 -> 84926 bytes docs/reference/nlme.mmkin.html | 152 ++++++++++++++---------------- docs/reference/plot.mkinfit.html | 4 +- docs/reference/plot.mmkin.html | 61 ++++++------ docs/reference/plot.nlme.mmkin-2.png | Bin 35313 -> 35346 bytes docs/reference/plot.nlme.mmkin.html | 58 ++++++------ docs/reference/sigma_twocomp.html | 55 +++++------ docs/reference/summary.mkinfit.html | 156 +++++++++++++++---------------- docs/reference/transform_odeparms.html | 95 +++++++++---------- 49 files changed, 906 insertions(+), 870 deletions(-) (limited to 'docs/reference') diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 6b782456..b91d40e3 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ same dataset." /> - +

@@ -114,7 +117,12 @@ same dataset." />

@@ -129,7 +137,7 @@ same dataset." />

@@ -165,7 +173,7 @@ column.

Value

As in the generic method (a numeric value for single fits, or a - dataframe if there are several fits in the column).

+dataframe if there are several fits in the column).

Examples


@@ -181,13 +189,13 @@ column.
   # of parameters, the higher (worse) the AIC
   AIC(f[, "FOCUS A"])
#>      df      AIC
 #> SFO   3 55.28197
-#> FOMC  4 57.28202
+#> FOMC  4 57.28211
 #> DFOP  5 59.28197
  AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
#>      df      AIC
 #> SFO   3 49.28197
-#> FOMC  4 49.28202
+#> FOMC  4 49.28211
 #> DFOP  5 49.28197
  BIC(f[, "FOCUS A"])        # Comparing the BIC gives a very similar picture
#>      df      BIC
 #> SFO   3 55.52030
-#> FOMC  4 57.59979
+#> FOMC  4 57.59987
 #> DFOP  5 59.67918

   # For FOCUS C, the more complex models fit better
   AIC(f[, "FOCUS C"])
#>      df      AIC
@@ -200,16 +208,10 @@ column.

- @@ -220,7 +222,7 @@ column.

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diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 84e33f9e..e0094738 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> @@ -180,15 +180,15 @@ kinetic constant.

References

FOCUS (2006) “Guidance Document on Estimating Persistence - 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 +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 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

+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

Note

The solution of the FOMC kinetic model reduces to the - SFO.solution for large values of alpha and - beta with \(k = \frac{\beta}{\alpha}\).

+SFO.solution for large values of alpha and +beta with \(k = \frac{\beta}{\alpha}\).

References

+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

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

Gustafson DI and Holden LR (1990) Nonlinear pesticide dissipation in soil: - A new model based on spatial variability. Environmental Science and - Technology 24, 1032-1038

+A new model based on spatial variability. Environmental Science and +Technology 24, 1032-1038

References

Note

The solution of the IORE kinetic model reduces to the - SFO.solution if N = 1. The parameters of the IORE model can - be transformed to equivalent parameters of the FOMC mode - see the NAFTA - guidance for details.

+SFO.solution if N = 1. The parameters of the IORE model can +be transformed to equivalent parameters of the FOMC mode - see the NAFTA +guidance for details.

References

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

+for Evaluating and Calculating Degradation Kinetics in Environmental Media

References

Value

The value of the response variable, which is the sum of free and - bound fractions at time t.

+bound fractions at time t.

References

Value

A list of datasets compatible with mmkin, i.e. the - components of the list are datasets compatible with mkinfit.

+components of the list are datasets compatible with mkinfit.

References

Ranke J and Lehmann R (2015) To t-test or not to t-test, that is @@ -236,28 +244,21 @@ http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

d_SFO_SFO_err, cores = 1, quiet = TRUE) -plot(f_SFO_SFO)

+plot(f_SFO_SFO)

# We would like to inspect the fit for dataset 3 more closely # Using double brackets makes the returned object an mkinfit object # instead of a list of mkinfit objects, so plot.mkinfit is used -plot(f_SFO_SFO[[3]], show_residuals = TRUE)

+plot(f_SFO_SFO[[3]], show_residuals = TRUE)

# If we use single brackets, we should give two indices (model and dataset), # and plot.mmkin is used -plot(f_SFO_SFO[1, 3])

# } +plot(f_SFO_SFO[1, 3])

# }

- @@ -268,7 +269,7 @@ http://chem.uft.uni-bremen.de/ranke/posters/piacenza_2015.pdf

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diff --git a/docs/reference/confint.mkinfit.html b/docs/reference/confint.mkinfit.html index 1dd6288b..7658a572 100644 --- a/docs/reference/confint.mkinfit.html +++ b/docs/reference/confint.mkinfit.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -44,7 +48,6 @@ parameter. The 'profile' method uses two nested optimisations and can take a very long time, even if parallelized by specifying 'cores' on unixoid platforms. The speed of the method could likely be improved by using the method of Venzon and Moolgavkar (1988)." /> - @@ -62,7 +65,7 @@ method of Venzon and Moolgavkar (1988)." /> - +

@@ -120,7 +123,12 @@ method of Venzon and Moolgavkar (1988)." />

@@ -135,7 +143,7 @@ method of Venzon and Moolgavkar (1988)." />

@@ -212,7 +220,7 @@ their confidence intervals?

cores -

The number of cores to be used for multicore processing. +

The number of cores to be used for multicore processing. On Windows machines, cores > 1 is currently not supported.

@@ -228,15 +236,15 @@ On Windows machines, cores > 1 is currently not supported.

Value

A matrix with columns giving lower and upper confidence limits for - each parameter.

+each parameter.

References

Bates DM and Watts GW (1988) Nonlinear regression analysis & its applications

Pawitan Y (2013) In all likelihood - Statistical modelling and - inference using likelihood. Clarendon Press, Oxford.

+inference using likelihood. Clarendon Press, Oxford.

Venzon DJ and Moolgavkar SH (1988) A Method for Computing - Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, - 87–94.

+Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37, +87–94.

Examples

f <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
@@ -262,50 +270,71 @@ On Windows machines, cores > 1 is currently not supported.
 SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"),
   use_of_ff = "max", quiet = TRUE)
 f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
-system.time(ci_profile <- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))
#> Error in mkinpredict.mkinmod(mkinmod, parms, odeini, outtimes, solution_type = solution_type,     use_compiled = use_compiled, method.ode = method.ode, atol = atol,     rtol = rtol, ...): Differential equations were not integrated for all output times because
-#> NaN values occurred in output from ode()
#> Timing stopped at: 11.39 0 11.4
# Using more cores does not save much time here, as parent_0 takes up most of the time
+system.time(ci_profile <- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))
#>        User      System verstrichen 
+#>       3.362       0.000       3.364 
# Using more cores does not save much time here, as parent_0 takes up most of the time
 # If we additionally exclude parent_0 (the confidence of which is often of
 # minor interest), we get a nice performance improvement from about 50
 # seconds to about 12 seconds if we use at least four cores
 system.time(ci_profile_no_parent_0 <- confint(f_d_1, method = "profile",
-  c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
#> Profiling the likelihood
#>        User      System verstrichen 
-#>      20.058       0.122      11.784 
ci_profile
#> Error in eval(expr, envir, enclos): Objekt 'ci_profile' nicht gefunden
ci_quadratic_transformed <- confint(f_d_1, method = "quadratic")
-ci_quadratic_transformed
#>                       2.5%        97.5%
-#> parent_0      96.403841641 1.027931e+02
-#> k_parent_sink  0.041033378 5.596269e-02
-#> k_parent_m1    0.046777902 5.511931e-02
-#> k_m1_sink      0.004012217 6.897547e-03
-#> sigma          2.396089689 3.854918e+00
ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE)
-ci_quadratic_untransformed
#>                       2.5%        97.5%
-#> parent_0      96.403841648 102.79312450
-#> k_parent_sink  0.040485331   0.05535491
-#> k_parent_m1    0.046611582   0.05494364
-#> k_m1_sink      0.003835483   0.00668582
-#> sigma          2.396089689   3.85491806
# Against the expectation based on Bates and Watts (1988), the confidence
+  c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
#> Profiling the likelihood
#> Warning: scheduled cores 1, 2, 3 encountered errors in user code, all values of the jobs will be affected
#> Error in dimnames(x) <- dn: Länge von 'dimnames' [2] ungleich der Arrayausdehnung
#> Timing stopped at: 0.011 0.045 0.201
ci_profile
#>                        2.5%        97.5%
+#> parent_0       96.456003640 1.027703e+02
+#> k_parent        0.090911032 1.071578e-01
+#> k_m1            0.003892605 6.702778e-03
+#> f_parent_to_m1  0.471328495 5.611550e-01
+#> sigma           2.535612399 3.985263e+00
ci_quadratic_transformed <- confint(f_d_1, method = "quadratic")
+ci_quadratic_transformed
#>                        2.5%        97.5%
+#> parent_0       96.403839460 1.027931e+02
+#> k_parent        0.090823790 1.072543e-01
+#> k_m1            0.004012216 6.897547e-03
+#> f_parent_to_m1  0.469118713 5.595960e-01
+#> sigma           2.396089689 3.854918e+00
ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE)
+ci_quadratic_untransformed
#>                        2.5%        97.5%
+#> parent_0       96.403839413 1.027931e+02
+#> k_parent        0.090491931 1.069035e-01
+#> k_m1            0.003835483 6.685819e-03
+#> f_parent_to_m1  0.469113365 5.598386e-01
+#> sigma           2.396089689 3.854918e+00
# Against the expectation based on Bates and Watts (1988), the confidence
 # intervals based on the internal parameter transformation are less
 # congruent with the likelihood based intervals. Note the superiority of the
 # interval based on the untransformed fit for k_m1_sink
-rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile)
#> Error in eval(expr, envir, enclos): Objekt 'ci_profile' nicht gefunden
rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile)
#> Error in eval(expr, envir, enclos): Objekt 'ci_profile' nicht gefunden
rel_diffs_transformed < rel_diffs_untransformed
#> Error in eval(expr, envir, enclos): Objekt 'rel_diffs_transformed' nicht gefunden
signif(rel_diffs_transformed, 3)
#> Error in eval(expr, envir, enclos): Objekt 'rel_diffs_transformed' nicht gefunden
signif(rel_diffs_untransformed, 3)
#> Error in eval(expr, envir, enclos): Objekt 'rel_diffs_untransformed' nicht gefunden

