From bc3825ae2d12c18ea3d3caf17eb23c93fef180b8 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 8 Oct 2020 09:31:35 +0200 Subject: Fix issues for release --- docs/dev/reference/AIC.mmkin.html | 8 +- docs/dev/reference/DFOP.solution.html | 2 +- docs/dev/reference/FOMC.solution.html | 2 +- docs/dev/reference/HS.solution.html | 2 +- docs/dev/reference/IORE.solution.html | 2 +- docs/dev/reference/SFO.solution.html | 2 +- docs/dev/reference/SFORB.solution.html | 2 +- docs/dev/reference/add_err.html | 2 +- docs/dev/reference/confint.mkinfit.html | 86 ++-- docs/dev/reference/create_deg_func.html | 16 +- docs/dev/reference/endpoints.html | 18 +- docs/dev/reference/get_deg_func.html | 2 +- docs/dev/reference/ilr.html | 2 +- docs/dev/reference/index.html | 6 - docs/dev/reference/logLik.mkinfit.html | 4 +- docs/dev/reference/logistic.solution.html | 16 +- docs/dev/reference/max_twa_parent.html | 2 +- docs/dev/reference/mccall81_245T.html | 18 +- docs/dev/reference/mkinds.html | 2 +- docs/dev/reference/mkinerrplot.html | 2 +- docs/dev/reference/mkinfit.html | 622 +++++++---------------------- docs/dev/reference/mkinmod.html | 9 +- docs/dev/reference/mkinparplot-1.png | Bin 16468 -> 16468 bytes docs/dev/reference/mkinparplot.html | 2 +- docs/dev/reference/mkinpredict.html | 6 +- docs/dev/reference/mkinresplot.html | 4 +- docs/dev/reference/mkinsub.html | 2 +- docs/dev/reference/mmkin-3.png | Bin 100817 -> 100799 bytes docs/dev/reference/mmkin-5.png | Bin 66959 -> 66958 bytes docs/dev/reference/mmkin.html | 17 +- docs/dev/reference/nafta.html | 22 +- docs/dev/reference/nlme-1.png | Bin 70555 -> 71651 bytes docs/dev/reference/nlme.html | 20 +- docs/dev/reference/nlme.mmkin.html | 97 +++-- docs/dev/reference/parms.html | 43 +- docs/dev/reference/plot.mkinfit.html | 4 +- docs/dev/reference/plot.mmkin-1.png | Bin 34273 -> 34584 bytes docs/dev/reference/plot.mmkin-2.png | Bin 34629 -> 34972 bytes docs/dev/reference/plot.mmkin-3.png | Bin 32259 -> 32445 bytes docs/dev/reference/plot.mmkin-4.png | Bin 25550 -> 25896 bytes docs/dev/reference/plot.mmkin-5.png | Bin 38129 -> 39246 bytes docs/dev/reference/plot.mmkin.html | 2 +- docs/dev/reference/plot.nlme.mmkin-2.png | Bin 35346 -> 35346 bytes docs/dev/reference/plot.nlme.mmkin.html | 5 +- docs/dev/reference/print.mkinds.html | 2 +- docs/dev/reference/sigma_twocomp.html | 2 +- docs/dev/reference/summary.mkinfit.html | 14 +- docs/dev/reference/transform_odeparms.html | 8 +- 48 files changed, 367 insertions(+), 710 deletions(-) (limited to 'docs/dev/reference') diff --git a/docs/dev/reference/AIC.mmkin.html