+rel_diffs_transformed <- abs((ci_quadratic_transformed - ci_profile)/ci_profile)
+rel_diffs_untransformed <- abs((ci_quadratic_untransformed - ci_profile)/ci_profile)
+rel_diffs_transformed < rel_diffs_untransformed
#>                 2.5% 97.5%
+#> parent_0        TRUE  TRUE
+#> k_parent        TRUE  TRUE
+#> k_m1           FALSE FALSE
+#> f_parent_to_m1  TRUE FALSE
+#> sigma          FALSE FALSE
signif(rel_diffs_transformed, 3)
#>                    2.5%    97.5%
+#> parent_0       0.000541 0.000222
+#> k_parent       0.000960 0.000900
+#> k_m1           0.030700 0.029100
+#> f_parent_to_m1 0.004690 0.002780
+#> sigma          0.055000 0.032700
signif(rel_diffs_untransformed, 3)
#>                    2.5%    97.5%
+#> parent_0       0.000541 0.000222
+#> k_parent       0.004610 0.002370
+#> k_m1           0.014700 0.002530
+#> f_parent_to_m1 0.004700 0.002350
+#> sigma          0.055000 0.032700

 
 # Investigate a case with formation fractions
 f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
 ci_profile_ff <- confint(f_d_2, method = "profile", cores = n_cores)
#> Profiling the likelihood
ci_profile_ff
#>                        2.5%        97.5%
-#> parent_0       96.456003650 1.027703e+02
+#> parent_0       96.456003640 1.027703e+02
 #> k_parent        0.090911032 1.071578e-01
 #> k_m1            0.003892605 6.702778e-03
 #> f_parent_to_m1  0.471328495 5.611550e-01
 #> sigma           2.535612399 3.985263e+00
ci_quadratic_transformed_ff <- confint(f_d_2, method = "quadratic")
 ci_quadratic_transformed_ff
#>                        2.5%        97.5%
-#> parent_0       96.403839992 1.027931e+02
+#> parent_0       96.403839460 1.027931e+02
 #> k_parent        0.090823790 1.072543e-01
 #> k_m1            0.004012216 6.897547e-03
-#> f_parent_to_m1  0.469118711 5.595960e-01
+#> f_parent_to_m1  0.469118713 5.595960e-01
 #> sigma           2.396089689 3.854918e+00
ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE)
 ci_quadratic_untransformed_ff
#>                        2.5%        97.5%
-#> parent_0       96.403839944 1.027931e+02
+#> parent_0       96.403839413 1.027931e+02
 #> k_parent        0.090491931 1.069035e-01
 #> k_m1            0.003835483 6.685819e-03
-#> f_parent_to_m1  0.469113362 5.598386e-01
+#> f_parent_to_m1  0.469113365 5.598386e-01
 #> sigma           2.396089689 3.854918e+00
rel_diffs_transformed_ff <- abs((ci_quadratic_transformed_ff - ci_profile_ff)/ci_profile_ff)
 rel_diffs_untransformed_ff <- abs((ci_quadratic_untransformed_ff - ci_profile_ff)/ci_profile_ff)
 # While the confidence interval for the parent rate constant is closer to
@@ -318,15 +347,15 @@ On Windows machines, cores > 1 is currently not supported.
 #> k_m1           FALSE FALSE
 #> f_parent_to_m1  TRUE FALSE
 #> sigma          FALSE FALSE
rel_diffs_transformed_ff
#>                        2.5%        97.5%
-#> parent_0       0.0005408026 0.0002217846
-#> k_parent       0.0009596414 0.0009003877
-#> k_m1           0.0307277403 0.0290579212
-#> f_parent_to_m1 0.0046884168 0.0027782604
+#> parent_0       0.0005408080 0.0002217794
+#> k_parent       0.0009596417 0.0009003876
+#> k_m1           0.0307277370 0.0290579182
+#> f_parent_to_m1 0.0046884130 0.0027782556
 #> sigma          0.0550252516 0.0327066836
rel_diffs_untransformed_ff
#>                        2.5%        97.5%
-#> parent_0       0.0005408031 0.0002217851
-#> k_parent       0.0046100089 0.0023730230
-#> k_m1           0.0146746442 0.0025300972
-#> f_parent_to_m1 0.0046997642 0.0023460268
+#> parent_0       0.0005408085 0.0002217799
+#> k_parent       0.0046100096 0.0023730229
+#> k_m1           0.0146746469 0.0025301011
+#> f_parent_to_m1 0.0046997599 0.0023460223
 #> sigma          0.0550252516 0.0327066836

 # The profiling for the following fit does not finish in a reasonable time,
 # therefore we use the quadratic approximation
@@ -337,29 +366,24 @@ On Windows machines, cores > 1 is currently not supported.
 DFOP_par_c <- synthetic_data_for_UBA_2014[[12]]$data
 f_tc_2 <- mkinfit(m_synth_DFOP_par, DFOP_par_c, error_model = "tc",
   error_model_algorithm = "direct", quiet = TRUE)
-confint(f_tc_2, method = "quadratic")
#>                        2.5%        97.5%
-#> parent_0       94.596218141 106.19934683
-#> k_M1            0.037605440   0.04490756
-#> k_M2            0.008568747   0.01087674
-#> f_parent_to_M1  0.021466788   0.62023878
-#> f_parent_to_M2  0.015168650   0.37975352
-#> k1              0.273897581   0.33388066
-#> k2              0.018614556   0.02250379
-#> g               0.671943831   0.73583255
-#> sigma_low       0.251283700   0.83992071
-#> rsd_high        0.040411017   0.07662005
confint(f_tc_2, "parent_0", method = "quadratic")
#>              2.5%    97.5%
-#> parent_0 94.59622 106.1993
# }
+confint(f_tc_2, method = "quadratic")
#>                       2.5%        97.5%
+#> parent_0       94.59613833 106.19939215
+#> k_M1            0.03760542   0.04490759
+#> k_M2            0.00856874   0.01087675
+#> f_parent_to_M1  0.02146166   0.62023888
+#> f_parent_to_M2  0.01516502   0.37975343
+#> k1              0.27389751   0.33388078
+#> k2              0.01861456   0.02250379
+#> g               0.67194349   0.73583256
+#> sigma_low       0.25128383   0.83992146
+#> rsd_high        0.04041100   0.07662001
confint(f_tc_2, "parent_0", method = "quadratic")
#>              2.5%    97.5%
+#> parent_0 94.59614 106.1994
# }

- @@ -370,7 +394,7 @@ On Windows machines, cores > 1 is currently not supported.