b/docs/dev/reference/AIC.mmkin.html index 517aff12..a1418b82 100644 --- a/docs/dev/reference/AIC.mmkin.html +++ b/docs/dev/reference/AIC.mmkin.html @@ -73,7 +73,7 @@ same dataset." /> mkin - 0.9.50.3 + 0.9.50.3 @@ -192,13 +192,13 @@ dataframe if there are several fits in the 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 diff --git a/docs/dev/reference/DFOP.solution.html b/docs/dev/reference/DFOP.solution.html index 48c3aabb..e7c69fc3 100644 --- a/docs/dev/reference/DFOP.solution.html +++ b/docs/dev/reference/DFOP.solution.html @@ -73,7 +73,7 @@ two exponential decline functions." /> mkin - 0.9.50.3 + 0.9.50.3
diff --git a/docs/dev/reference/FOMC.solution.html b/docs/dev/reference/FOMC.solution.html index 8ed22157..f89f87c9 100644 --- a/docs/dev/reference/FOMC.solution.html +++ b/docs/dev/reference/FOMC.solution.html @@ -73,7 +73,7 @@ a decreasing rate constant." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/HS.solution.html b/docs/dev/reference/HS.solution.html index 5053542a..4622ac80 100644 --- a/docs/dev/reference/HS.solution.html +++ b/docs/dev/reference/HS.solution.html @@ -73,7 +73,7 @@ between them." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/IORE.solution.html b/docs/dev/reference/IORE.solution.html index 9db7447c..26a34c73 100644 --- a/docs/dev/reference/IORE.solution.html +++ b/docs/dev/reference/IORE.solution.html @@ -73,7 +73,7 @@ a concentration dependent rate constant." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/SFO.solution.html b/docs/dev/reference/SFO.solution.html index 02b3cf22..930c2a97 100644 --- a/docs/dev/reference/SFO.solution.html +++ b/docs/dev/reference/SFO.solution.html @@ -72,7 +72,7 @@ mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/SFORB.solution.html b/docs/dev/reference/SFORB.solution.html index 87d39b4e..845377a2 100644 --- a/docs/dev/reference/SFORB.solution.html +++ b/docs/dev/reference/SFORB.solution.html @@ -76,7 +76,7 @@ and no substance in the bound fraction." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/add_err.html b/docs/dev/reference/add_err.html index a4317cd7..852ae0d9 100644 --- a/docs/dev/reference/add_err.html +++ b/docs/dev/reference/add_err.html @@ -74,7 +74,7 @@ may depend on the predicted value and is specified as a standard deviation." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/confint.mkinfit.html b/docs/dev/reference/confint.mkinfit.html index 074bed3e..5b683355 100644 --- a/docs/dev/reference/confint.mkinfit.html +++ b/docs/dev/reference/confint.mkinfit.html @@ -258,15 +258,15 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,

Examples

f <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
-confint(f, method = "quadratic")
#>                     2.5%      97.5%
-#> parent_0      71.8242430 93.1600766
-#> k_parent_sink  0.2109541  0.4440528
-#> sigma          1.9778868  7.3681380

+confint(f, method = "quadratic")
#>                2.5%      97.5%
+#> parent_0 71.8242430 93.1600766
+#> k_parent  0.2109541  0.4440528
+#> sigma     1.9778868  7.3681380

 # \dontrun{
-confint(f, method = "profile")
#> Profiling the likelihood
#>                     2.5%      97.5%
-#> parent_0      73.0641834 92.1392181
-#> k_parent_sink  0.2170293  0.4235348
-#> sigma          3.1307772  8.0628314

+confint(f, method = "profile")
#> Profiling the likelihood
#>                2.5%      97.5%
+#> parent_0 73.0641834 92.1392181
+#> k_parent  0.2170293  0.4235348
+#> sigma     3.1307772  8.0628314

 # Set the number of cores for the profiling method for further examples
 if (identical(Sys.getenv("NOT_CRAN"), "true")) {
   n_cores <- parallel::detectCores() - 1
@@ -279,30 +279,29 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), quiet = TRUE)
 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))
#>    user  system elapsed 
-#>   3.610   1.071   3.378 
# Using more cores does not save much time here, as parent_0 takes up most of the time
+f_d_1 <- mkinfit(SFO_SFO, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
system.time(ci_profile <- confint(f_d_1, method = "profile", cores = 1, quiet = TRUE))
#>    user  system elapsed 
+#>   3.810   0.964   3.430 
# 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
#> Warning: scheduled cores 2, 1, 3 encountered errors in user code, all values of the jobs will be affected
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
#> Timing stopped at: 0.005 0.04 0.206
ci_profile
#>                        2.5%        97.5%
+  c("k_parent_sink", "k_parent_m1", "k_m1_sink", "sigma"), cores = n_cores))
#> Profiling the likelihood
#> Warning: scheduled cores 2, 1, 3 encountered errors in user code, all values of the jobs will be affected
#> Error in dimnames(x) <- dn: length of 'dimnames' [2] not equal to array extent
#> Timing stopped at: 0.015 0.029 0.198
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       96.403839476 1.027931e+02
+#> parent_0       96.403839460 1.027931e+02
 #> k_parent        0.090823790 1.072543e-01
 #> k_m1            0.004012216 6.897547e-03
 #> f_parent_to_m1  0.469118713 5.595960e-01
 #> sigma           2.396089689 3.854918e+00
ci_quadratic_untransformed <- confint(f_d_1, method = "quadratic", transformed = FALSE)
 ci_quadratic_untransformed
#>                        2.5%        97.5%
-#> parent_0       96.403839429 1.027931e+02
+#> parent_0       96.403839413 1.027931e+02
 #> k_parent        0.090491931 1.069035e-01
 #> k_m1            0.003835483 6.685819e-03
-#> f_parent_to_m1  0.469113364 5.598386e-01
+#> f_parent_to_m1  0.469113365 5.598386e-01
 #> sigma           2.396089689 3.854918e+00
# 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
@@ -314,7 +313,7 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 #> k_parent        TRUE  TRUE
 #> k_m1           FALSE FALSE
 #> f_parent_to_m1  TRUE FALSE
-#> sigma          FALSE  TRUE
signif(rel_diffs_transformed, 3)
#>                    2.5%    97.5%
+#> 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
@@ -327,24 +326,23 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 #> 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%
+f_d_2 <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
ci_profile_ff <- confint(f_d_2, method = "profile", cores = n_cores)
#> Profiling the likelihood
ci_profile_ff
#>                        2.5%        97.5%
 #> parent_0       96.456003640 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       96.403839476 1.027931e+02
+#> parent_0       96.403839460 1.027931e+02
 #> k_parent        0.090823790 1.072543e-01
 #> k_m1            0.004012216 6.897547e-03
 #> f_parent_to_m1  0.469118713 5.595960e-01
 #> sigma           2.396089689 3.854918e+00
ci_quadratic_untransformed_ff <- confint(f_d_2, method = "quadratic", transformed = FALSE)
 ci_quadratic_untransformed_ff
#>                        2.5%        97.5%
-#> parent_0       96.403839429 1.027931e+02
+#> parent_0       96.403839413 1.027931e+02
 #> k_parent        0.090491931 1.069035e-01
 #> k_m1            0.003835483 6.685819e-03
-#> f_parent_to_m1  0.469113364 5.598386e-01
+#> f_parent_to_m1  0.469113365 5.598386e-01
 #> sigma           2.396089689 3.854918e+00
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
@@ -356,17 +354,17 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
 #> k_parent        TRUE  TRUE
 #> k_m1           FALSE FALSE
 #> f_parent_to_m1  TRUE FALSE
-#> sigma          FALSE  TRUE
rel_diffs_transformed_ff
#>                        2.5%        97.5%
-#> parent_0       0.0005408078 0.0002217796
+#> sigma          FALSE FALSE
rel_diffs_transformed_ff
#>                        2.5%        97.5%
+#> parent_0       0.0005408080 0.0002217794
 #> k_parent       0.0009596417 0.0009003876
-#> k_m1           0.0307277372 0.0290579184
-#> f_parent_to_m1 0.0046884131 0.0027782558
-#> sigma          0.0550252516 0.0327066836
rel_diffs_untransformed_ff
#>                        2.5%       97.5%
-#> parent_0       0.0005408083 0.000221780
-#> k_parent       0.0046100096 0.002373023
-#> k_m1           0.0146746467 0.002530101
-#> f_parent_to_m1 0.0046997600 0.002346022
-#> sigma          0.0550252516 0.032706684