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diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html index ef38c521..05d65191 100644 --- a/docs/reference/endpoints.html +++ b/docs/reference/endpoints.html @@ -11,23 +11,27 @@ with mkinfit — endpoints • mkin - + - + - + + + + + - - + + - + - - + + @@ -43,7 +47,6 @@ from kinetic models fitted with mkinfit. If the SFORB model was specified for one of the parents or metabolites, the Eigenvalues are returned. These are equivalent to the rate constantes of the DFOP model, but with the advantage that the SFORB model can also be used for metabolites." /> - @@ -61,7 +64,7 @@ advantage that the SFORB model can also be used for metabolites." /> - +

@@ -119,7 +122,12 @@ advantage that the SFORB model can also be used for metabolites." />

@@ -135,7 +143,7 @@ advantage that the SFORB model can also be used for metabolites." /> @@ -162,9 +170,9 @@ advantage that the SFORB model can also be used for metabolites.

Value

A list with a matrix of dissipation times named distimes, - and, if applicable, a vector of formation fractions named ff - and, if the SFORB model was in use, a vector of eigenvalues - of these SFORB models, equivalent to DFOP rate constants

+and, if applicable, a vector of formation fractions named ff +and, if the SFORB model was in use, a vector of eigenvalues +of these SFORB models, equivalent to DFOP rate constants

Note

The function is used internally by summary.mkinfit.

@@ -192,17 +200,10 @@ advantage that the SFORB model can also be used for metabolites.

- @@ -213,7 +214,7 @@ advantage that the SFORB model can also be used for metabolites.

Site built with pkgdown 1.4.1.

Site built with pkgdown 1.5.1.

diff --git a/docs/reference/get_deg_func.html b/docs/reference/get_deg_func.html index c5b8e38c..598a25c0 100644 --- a/docs/reference/get_deg_func.html +++ b/docs/reference/get_deg_func.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -37,7 +41,6 @@ - @@ -55,7 +58,7 @@ - +

@@ -113,7 +116,12 @@

@@ -128,7 +136,7 @@

@@ -142,15 +150,13 @@

Value

A function that was likely previously assigned from within - nlme.mmkin

+nlme.mmkin

- @@ -161,7 +167,7 @@

Site built with pkgdown 1.4.1.

Site built with pkgdown 1.5.1.

diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html index 76ce2233..8410e499 100644 --- a/docs/reference/ilr.html +++ b/docs/reference/ilr.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -38,7 +42,6 @@ - @@ -56,7 +59,7 @@ transformations." /> - +

@@ -114,7 +117,12 @@ transformations." />

@@ -129,7 +137,7 @@ transformations." />

@@ -155,16 +163,16 @@ sensible for vectors with all elements being greater than zero.

Value

The result of the forward or backward transformation. The returned - components always sum to 1 for the case of the inverse log-ratio - transformation.

+components always sum to 1 for the case of the inverse log-ratio +transformation.

References

Peter Filzmoser, Karel Hron (2008) Outlier Detection for - Compositional Data Using Robust Methods. Math Geosci 40 233-248

+Compositional Data Using Robust Methods. Math Geosci 40 233-248

Examples


@@ -180,18 +188,10 @@ sensible for vectors with all elements being greater than zero.
 length(b) # Four elements
#> [1] 4
ilr(c(b[1:3], 1 - sum(b[1:3]))) # Gives c(0.1, 0.3, 0.5)
#> [1] 0.1 0.3 0.5

- @@ -202,7 +202,7 @@ sensible for vectors with all elements being greater than zero.

Site built with pkgdown 1.4.1.

Site built with pkgdown 1.5.1.

diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html index 9a54496e..8e33c28f 100644 --- a/docs/reference/logLik.mkinfit.html +++ b/docs/reference/logLik.mkinfit.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ observed value, as calculated as part of the fitting procedure using dnorm, i.e. assuming normal distribution, and with the means predicted by the degradation model, and the standard deviations predicted by the error model." /> - @@ -59,7 +62,7 @@ the error model." /> - +

@@ -117,7 +120,12 @@ the error model." />

@@ -132,7 +140,7 @@ the error model." />

@@ -163,8 +171,8 @@ the error model.

Value

An object of class logLik with the number of estimated - parameters (degradation model parameters plus variance model parameters) - as attribute.

+parameters (degradation model parameters plus variance model parameters) +as attribute.

Details

The total number of estimated parameters returned with the value of the @@ -173,7 +181,7 @@ and the fitted error model parameters.

Examples


@@ -189,18 +197,10 @@ and the fitted error model parameters.

- @@ -211,7 +211,7 @@ and the fitted error model parameters.

Site built with pkgdown 1.4.1.

Site built with pkgdown 1.5.1.

diff --git a/docs/reference/logistic.solution.html b/docs/reference/logistic.solution.html index b0ec77b1..9907297c 100644 --- a/docs/reference/logistic.solution.html +++ b/docs/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> @@ -179,19 +179,19 @@ an increasing rate constant, supposedly caused by microbial growth

Note

The solution of the logistic model reduces to the - SFO.solution if k0 is equal to kmax.

+SFO.solution if k0 is equal to kmax.

References

Value

For max_twa_parent, a numeric vector, named using the - windows argument. For the other functions, a numeric vector of - length one (also known as 'a number').

+windows argument. For the other functions, a numeric vector of +length one (also known as 'a number').

References

Examples


@@ -230,17 +238,10 @@ factor (f_twa) is calculated.
 #> 34.71343 18.22124

- @@ -251,7 +252,7 @@ factor (f_twa) is calculated.

Site built with pkgdown 1.4.1.

Site built with pkgdown 1.5.1.

diff --git a/docs/reference/mkinerrplot-1.png b/docs/reference/mkinerrplot-1.png index 46a80308..c5d3495f 100644 Binary files a/docs/reference/mkinerrplot-1.png and b/docs/reference/mkinerrplot-1.png differ diff --git a/docs/reference/mkinerrplot.html b/docs/reference/mkinerrplot.html index 47f48b72..7e5a00f2 100644 --- a/docs/reference/mkinerrplot.html +++ b/docs/reference/mkinerrplot.html @@ -10,23 +10,27 @@ - + - + - + + + + + - - + + - + - - + + @@ -41,7 +45,6 @@ observed variables from an mkinfit object. In addition, one or more dashed line(s) show the fitted error model. A combined plot of the fitted model and this error model plot can be obtained with plot.mkinfit using the argument show_errplot = TRUE." /> - @@ -59,7 +62,7 @@ using the argument show_errplot = TRUE." /> - +

@@ -117,7 +120,12 @@ using the argument show_errplot = TRUE." />

@@ -132,7 +140,7 @@ using the argument show_errplot = TRUE." />

@@ -212,18 +220,18 @@ be passed on to

legend
     
       ...
-      further arguments passed to plot.
+      further arguments passed to plot.
     
     
 
     Value
 
     Nothing is returned by this function, as it is called for its side
-  effect, namely to produce a plot.
+effect, namely to produce a plot.
     See also
 
     mkinplot, for a way to plot the data and the fitted
-  lines of the mkinfit object.
+lines of the mkinfit object.