+#> 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       0.0005408085 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
 m_synth_DFOP_par <- mkinmod(parent = mkinsub("DFOP", c("M1", "M2")),
@@ -375,19 +373,19 @@ Profile-Likelihood Based Confidence Intervals, Applied Statistics, 37,
   use_of_ff = "max", quiet = TRUE)
 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)
#> Warning: Optimisation did not converge:
-#> iteration limit reached without convergence (10)
confint(f_tc_2, method = "quadratic")
#>                        2.5%        97.5%
-#> parent_0       95.654015524 105.79279749
-#> k_M1            0.037723773   0.04447598
-#> k_M2            0.008586438   0.01078076
-#> f_parent_to_M1  0.230403596   0.61953014
-#> f_parent_to_M2  0.162909765   0.38019017
-#> k1              0.275434628   0.33331386
-#> k2              0.018602188   0.02249211
-#> g               0.675149759   0.73520889
-#> sigma_low       0.251416929   0.84272023
-#> rsd_high        0.040371818   0.07666540
confint(f_tc_2, "parent_0", method = "quadratic")
#>              2.5%    97.5%
-#> parent_0 95.65402 105.7928
# }
+  error_model_algorithm = "direct", quiet = TRUE)
+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
# }
@@ -171,16 +171,14 @@ SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
FOCUS_D <- subset(FOCUS_2006_D, value != 0) # to avoid warnings -fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE) -fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE) -# \dontrun{ +fit_1 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
fit_2 <- mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
# \dontrun{ if (require(rbenchmark)) benchmark( analytical = mkinfit(SFO_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE), deSolve = mkinfit(SFO_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), - replications = 2)
#> Loading required package: rbenchmark
#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.422 1.000 0.421 0 0 -#> 2 deSolve 2 0.722 1.711 0.721 0 0 + replications = 2)
#> Loading required package: rbenchmark
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
#> test replications elapsed relative user.self sys.self user.child +#> 1 analytical 2 0.423 1.000 0.423 0 0 +#> 2 deSolve 2 0.716 1.693 0.715 0 0 #> sys.child #> 1 0 #> 2 0
DFOP_SFO <- mkinmod( @@ -189,8 +187,8 @@ analytical = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "analytical", quiet = TRUE), deSolve = mkinfit(DFOP_SFO, FOCUS_D, solution_type = "deSolve", quiet = TRUE), replications = 2)
#> test replications elapsed relative user.self sys.self user.child -#> 1 analytical 2 0.907 1.000 0.906 0 0 -#> 2 deSolve 2 1.659 1.829 1.658 0 0 +#> 1 analytical 2 0.910 1.000 0.909 0 0 +#> 2 deSolve 2 1.734 1.905 1.733 0 0 #> sys.child #> 1 0 #> 2 0
# } diff --git a/docs/dev/reference/endpoints.html b/docs/dev/reference/endpoints.html index 5751df93..1858e243 100644 --- a/docs/dev/reference/endpoints.html +++ b/docs/dev/reference/endpoints.html @@ -78,7 +78,7 @@ advantage that the SFORB model can also be used for metabolites." /> mkin - 0.9.50.3 + 0.9.50.3
@@ -187,18 +187,22 @@ of these SFORB models, equivalent to DFOP rate constants

#> DT50 DT90 DT50back #> parent 1.785233 15.1479 4.559973 #>
# \dontrun{ - fit_2 <- mkinfit("SFORB", FOCUS_2006_C, quiet = TRUE) - endpoints(fit_2)
#> $ff -#> parent_free_sink -#> 1 + fit_2 <- mkinfit("DFOP", FOCUS_2006_C, quiet = TRUE) + endpoints(fit_2)
#> $distimes +#> DT50 DT90 DT50back DT50_k1 DT50_k2 +#> parent 1.886925 21.25106 6.397207 1.508293 38.83438 +#>
fit_3 <- mkinfit("SFORB", FOCUS_2006_C, quiet = TRUE) + endpoints(fit_3)
#> $ff +#> parent_free +#> 1 #> #> $SFORB #> parent_b1 parent_b2 #> 0.4595574 0.0178488 #> #> $distimes -#> DT50 DT90 DT50_parent_b1 DT50_parent_b2 -#> parent 1.886925 21.25106 1.508293 38.83438 +#> DT50 DT90 DT50back DT50_parent_b1 DT50_parent_b2 +#> parent 1.886925 21.25106 6.397208 1.508293 38.83438 #>
# }
diff --git a/docs/dev/reference/get_deg_func.html b/docs/dev/reference/get_deg_func.html index 7500186b..ea0676cc 100644 --- a/docs/dev/reference/get_deg_func.html +++ b/docs/dev/reference/get_deg_func.html @@ -72,7 +72,7 @@ mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/ilr.html b/docs/dev/reference/ilr.html index 245880f2..8f58949e 100644 --- a/docs/dev/reference/ilr.html +++ b/docs/dev/reference/ilr.html @@ -73,7 +73,7 @@ transformations." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/index.html b/docs/dev/reference/index.html index f1ab22e6..b72d0c85 100644 --- a/docs/dev/reference/index.html +++ b/docs/dev/reference/index.html @@ -330,12 +330,6 @@ of an mmkin object

Helper functions to create nlme models from mmkin row objects

- -

saemix_model() saemix_data()

- -

Create saemix models from mmkin row objects

- -

get_deg_func()

diff --git a/docs/dev/reference/logLik.mkinfit.html b/docs/dev/reference/logLik.mkinfit.html index 840a4821..7ab36f0d 100644 --- a/docs/dev/reference/logLik.mkinfit.html +++ b/docs/dev/reference/logLik.mkinfit.html @@ -76,7 +76,7 @@ the error model." /> mkin - 0.9.50.3 + 0.9.50.3 @@ -193,7 +193,7 @@ and the fitted error model parameters.

parent = mkinsub("SFO", to = "m1"), m1 = mkinsub("SFO") )
#> Successfully compiled differential equation model from auto-generated C code.
d_t <- FOCUS_2006_D - f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
#> Warning: Observations with value of zero were removed from the data
f_obs <- mkinfit(sfo_sfo, d_t, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
f_tc <- mkinfit(sfo_sfo, d_t, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
AIC(f_nw, f_obs, f_tc)
#> df AIC + f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
f_obs <- mkinfit(sfo_sfo, d_t, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
f_tc <- mkinfit(sfo_sfo, d_t, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
AIC(f_nw, f_obs, f_tc)
#> df AIC #> f_nw 5 204.4486 #> f_obs 6 205.8727 #> f_tc 6 141.9656
# } diff --git a/docs/dev/reference/logistic.solution.html b/docs/dev/reference/logistic.solution.html index 4804fc73..248edcda 100644 --- a/docs/dev/reference/logistic.solution.html +++ b/docs/dev/reference/logistic.solution.html @@ -73,7 +73,7 @@ an increasing rate constant, supposedly caused by microbial growth" /> mkin - 0.9.50.3 + 0.9.50.3
@@ -232,18 +232,18 @@ Version 1.1, 18 December 2014 m <- mkinfit("logistic", d_2_1, quiet = TRUE) plot_sep(m)
summary(m)$bpar
#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 1.9023449649 55.610120 3.768361e-16 1.016451e+02 -#> kmax 6.398190e-02 0.0143201029 4.467978 3.841828e-04 3.929235e-02 -#> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846685e-08 -#> r 2.263946e-01 0.1718110773 1.317695 1.061044e-01 4.335843e-02 +#> parent_0 1.057896e+02 1.9023449703 55.610119 3.768361e-16 1.016451e+02 +#> kmax 6.398190e-02 0.0143201031 4.467978 3.841829e-04 3.929235e-02 +#> k0 1.612775e-04 0.0005866813 0.274898 3.940351e-01 5.846688e-08 +#> r 2.263946e-01 0.1718110715 1.317695 1.061044e-01 4.335843e-02 #> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper #> parent_0 109.9341588 #> kmax 0.1041853 -#> k0 0.4448750 -#> r 1.1821121 +#> k0 0.4448749 +#> r 1.1821120 #> sigma 7.3256566
endpoints(m)$distimes
#> DT50 DT90 DT50_k0 DT50_kmax -#> parent 36.86533 62.41511 4297.854 10.83349
+#> parent 36.86533 62.41511 4297.853 10.83349
diff --git a/docs/dev/reference/mccall81_245T.html b/docs/dev/reference/mccall81_245T.html index dc0dfbf8..fa352d0a 100644 --- a/docs/dev/reference/mccall81_245T.html +++ b/docs/dev/reference/mccall81_245T.html @@ -74,7 +74,7 @@ mkin - 0.9.50.3 + 0.9.50.3 @@ -178,15 +178,15 @@ phenol = list(type = "SFO", to = "anisole"), anisole = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# \dontrun{ fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(fit.1)$bpar
#> Estimate se_notrans t value Pr(>t) -#> T245_0 1.038550e+02 2.184707509 47.537272 4.472189e-18 +#> T245_0 1.038550e+02 2.184707514 47.537272 4.472189e-18 #> k_T245 4.337042e-02 0.001898397 22.845818 2.276912e-13 -#> k_phenol 4.050581e-01 0.298699410 1.356073 9.756993e-02 +#> k_phenol 4.050581e-01 0.298699428 1.356073 9.756994e-02 #> k_anisole 6.678742e-03 0.000802144 8.326114 2.623179e-07 -#> f_T245_to_phenol 6.227599e-01 0.398534147 1.562626 6.949418e-02 -#> f_phenol_to_anisole 1.000000e+00 0.671844135 1.488440 7.867793e-02 -#> sigma 2.514628e+00 0.490755933 5.123989 6.233163e-05 +#> f_T245_to_phenol 6.227599e-01 0.398534167 1.562626 6.949418e-02 +#> f_phenol_to_anisole 1.000000e+00 0.671844168 1.488440 7.867794e-02 +#> sigma 2.514628e+00 0.490755943 5.123989 6.233164e-05 #> Lower Upper -#> T245_0 99.246061427 1.084640e+02 +#> T245_0 99.246061371 1.084640e+02 #> k_T245 0.039631621 4.746194e-02 #> k_phenol 0.218013878 7.525762e-01 #> k_anisole 0.005370739 8.305299e-03 @@ -194,7 +194,7 @@ #> f_phenol_to_anisole 0.000000000 1.000000e+00 #> sigma 1.706607296 3.322649e+00