 
     Examples
     
@@ -232,17 +240,10 @@ be passed on to legend

   
-  
-    Contents
-    
-      Arguments
-      Value
-      See also
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -253,7 +254,7 @@ be passed on to legend
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index 9974b66b..ceac59bf 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -78,7 +78,7 @@ likelihood function." />
       
       
         mkin
-        0.9.50
+        0.9.50.1
       
     
 
@@ -209,15 +209,15 @@ detection.
     
       parms.ini
       A named vector of initial values for the parameters,
-  including parameters to be optimised and potentially also fixed parameters
-  as indicated by fixed_parms.  If set to "auto", initial values for
-  rate constants are set to default values.  Using parameter names that are
-  not in the model gives an error.
+including parameters to be optimised and potentially also fixed parameters
+as indicated by fixed_parms.  If set to "auto", initial values for
+rate constants are set to default values.  Using parameter names that are
+not in the model gives an error.
 It is possible to only specify a subset of the parameters that the model
-  needs. You can use the parameter lists "bparms.ode" from a previously
-  fitted model, which contains the differential equation parameters from
-  this model.  This works nicely if the models are nested. An example is
-  given below.
+needs. You can use the parameter lists "bparms.ode" from a previously
+fitted model, which contains the differential equation parameters from
+this model.  This works nicely if the models are nested. An example is
+given below.
     
     
       state.ini
@@ -326,42 +326,44 @@ is 1e-10, much lower than in lsoda.
     
       error_model
       If the error model is "const", a constant standard
-  deviation is assumed.
+deviation is assumed.
 If the error model is "obs", each observed variable is assumed to have its
-  own variance.
+own variance.
 If the error model is "tc" (two-component error model), a two component
-  error model similar to the one described by Rocke and Lorenzato (1995) is
-  used for setting up the likelihood function.  Note that this model
-  deviates from the model by Rocke and Lorenzato, as their model implies
-  that the errors follow a lognormal distribution for large values, not a
-  normal distribution as assumed by this method.
+error model similar to the one described by Rocke and Lorenzato (1995) is
+used for setting up the likelihood function.  Note that this model
+deviates from the model by Rocke and Lorenzato, as their model implies
+that the errors follow a lognormal distribution for large values, not a
+normal distribution as assumed by this method.
     
     
       error_model_algorithm
       If "auto", the selected algorithm depends on
-  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 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 "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.
+initial unweighted least 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.
+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.
+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" (Iteratively Reweighted Least Squares) 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.
+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
@@ -383,14 +385,10 @@ the error model parameters in IRLS fits.
     
     
 
-    Source
-
-    Rocke, David M. und Lorenzato, Stefan (1995) A two-component model
-  for measurement error in analytical chemistry. Technometrics 37(2), 176-184.
     Value
 
     A list with "mkinfit" in the class attribute.  A summary can be
-  obtained by summary.mkinfit.
+obtained by summary.mkinfit.
     Details
 
     Per default, parameters in the kinetic models are internally transformed in
@@ -399,33 +397,40 @@ estimators.
     Note
 
     When using the "IORE" submodel for metabolites, fitting with
-  "transform_rates = TRUE" (the default) often leads to failures of the
-  numerical ODE solver. In this situation it may help to switch off the
-  internal rate transformation.
+"transform_rates = TRUE" (the default) often leads to failures of the
+numerical ODE solver. In this situation it may help to switch off the
+internal rate transformation.
+    References
+
+    Rocke DM and Lorenzato S (1995) A two-component model
+for measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+Ranke J and Meinecke S (2019) Error Models for the Kinetic Evaluation of Chemical
+Degradation Data. Environments 6(12) 124
+doi:10.3390/environments6120124.
     See also
 
     Plotting methods plot.mkinfit and
-  mkinparplot.
+mkinparplot.
 Comparisons of models fitted to the same data can be made using
-  AIC by virtue of the method logLik.mkinfit.
+AIC by virtue of the method logLik.mkinfit.
 Fitting of several models to several datasets in a single call to
-  mmkin.
+mmkin.
 
     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 
+summary(fit)
#> mkin version used for fitting:    0.9.50.1 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Mon May 11 05:14:26 2020 
-#> Date of summary: Mon May 11 05:14:26 2020 
+#> Date of fit:     Tue May 12 08:36:07 2020 
+#> Date of summary: Tue May 12 08:36:07 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 0.047 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -502,7 +507,7 @@ estimators.
   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 
-#>       0.407       0.002       0.409 
parms(fit)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#>       0.408       0.008       0.416 
parms(fit)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
 #>   99.598483222    0.098697734    0.005260651    0.514475962    3.125503875 
endpoints(fit)
#> $ff
 #>   parent_m1 parent_sink 
 #>    0.514476    0.485524 
@@ -592,7 +597,7 @@ estimators.
 #> Sum of squared residuals at call 166: 371.2134
 #> Sum of squared residuals at call 168: 371.2134
 #> Negative log-likelihood at call 178: 97.22429
#> Optimisation successfully terminated.
#>        User      System verstrichen 
-#>       0.349       0.000       0.350 
parms(fit.deSolve)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
+#>       0.350       0.001       0.351 
parms(fit.deSolve)
#>       parent_0       k_parent           k_m1 f_parent_to_m1          sigma 
 #>   99.598480759    0.098697739    0.005260651    0.514475958    3.125503874 
endpoints(fit.deSolve)
#> $ff
 #>   parent_m1 parent_sink 
 #>    0.514476    0.485524 
@@ -622,12 +627,12 @@ estimators.
 fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$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
# }
 
 # \dontrun{
-# Weighted fits, including IRLS
+# 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 
+                      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:    0.9.50.1 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Mon May 11 05:14:31 2020 
-#> Date of summary: Mon May 11 05:14:31 2020 
+#> Date of fit:     Tue May 12 08:36:12 2020 
+#> Date of summary: Tue May 12 08:36:12 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -635,7 +640,7 @@ estimators.
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 421 model solutions performed in 0.124 s
+#> Fitted using 421 model solutions performed in 0.146 s
 #> 
 #> Error model: Constant variance 
 #> 
@@ -746,10 +751,10 @@ estimators.
 #>   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 
+#>   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:    0.9.50.1 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Mon May 11 05:14:32 2020 
-#> Date of summary: Mon May 11 05:14:32 2020 
+#> Date of fit:     Tue May 12 08:36:13 2020 
+#> Date of summary: Tue May 12 08:36:13 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -757,7 +762,7 @@ estimators.
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 978 model solutions performed in 0.336 s
+#> Fitted using 978 model solutions performed in 0.337 s
 #> 
 #> Error model: Variance unique to each observed variable 
 #> 
@@ -883,10 +888,10 @@ estimators.
 #>   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 
+#>   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:    0.9.50.1 
 #> R version used for fitting:       4.0.0 
-#> Date of fit:     Mon May 11 05:14:32 2020 
-#> Date of summary: Mon May 11 05:14:32 2020 
+#> Date of fit:     Tue May 12 08:36:14 2020 
+#> Date of summary: Tue May 12 08:36:14 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -894,7 +899,7 @@ estimators.
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 1875 model solutions performed in 0.642 s
+#> Fitted using 1875 model solutions performed in 0.647 s
 #> 
 #> Error model: Two-component variance function 
 #> 
diff --git a/docs/reference/mkinmod.html b/docs/reference/mkinmod.html
index 81b62ae1..1c97048e 100644
--- a/docs/reference/mkinmod.html
+++ b/docs/reference/mkinmod.html
@@ -75,7 +75,7 @@ list of lists can be given in the speclist argument." />
       
       
         mkin
-        0.9.50
+        0.9.50.1
       
     
 
@@ -205,15 +205,18 @@ applicable to give detailed information about the C function being built.
Value
 
     A list of class mkinmod for use with mkinfit,
-  containing, among others,
+containing, among others,
 diffs
A vector of string representations of differential equations, one for
-    each modelling variable.
-  map
A list containing named character vectors for each observed variable,
-    specifying the modelling variables by which it is represented.
-  use_of_ff
The content of use_of_ff is passed on in this list component.
-  coefmat
The coefficient matrix, if the system of differential equations can be
-    represented by one.
-  cf
If generated, the compiled function as returned by cfunction.
+each modelling variable.
+map
A list containing named character vectors for each observed variable,
+specifying the modelling variables by which it is represented.
+use_of_ff
The content of use_of_ff is passed on in this list component.
+deg_func
If generated, a function containing the solution of the degradation
+model.
+coefmat
The coefficient matrix, if the system of differential equations can be
+represented by one.
+cf
If generated, a compiled function calculating the derivatives as
+returned by cfunction.
 