endpoints(fit.1)
#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 1.005127e-10 +#> 6.227599e-01 3.772401e-01 1.000000e+00 1.748047e-10 #> #> $distimes #> DT50 DT90 @@ -206,7 +206,7 @@ parms.ini = c(k_phenol_sink = 0), fixed_parms = "k_phenol_sink", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Initial parameter(s) k_phenol_sink not used in the model
#> Error in data.frame(value = c(state.ini.fixed, parms.fixed)): row names contain missing values
summary(fit.2)$bpar
#> Error in summary(fit.2): object 'fit.2' not found
endpoints(fit.1)
#> $ff #> T245_phenol T245_sink phenol_anisole phenol_sink -#> 6.227599e-01 3.772401e-01 1.000000e+00 1.005127e-10 +#> 6.227599e-01 3.772401e-01 1.000000e+00 1.748047e-10 #> #> $distimes #> DT50 DT90 diff --git a/docs/dev/reference/mkinds.html b/docs/dev/reference/mkinds.html index 5c7d9490..a8641375 100644 --- a/docs/dev/reference/mkinds.html +++ b/docs/dev/reference/mkinds.html @@ -75,7 +75,7 @@ provided by this package come as mkinds objects nevertheless." /> mkin - 0.9.50.3 + 0.9.50.3
diff --git a/docs/dev/reference/mkinerrplot.html b/docs/dev/reference/mkinerrplot.html index 940a6861..104d1e3a 100644 --- a/docs/dev/reference/mkinerrplot.html +++ b/docs/dev/reference/mkinerrplot.html @@ -76,7 +76,7 @@ using the argument show_errplot = TRUE." /> mkin - 0.9.50.3 + 0.9.50.3 diff --git a/docs/dev/reference/mkinfit.html b/docs/dev/reference/mkinfit.html index e1e75767..90fb26be 100644 --- a/docs/dev/reference/mkinfit.html +++ b/docs/dev/reference/mkinfit.html @@ -423,16 +423,16 @@ Degradation Data. Environments 6(12) 124 # Use shorthand notation for parent only degradation fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE) summary(fit)
#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:45 2020 -#> Date of summary: Wed May 27 07:43:45 2020 +#> R version used for fitting: 4.0.2 +#> Date of fit: Thu Oct 8 09:12:15 2020 +#> Date of summary: Thu Oct 8 09:12:15 2020 #> #> Equations: #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent #> #> Model predictions using solution type analytical #> -#> Fitted using 222 model solutions performed in 0.044 s +#> Fitted using 222 model solutions performed in 0.045 s #> #> Error model: Constant variance #> @@ -467,10 +467,10 @@ Degradation Data. Environments 6(12) 124 #> #> Parameter correlation: #> parent_0 log_alpha log_beta sigma -#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.770e-08 -#> log_alpha -1.565e-01 1.000e+00 9.564e-01 9.974e-08 -#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.468e-08 -#> sigma 4.770e-08 9.974e-08 8.468e-08 1.000e+00 +#> parent_0 1.000e+00 -1.565e-01 -3.142e-01 4.758e-08 +#> log_alpha -1.565e-01 1.000e+00 9.564e-01 1.007e-07 +#> log_beta -3.142e-01 9.564e-01 1.000e+00 8.568e-08 +#> sigma 4.758e-08 1.007e-07 8.568e-08 1.000e+00 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. @@ -503,426 +503,118 @@ Degradation Data. Environments 6(12) 124 #> 91 parent 3.9 1.441 2.4590 #> 119 parent 0.6 1.092 -0.4919
# One parent compound, one metabolite, both single first order. +# We remove zero values from FOCUS dataset D in order to avoid warnings +FOCUS_D <- subset(FOCUS_2006_D, value != 0) # Use mkinsub for convenience in model formulation. Pathway to sink included per default. SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), - m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults -print(system.time(fit <- mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "eigen", quiet = TRUE)))
#> Warning: Observations with value of zero were removed from the data