     Details
 
@@ -222,17 +225,17 @@ in the FOCUS and NAFTA guidance documents are used.
     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 second note in the help
+page to mkinfit.
     References
 
     FOCUS (2006) “Guidance Document on Estimating Persistence
-  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
+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
 NAFTA Technical Working Group on Pesticides (not dated) Guidance for
-  Evaluating and Calculating Degradation Kinetics in Environmental Media
+Evaluating and Calculating Degradation Kinetics in Environmental Media
 
     Examples
     
@@ -252,7 +255,7 @@ in the FOCUS and NAFTA guidance documents are used.
 SFO_SFO <- mkinmod(
   parent = mkinsub("SFO", "m1"),
   m1 = mkinsub("SFO"), verbose = TRUE)
#> Compilation argument:
-#>  /usr/lib/R/bin/R CMD SHLIB file1a1abf38df.c 2> file1a1abf38df.c.err.txt 
+#>  /usr/lib/R/bin/R CMD SHLIB file15d32c282a7d.c 2> file15d32c282a7d.c.err.txt 
 #> Program source:
 #>   1: #include <R.h>
 #>   2: 
diff --git a/docs/reference/mkinparplot-1.png b/docs/reference/mkinparplot-1.png
index d4259579..6853a4ba 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 af936795..dfe695d0 100644
--- a/docs/reference/mkinparplot.html
+++ b/docs/reference/mkinparplot.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -38,7 +42,6 @@
 
 
-
 
 
 
@@ -56,7 +59,7 @@ mkinfit." />
 
   
 
-  
+  
     
       
       
@@ -70,7 +73,7 @@ mkinfit." />
       
       
         mkin
-        0.9.49.6
+        0.9.50.1
       
     
 
@@ -114,7 +117,12 @@ mkinfit." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -129,7 +137,7 @@ mkinfit." />
   
     
     Function to plot the confidence intervals obtained using mkinfit
-    
+    Source: R/mkinparplot.R
     mkinparplot.Rd
     
 
@@ -152,7 +160,7 @@ mkinfit." />
     Value
 
     Nothing is returned by this function, as it is called for its side
-  effect, namely to produce a plot.
+effect, namely to produce a plot.
 
     Examples
     
@@ -163,16 +171,10 @@ mkinfit." />
   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)
# }
 
   
-  
-    Contents
-    
-      Arguments
-      Value
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -183,7 +185,7 @@ mkinfit." />
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/mkinresplot-1.png b/docs/reference/mkinresplot-1.png
index d29181c3..bb9657b4 100644
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diff --git a/docs/reference/mkinresplot.html b/docs/reference/mkinresplot.html
index 698af302..cd6bf4a1 100644
--- a/docs/reference/mkinresplot.html
+++ b/docs/reference/mkinresplot.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -40,7 +44,6 @@
 variables from an mkinfit object. A combined plot of the fitted model and
 the residuals can be obtained using plot.mkinfit using the
 argument show_residuals = TRUE." />
-
 
 
 
@@ -58,7 +61,7 @@ argument show_residuals = TRUE." />
 
   
 
-  
+  
     
       
       
@@ -72,7 +75,7 @@ argument show_residuals = TRUE." />
       
       
         mkin
-        0.9.49.9
+        0.9.50.1
       
     
 
@@ -116,7 +119,12 @@ argument show_residuals = TRUE." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -131,7 +139,7 @@ argument show_residuals = TRUE." />
   
     
     Function to plot residuals stored in an mkin object
-    
+    Source: R/mkinresplot.R
     mkinresplot.Rd
     
 
@@ -216,36 +224,29 @@ be passed on to legend
     
       ...
-      further arguments passed to plot.
+      further arguments passed to plot.
     
     
 
     Value
 
     Nothing is returned by this function, as it is called for its side
-  effect, namely to produce a plot.
+effect, namely to produce a plot.
     See also
 
     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.
+lines of the mkinfit object, and plot_res for a function
+combining the plot of the fit and the residual plot.

 
     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")

 
   
-  
-    Contents
-    
-      Arguments
-      Value
-      See also
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -256,7 +257,7 @@ be passed on to legend
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/mmkin-1.png b/docs/reference/mmkin-1.png
index 328cec9b..135d5446 100644
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diff --git a/docs/reference/mmkin-2.png b/docs/reference/mmkin-2.png
index 7b1d73d5..40109afc 100644
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diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png
index 4c412abc..bce34531 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 201ef0fd..02976ced 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 29206932..56750342 100644
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diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html
index c1f62be7..db359f48 100644
--- a/docs/reference/mmkin.html
+++ b/docs/reference/mmkin.html
@@ -11,23 +11,27 @@ more datasets — mmkin • mkin
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -40,7 +44,6 @@ more datasets — mmkin • mkin
 more datasets — mmkin" />
 
-
 
 
 
@@ -58,7 +61,7 @@ datasets specified in its first two arguments." />
 
   
 
-  
+  
     
       
       
@@ -72,7 +75,7 @@ datasets specified in its first two arguments." />
       
       
         mkin
-        0.9.49.10
+        0.9.50.1
       
     
 
@@ -116,7 +119,12 @@ datasets specified in its first two arguments." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -132,7 +140,7 @@ 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
     mmkin.Rd
     
 
@@ -184,12 +192,12 @@ 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 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
-  plotting.
+plotting.
 
     Examples
     
@@ -205,42 +213,35 @@ for parallel execution.
 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))
-
+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 verstrichen 
-#>      16.471       0.352       5.654 
time_1
#>        User      System verstrichen 
-#>      19.578       0.000      19.590 

+#>       4.370       0.401       1.265 
time_1
#>        User      System verstrichen 
+#>       5.000       0.008       5.011 

 endpoints(fits.0[["SFO_lin", 2]])
#> $ff
 #>   parent_M1 parent_sink       M1_M2     M1_sink 
-#>   0.7340480   0.2659520   0.7505686   0.2494314 
+#>   0.7340478   0.2659522   0.7505691   0.2494309 
 #> 
 #> $distimes
 #>              DT50       DT90
-#> parent  0.8777689   2.915885
-#> M1      2.3257452   7.725958
-#> M2     33.7200890 112.015711
+#> parent  0.8777688   2.915885
+#> 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])
# }
 
 
   
-  
-    Contents
-    
-      Arguments
-      Value
-      See also
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -251,7 +252,7 @@ for parallel execution.
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/nafta.html b/docs/reference/nafta.html
index 60917ff4..02c44737 100644
--- a/docs/reference/nafta.html
+++ b/docs/reference/nafta.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -41,7 +45,6 @@ and returns an object of class nafta that has methods for printing
 and plotting.
 Print nafta objects. The results for the three models are printed in the
 order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
-
 
 
 
@@ -59,7 +62,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
 
   
 
-  
+  
     
       
       
@@ -73,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
       
       
         mkin
-        0.9.49.6
+        0.9.50.1
       
     
 
@@ -117,7 +120,12 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -132,7 +140,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." />
   
     
     Evaluate parent kinetics using the NAFTA guidance
-    
+    Source: R/nafta.R
     nafta.Rd
     
 
@@ -186,20 +194,20 @@ dissipation times.
     Source
 
     NAFTA (2011) Guidance for evaluating and calculating degradation
-  kinetics in environmental media. NAFTA Technical Working Group on
-  Pesticides
-  https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation
-  accessed 2019-02-22
+kinetics in environmental media. NAFTA Technical Working Group on
+Pesticides
+https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/guidance-evaluating-and-calculating-degradation
+accessed 2019-02-22
 US EPA (2015) Standard Operating Procedure for Using the NAFTA Guidance to
-  Calculate Representative Half-life Values and Characterizing Pesticide
-  Degradation
-  https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance
+Calculate Representative Half-life Values and Characterizing Pesticide
+Degradation
+https://www.epa.gov/pesticide-science-and-assessing-pesticide-risks/standard-operating-procedure-using-nafta-guidance
     Value
 
     An list of class nafta. The list element named "mmkin" is the
-  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.
+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.
 
     Examples
     
@@ -240,20 +248,13 @@ dissipation times.
 #> DFOP  429    2380      841
 #> 
 #> Representative half-life:
-#> [1] 841.41
  plot(nafta_evaluation)

+#> [1] 841.41
  plot(nafta_evaluation)

 
   
-  
-    Contents
-    
-      Arguments
-      Source
-      Value
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -264,7 +265,7 @@ dissipation times.
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/nlme.html b/docs/reference/nlme.html
index b92d2141..78489ee7 100644
--- a/docs/reference/nlme.html
+++ b/docs/reference/nlme.html
@@ -75,7 +75,7 @@ datasets." />
       
       
         mkin
-        0.9.50
+        0.9.50.1
       
     
 
@@ -173,9 +173,9 @@ datasets.
 
     A function that can be used with nlme
 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?
+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?
 A groupedData object
     See also
 
diff --git a/docs/reference/nlme.mmkin-1.png b/docs/reference/nlme.mmkin-1.png
index 67bd07de..67f279da 100644
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index ee6bb2e4..16a5d7bd 100644
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diff --git a/docs/reference/nlme.mmkin-3.png b/docs/reference/nlme.mmkin-3.png
index 1d5569e9..73426834 100644
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index 7249bd1e..9b46b425 100644
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index 26781b85..3398dd4a 100644
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diff --git a/docs/reference/nlme.mmkin-6.png b/docs/reference/nlme.mmkin-6.png
index 5f5a759a..7b663991 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 1d4fa8ea..d2c38a63 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 33cc9164..4e5a86cb 100644
--- a/docs/reference/nlme.mmkin.html
+++ b/docs/reference/nlme.mmkin.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -39,7 +43,6 @@
 
-
 
 
 
@@ -57,7 +60,7 @@ have been obtained by fitting the same model to a list of datasets." />
 
   
 
-  
+  
     
       
       
@@ -71,7 +74,7 @@ have been obtained by fitting the same model to a list of datasets." />
       
       
         mkin
-        0.9.49.10
+        0.9.50.1
       
     
 
@@ -115,7 +118,12 @@ have been obtained by fitting the same model to a list of datasets." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -130,7 +138,7 @@ 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
     nlme.mmkin.Rd
     
 
@@ -243,7 +251,7 @@ parameters taken from the mmkin object are used
     Value
 
     Upon success, a fitted nlme.mmkin object, which is an nlme object
-  with additional elements
+with additional elements
     See also
 
     nlme_function
@@ -309,142 +317,124 @@ parameters taken from the mmkin object are used
    "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

+  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

+  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)
#> 
 #> **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 
-#> -0.2079984  0.8563873  1.7454146  1.0917723  1.2756924 
+#> -0.2079793  0.8563830  1.7454105  1.0917354  1.2756825 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.8786 
-#>  fixed effects: 94.17379  -5.473199  -0.6970239  -0.2025094  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 
-#> 0.7865373 0.1448077 
+#> 0.7960134 0.1447728 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -396.3824, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -1.712408e-01 -2.680989e-05  1.842119e+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 =  643.8022 
-#>  fixed effects: 94.17385  -5.473491  -0.6970405  -0.202514  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 
-#> 5.341904e-05 1.227073e-03 
+#> 5.382757e-05 1.236667e-03 
 #> 
 #> **Iteration 3
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -0.1712484347 -0.0001513555  1.8420964843  1.0739800649  1.3229176990 
+#> -0.1712499044 -0.0001499831  1.8420971364  1.0739799123  1.3229167796 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7947 
-#>  fixed effects: 94.17386  -5.473522  -0.6970423  -0.2025142  2.10387  
+#> 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 
-#> 5.568186e-06 1.276609e-04 
+#> 6.072817e-06 1.400857e-04 
 #> 
 #> **Iteration 4
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
-#>          ds1          ds2          ds3          ds4          ds5 
-#> -0.171251200 -0.000164506  1.842095097  1.073980200  1.322916184 
+#>           ds1           ds2           ds3           ds4           ds5 
+#> -0.1712529502 -0.0001641277  1.8420957542  1.0739797181  1.3229173076 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7934 
+#> 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 
-#> 2.332100e-06 1.979007e-05 
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
+#> 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: -404.9591, nlminb iterations: 1
+#> LME step: Loglik: -404.9582, nlminb iterations: 1
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5        ds6 
-#> -0.4114594  0.9798456  1.6990016  0.7293119  0.3353829  1.7112922 
+#> -0.4114355  0.9798697  1.6990037  0.7293315  0.3354323  1.7113046 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.391 
-#>  fixed effects: 93.82265  -5.455841  -0.6788837  -1.862191  -4.199654  0.05531046  
+#> 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 
-#> 0.7872619 0.5811683 
+#> 0.7885368 0.5822683 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -407.7755, nlminb iterations: 11
 #> reStruct  parameters:
 #>          ds1          ds2          ds3          ds4          ds5          ds6 
-#> -0.371222832  0.003084754  1.789952290  0.724634064  0.301559136  1.754244638 
-#>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.359 
-#>  fixed effects: 93.82269  -5.456014  -0.6788967  -1.862202  -4.199678  0.05534118  
-#>  iterations: 120 
-#> Convergence crit. (must all become <= tolerance = 0.0005):
-#>        fixed     reStruct 
-#> 0.0005550885 0.0007749418 
-#> 
-#> **Iteration 3
-#> LME step: Loglik: -407.7756, nlminb iterations: 11
-#> reStruct  parameters:
-#>          ds1          ds2          ds3          ds4          ds5          ds6 
-#> -0.371217033  0.003064156  1.789935045  0.724683005  0.301622307  1.754234135 
+#> -0.371224133  0.003056179  1.789939402  0.724671158  0.301602977  1.754200729 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.358 
-#>  fixed effects: 93.82269  -5.456017  -0.6788969  -1.862197  -4.199677  0.05532978  
+#> 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 
-#> 2.059533e-04 4.860085e-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)

+#> 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  843.8541  884.6194 -408.9270                         
-#> f_nlme_fomc_sfo     2 11  818.5149  853.0087 -398.2575 1 vs 2  21.33913  <.0001
-#> f_nlme_sfo_sfo      3  9 1085.1821 1113.4042 -533.5910 2 vs 3 270.66712  <.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  843.8541  884.6194 -408.927                       
-#> f_nlme_sfo_sfo      2  9 1085.1821 1113.4042 -533.591 1 vs 2 249.328  <.0001

+#> 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
 #> parent_sink   parent_A1     A1_sink 
-#>   0.5912435   0.4087565   1.0000000 
+#>   0.5912432   0.4087568   1.0000000 
 #> 
 #> $distimes
-#>            DT50      DT90
-#> parent 19.13517  63.56565
-#> A1     66.02149 219.31865
+#>            DT50     DT90
+#> parent 19.13518  63.5657
+#> A1     66.02155 219.3189
 #> 
  endpoints(f_nlme_dfop_sfo)
#> $ff
-#>   parent_A1 parent_sink     A1_sink 
-#>   0.2768571   0.7231429   1.0000000 
+#>   parent_A1 parent_sink 
+#>   0.2768574   0.7231426 
 #> 
 #> $distimes
-#>             DT50     DT90  DT50_k1 DT50_k2
-#> parent  11.07092 104.6325 4.462389 46.2085
-#> A1     162.30937 539.1801       NA      NA
+#>             DT50     DT90  DT50_k1  DT50_k2
+#> parent  11.07091 104.6320 4.462384 46.20825
+#> A1     162.30536 539.1667       NA       NA
 #> 
# }
 

   
-  
-    Contents
-    
-      Arguments
-      Value
-      See also
-      Examples
-    
-
+  
+    
+      Contents
+    
   
 
 
@@ -455,7 +445,7 @@ parameters taken from the mmkin object are used
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/plot.mkinfit.html b/docs/reference/plot.mkinfit.html
index 2ede2dec..284c30a6 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
+        0.9.50.1
       
     
 
@@ -255,7 +255,7 @@ fits is shown, the residual plot is in the lower third of the plot.
 Otherwise, i.e. if "sep_obs" is given, the residual plots will be located
 to the right of the plots of the fitted curves. If this is set to
 'standardized', a plot of the residuals divided by the standard deviation
- given by the fitted error model will be shown.
+given by the fitted error model will be shown.
     
     
       show_errplot
diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html
index ef5718e5..4b893032 100644
--- a/docs/reference/plot.mmkin.html
+++ b/docs/reference/plot.mmkin.html
@@ -11,23 +11,27 @@ of an mmkin object — plot.mmkin • mkin
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -41,7 +45,6 @@ of an mmkin object — plot.mmkin" />
 
-
 
 
 
@@ -59,7 +62,7 @@ the fit of at least one model to the same dataset is shown." />
 
   
 
-  
+  
     
       
       
@@ -73,7 +76,7 @@ the fit of at least one model to the same dataset is shown." />
       
       
         mkin
-        0.9.49.10
+        0.9.50.1
       
     
 
@@ -117,7 +120,12 @@ the fit of at least one model to the same dataset is shown." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -133,7 +141,7 @@ 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
     plot.mmkin.Rd
     
 
@@ -144,7 +152,7 @@ the fit of at least one model to the same dataset is shown.
     
 
     # S3 method for mmkin
-plot(
+plot(
   x,
   main = "auto",
   legends = 1,
@@ -185,7 +193,7 @@ values, with the error model, using mkinerrplot
       standardized
       Should the residuals be standardized? This option
 is passed to mkinresplot, it only takes effect if
-`resplot = "time"`.
+resplot = "time".
     
     
       show_errmin
@@ -237,27 +245,20 @@ latex is being used for the formatting of the chi2 error level.
   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)

+#> 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")
  # }
 
 

   
-  
-    Contents
-    
-      Arguments
-      Value
-      Details
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -268,7 +269,7 @@ latex is being used for the formatting of the chi2 error level.
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/plot.nlme.mmkin-2.png b/docs/reference/plot.nlme.mmkin-2.png
index 27c09796..265fd2e0 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 256739eb..9c85fbf0 100644
--- a/docs/reference/plot.nlme.mmkin.html
+++ b/docs/reference/plot.nlme.mmkin.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -37,7 +41,6 @@
 
 
 
-
 
 
 
@@ -55,7 +58,7 @@
 
   
 
-  
+  
     
       
       
@@ -69,7 +72,7 @@
       
       
         mkin
-        0.9.49.10
+        0.9.50.1
       
     
 
@@ -113,7 +116,12 @@
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -128,7 +136,7 @@
   
     
     Plot a fitted nonlinear mixed model obtained via an mmkin row object
-    
+    Source: R/plot.nlme.mmkin.R
     plot.nlme.mmkin.Rd
     
 
@@ -137,7 +145,7 @@
     
 
     # S3 method for nlme.mmkin
-plot(
+plot(
   x,
   i = 1:ncol(x$mmkin_orig),
   main = "auto",
@@ -183,7 +191,7 @@ values, with the error model, using mkinerrplot
       standardized
       Should the residuals be standardized? This option
 is passed to mkinresplot, it only takes effect if
-`resplot = "time"`.
+resplot = "time".
     
     
       show_errmin
@@ -229,22 +237,16 @@ than two rows of plots are shown.
  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)
+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)

   
-  
-    Contents
-    
-      Arguments
-      Value
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -255,7 +257,7 @@ than two rows of plots are shown.
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html
index 1795e6c3..76e2eb8f 100644
--- a/docs/reference/sigma_twocomp.html
+++ b/docs/reference/sigma_twocomp.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -38,7 +42,6 @@
 
 
-
 
 
 
@@ -56,7 +59,7 @@ dependence of the measured value \(y\):" />
 
   
 
-  
+  
     
       
       
@@ -70,7 +73,7 @@ dependence of the measured value \(y\):" />
       
       
         mkin
-        0.9.49.8
+        0.9.50.1
       
     
 
@@ -114,7 +117,12 @@ dependence of the measured value \(y\):" />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -129,7 +137,7 @@ dependence of the measured value \(y\):" />
   
     
     Two-component error model
-    
+    Source: R/sigma_twocomp.R
     sigma_twocomp.Rd
     
 
@@ -173,21 +181,16 @@ y.
     References
 
     Werner, Mario, Brooks, Samuel H., and Knott, Lancaster B. (1978)
-  Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry
-  24(11), 1895-1898.
+Additive, Multiplicative, and Mixed Analytical Errors. Clinical Chemistry
+24(11), 1895-1898.
 Rocke, David M. and Lorenzato, Stefan (1995) A two-component model for
-  measurement error in analytical chemistry. Technometrics 37(2), 176-184.
+measurement error in analytical chemistry. Technometrics 37(2), 176-184.
 
   
-  
-    Contents
-    
-      Arguments
-      Value
-      Details
-      References
-    
-
+  
+    
+      Contents
+    
   
 
 
@@ -198,7 +201,7 @@ y.
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index ec79c37d..84e65787 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -41,7 +45,6 @@ some uncertainty statistics, the chi2 error levels calculated according to
 FOCUS guidance (2006) as defined therein, formation fractions, DT50 values
 and optionally the data, consisting of observed, predicted and residual
 values." />
-
 
 
 
@@ -59,7 +62,7 @@ values." />
 
   
 
-  
+  
     
       
       
@@ -73,7 +76,7 @@ values." />
       
       
         mkin
-        0.9.49.9
+        0.9.50.1
       
     
 
@@ -117,7 +120,12 @@ values." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -132,7 +140,7 @@ values." />
   
     
     Summary method for class "mkinfit"
-    
+    Source: R/summary.mkinfit.R
     summary.mkinfit.Rd
     
 
@@ -190,96 +198,97 @@ distribution
 
     The summary function returns a list with components, among others
 version, Rversion
The mkin and R versions used
-  date.fit, date.summary
The dates where the fit and the summary were
-    produced
-  diffs
The differential equations used in the model
-  use_of_ff
Was maximum or minimum use made of formation fractions
-  bpar
Optimised and backtransformed
-    parameters
-  data
The data (see Description above).
-  start
The starting values and bounds, if applicable, for optimised
-    parameters.
-  fixed
The values of fixed parameters.
-  errmin 
The chi2 error levels for
-    each observed variable.
-  bparms.ode
All backtransformed ODE
-    parameters, for use as starting parameters for related models.
-  errparms
Error model parameters.
-  ff
The estimated formation fractions derived from the fitted
-     model.
-  distimes
The DT50 and DT90 values for each observed variable.
-  SFORB
If applicable, eigenvalues of SFORB components of the model.
-  The print method is called for its side effect, i.e. printing the summary.
+date.fit, date.summary
The dates where the fit and the summary were
+produced
+diffs
The differential equations used in the model
+use_of_ff
Was maximum or minimum use made of formation fractions
+bpar
Optimised and backtransformed
+parameters
+data
The data (see Description above).
+start
The starting values and bounds, if applicable, for optimised
+parameters.
+fixed
The values of fixed parameters.
+errmin 
The chi2 error levels for
+each observed variable.
+bparms.ode
All backtransformed ODE
+parameters, for use as starting parameters for related models.
+errparms
Error model parameters.
+ff
The estimated formation fractions derived from the fitted
+model.
+distimes
The DT50 and DT90 values for each observed variable.
+SFORB
If applicable, eigenvalues of SFORB components of the model.
+The print method is called for its side effect, i.e. printing the summary.
 
     References
 
     FOCUS (2006) “Guidance Document on Estimating Persistence
-  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
+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
 
     Examples
     
-  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.49.9 
-#> R version used for fitting:       3.6.3 
-#> Date of fit:     Mon Mar 30 21:43:00 2020 
-#> Date of summary: Mon Mar 30 21:43:00 2020 
+  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.50.1 
+#> R version used for fitting:       4.0.0 
+#> Date of fit:     Tue May 12 08:07:03 2020 
+#> Date of summary: Tue May 12 08:07:03 2020 
 #> 
 #> Equations:
-#> d_parent/dt = - k_parent_sink * parent
+#> d_parent/dt = - k_parent * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted using 131 model solutions performed in 0.306 s
+#> Fitted using 131 model solutions performed in 0.026 s
 #> 
 #> Error model: Constant variance 
 #> 
 #> Error model algorithm: OLS 
 #> 
 #> Starting values for parameters to be optimised:
-#>                value   type
-#> parent_0      101.24  state
-#> k_parent_sink   0.10 deparm
+#>           value   type
+#> parent_0 101.24  state
+#> k_parent   0.10 deparm
 #> 
 #> Starting values for the transformed parameters actually optimised:
-#>                        value lower upper
-#> parent_0          101.240000  -Inf   Inf
-#> log_k_parent_sink  -2.302585  -Inf   Inf
+#>                   value lower upper
+#> parent_0     101.240000  -Inf   Inf
+#> log_k_parent  -2.302585  -Inf   Inf
 #> 
 #> Fixed parameter values:
 #> None
 #> 
+#> Results:
+#> 
+#>        AIC     BIC    logLik
+#>   55.28197 55.5203 -24.64099
+#> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
-#>                   Estimate Std. Error  Lower   Upper
-#> parent_0           109.200    3.70400 99.630 118.700
-#> log_k_parent_sink   -3.291    0.09176 -3.527  -3.055
-#> sigma                5.266    1.31600  1.882   8.649
+#>              Estimate Std. Error  Lower   Upper
+#> parent_0      109.200    3.70400 99.630 118.700
+#> log_k_parent   -3.291    0.09176 -3.527  -3.055
+#> sigma           5.266    1.31600  1.882   8.649
 #> 
 #> Parameter correlation:
-#>                    parent_0 log_k_parent_sink     sigma
-#> parent_0          1.000e+00         5.428e-01 1.642e-07
-#> log_k_parent_sink 5.428e-01         1.000e+00 2.507e-07
-#> sigma             1.642e-07         2.507e-07 1.000e+00
+#>               parent_0 log_k_parent     sigma
+#> 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.
 #> t-test (unrealistically) based on the assumption of normal distribution
 #> for estimators of untransformed parameters.
-#>                Estimate t value    Pr(>t)   Lower     Upper
-#> parent_0      109.20000   29.47 4.218e-07 99.6300 118.70000
-#> k_parent_sink   0.03722   10.90 5.650e-05  0.0294   0.04712
-#> sigma           5.26600    4.00 5.162e-03  1.8820   8.64900
+#>           Estimate t value    Pr(>t)   Lower     Upper
+#> parent_0 109.20000   29.47 4.218e-07 99.6300 118.70000
+#> k_parent   0.03722   10.90 5.650e-05  0.0294   0.04712
+#> sigma      5.26600    4.00 5.162e-03  1.8820   8.64900
 #> 
 #> FOCUS Chi2 error levels in percent:
 #>          err.min n.optim df
 #> All data   8.385       2  6
 #> parent     8.385       2  6
 #> 
-#> Resulting formation fractions:
-#>             ff
-#> parent_sink  1
-#> 
 #> Estimated disappearance times:
 #>         DT50  DT90
 #> parent 18.62 61.87
@@ -296,17 +305,10 @@ distribution
 #>   118   parent     0.39     1.351  -0.9613

 
   
-  
-    Contents
-    
-      Arguments
-      Value
-      References
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -317,7 +319,7 @@ distribution
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html
index 2a08228d..fc0ffef2 100644
--- a/docs/reference/transform_odeparms.html
+++ b/docs/reference/transform_odeparms.html
@@ -10,23 +10,27 @@
 
 
 
-
+
 
 
-
+
 
-
+
+
+
+
+
 
 
-
-
+
+
 
 
-
+
 
 
-
-
+
+
 
 
 
@@ -42,7 +46,6 @@ constants and other paramters that can only take on positive values, a
 simple log transformation is used. For compositional parameters, such as the
 formations fractions that should always sum up to 1 and can not be negative,
 the ilr transformation is used." />
-
 
 
 
@@ -60,7 +63,7 @@ the ilr transformation is used." />
 
   
 
-  
+  
     
       
       
@@ -74,7 +77,7 @@ the ilr transformation is used." />
       
       
         mkin
-        0.9.49.9
+        0.9.50.1
       
     
 
@@ -118,7 +121,12 @@ the ilr transformation is used." />
 
       
       
-        
+        
+  
+    
+     
+  
+
       
       
     

@@ -133,7 +141,7 @@ the ilr transformation is used." />
   
     
     Functions to transform and backtransform kinetic parameters for fitting
-    
+    Source: R/transform_odeparms.R
     transform_odeparms.Rd
     
 
@@ -204,7 +212,7 @@ fitting procedure.
     Value
 
     A vector of transformed or backtransformed parameters with the same
-  names as the original parameters.
+names as the original parameters.
     Details
 
     The transformation of sets of formation fractions is fragile, as it supposes
@@ -224,39 +232,28 @@ This is no problem for the internal use in mkinfit<
   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 <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.s <- summary(fit)
 # Transformed and backtransformed parameters
-print(fit.s$par, 3)
#>                   Estimate Std. Error Lower  Upper
-#> parent_0             99.60     1.5702 96.40 102.79
-#> log_k_parent_sink    -3.04     0.0763 -3.19  -2.88
-#> log_k_parent_m1      -2.98     0.0403 -3.06  -2.90
-#> log_k_m1_sink        -5.25     0.1332 -5.52  -4.98
-#> sigma                 3.13     0.3585  2.40   3.85
print(fit.s$bpar, 3)
#>               Estimate se_notrans t value   Pr(>t)    Lower    Upper
-#> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931
-#> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04103   0.0560
-#> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04678   0.0551
-#> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069
-#> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549

+print(fit.s$par, 3)
#>                Estimate Std. Error   Lower   Upper
+#> parent_0         99.598     1.5702 96.4038 102.793
+#> log_k_parent     -2.316     0.0409 -2.3988  -2.233
+#> log_k_m1         -5.248     0.1332 -5.5184  -4.977
+#> f_parent_ilr_1    0.041     0.0631 -0.0875   0.169
+#> sigma             3.126     0.3585  2.3961   3.855
print(fit.s$bpar, 3)
#>                Estimate se_notrans t value   Pr(>t)    Lower    Upper
+#> parent_0       99.59848    1.57022   63.43 2.30e-36 96.40384 102.7931
+#> k_parent        0.09870    0.00403   24.47 4.96e-23  0.09082   0.1073
+#> k_m1            0.00526    0.00070    7.51 6.16e-09  0.00401   0.0069
+#> f_parent_to_m1  0.51448    0.02230   23.07 3.10e-22  0.46912   0.5596
+#> sigma           3.12550    0.35852    8.72 2.24e-10  2.39609   3.8549

 # \dontrun{
 # Compare to the version without transforming rate parameters
-fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.2.s <- summary(fit.2)
-print(fit.2.s$par, 3)
#>               Estimate Std. Error    Lower    Upper
-#> parent_0      99.59848    1.57022 96.40384 1.03e+02
-#> k_parent_sink  0.04792    0.00365  0.04049 5.54e-02
-#> k_parent_m1    0.05078    0.00205  0.04661 5.49e-02
-#> k_m1_sink      0.00526    0.00070  0.00384 6.69e-03
-#> sigma          3.12550    0.35852  2.39609 3.85e+00
print(fit.2.s$bpar, 3)
#>               Estimate se_notrans t value   Pr(>t)    Lower    Upper
-#> parent_0      99.59848    1.57022   63.43 2.30e-36 96.40384 1.03e+02
-#> k_parent_sink  0.04792    0.00365   13.11 6.13e-15  0.04049 5.54e-02
-#> k_parent_m1    0.05078    0.00205   24.80 3.27e-23  0.04661 5.49e-02
-#> k_m1_sink      0.00526    0.00070    7.51 6.16e-09  0.00384 6.69e-03
-#> sigma          3.12550    0.35852    8.72 2.24e-10  2.39609 3.85e+00
# }
+fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Error in if (cost < cost.current) {    assign("cost.current", cost, inherits = TRUE)    if (!quiet)         cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"),             " at call ", calls, ": ", cost.current, "\n", sep = "")}: Fehlender Wert, wo TRUE/FALSE nötig ist
#> Timing stopped at: 0.002 0 0.003
fit.2.s <- summary(fit.2)
#> Error in summary(fit.2): Objekt 'fit.2' nicht gefunden
print(fit.2.s$par, 3)
#> Error in print(fit.2.s$par, 3): Objekt 'fit.2.s' nicht gefunden
print(fit.2.s$bpar, 3)
#> Error in print(fit.2.s$bpar, 3): Objekt 'fit.2.s' nicht gefunden
# }
 
 initials <- fit$start$value
 names(initials) <- rownames(fit$start)
 transformed <- fit$start_transformed$value
 names(transformed) <- rownames(fit$start_transformed)
-transform_odeparms(initials, SFO_SFO)
#>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
-#>        100.750000         -2.302585         -2.301586         -2.300587 
backtransform_odeparms(transformed, SFO_SFO)
#>      parent_0 k_parent_sink   k_parent_m1     k_m1_sink 
-#>      100.7500        0.1000        0.1001        0.1002 

+transform_odeparms(initials, SFO_SFO)
#>       parent_0   log_k_parent       log_k_m1 f_parent_ilr_1 
+#>     100.750000      -2.302585      -2.301586       0.000000 
backtransform_odeparms(transformed, SFO_SFO)
#>       parent_0       k_parent           k_m1 f_parent_to_m1 
+#>       100.7500         0.1000         0.1001         0.5000 

 # \dontrun{
 # The case of formation fractions
 SFO_SFO.ff <- mkinmod(
@@ -297,18 +294,10 @@ This is no problem for the internal use in mkinfit<
 
 

   
-  
-    Contents
-    
-      Arguments
-      Value
-      Details
-      Functions
-      Examples
-    
-
-    Author
-    Johannes Ranke
+  
+    
+      Contents
+    
   
 
 
@@ -319,7 +308,7 @@ This is no problem for the internal use in mkinfit<
 
 
 
-  Site built with pkgdown 1.4.1.
+  Site built with pkgdown 1.5.1.
 
 
       
-- 
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