#> user system elapsed -#> 0.405 0.001 0.407
parms(fit)
#> parent_0 k_parent k_m1 f_parent_to_m1 sigma -#> 99.598481046 0.098697740 0.005260651 0.514475962 3.125503875
endpoints(fit)
#> $ff + m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
+# Fit the model quietly to the FOCUS example dataset D using defaults +fit <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0165
# Since mkin 0.9.50.3, we get a warning about non-normality of residuals, +# so we try an alternative error model +fit.tc <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc") +# This avoids the warning, and the likelihood ratio test confirms it is preferable +lrtest(fit.tc, fit)
#> Likelihood ratio test +#> +#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model const and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 6 -64.983 +#> 2 5 -97.224 -1 64.483 9.737e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
# We can also allow for different variances of parent and metabolite as error model +fit.obs <- mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "obs") +# This also avoids the warning about non-normality, but the two-component error model +# has significantly higher likelihood +lrtest(fit.obs, fit.tc)
#> Likelihood ratio test +#> +#> Model 1: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model obs and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 6 -64.983 +#> 2 6 -96.936 0 63.907 < 2.2e-16 *** +#> --- +#> Signif. codes: 0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1
parms(fit.tc)
#> parent_0 k_parent k_m1 f_parent_to_m1 sigma_low +#> 1.007343e+02 1.005562e-01 5.166712e-03 5.083933e-01 3.049891e-03 +#> rsd_high +#> 7.928117e-02
endpoints(fit.tc)
#> $ff #> parent_m1 parent_sink -#> 0.514476 0.485524 +#> 0.5083933 0.4916067 #> #> $distimes -#> DT50 DT90 -#> parent 7.022929 23.32966 -#> m1 131.760724 437.69965 -#>
# \dontrun{ -# deSolve is slower when no C compiler (gcc) was available during model generation -print(system.time(fit.deSolve <- mkinfit(SFO_SFO, FOCUS_2006_D, - solution_type = "deSolve")))
#> Warning: Observations with value of zero were removed from the data
#> Ordinary least squares optimisation
#> Sum of squared residuals at call 1: 15156.12 -#> Sum of squared residuals at call 2: 15156.12 -#> Sum of squared residuals at call 6: 8243.645 -#> Sum of squared residuals at call 12: 6290.712 -#> Sum of squared residuals at call 13: 6290.683 -#> Sum of squared residuals at call 15: 6290.452 -#> Sum of squared residuals at call 18: 1700.749 -#> Sum of squared residuals at call 20: 1700.611 -#> Sum of squared residuals at call 24: 1190.923 -#> Sum of squared residuals at call 26: 1190.922 -#> Sum of squared residuals at call 29: 1017.417 -#> Sum of squared residuals at call 31: 1017.417 -#> Sum of squared residuals at call 33: 1017.416 -#> Sum of squared residuals at call 34: 644.0472 -#> Sum of squared residuals at call 36: 644.047 -#> Sum of squared residuals at call 38: 644.047 -#> Sum of squared residuals at call 39: 590.5025 -#> Sum of squared residuals at call 41: 590.5022 -#> Sum of squared residuals at call 43: 590.5016 -#> Sum of squared residuals at call 44: 543.2196 -#> Sum of squared residuals at call 45: 543.2193 -#> Sum of squared residuals at call 46: 543.2192 -#> Sum of squared residuals at call 50: 391.348 -#> Sum of squared residuals at call 51: 391.3479 -#> Sum of squared residuals at call 56: 386.479 -#> Sum of squared residuals at call 58: 386.479 -#> Sum of squared residuals at call 60: 386.4779 -#> Sum of squared residuals at call 61: 384.0686 -#> Sum of squared residuals at call 63: 384.0686 -#> Sum of squared residuals at call 66: 382.7813 -#> Sum of squared residuals at call 68: 382.7813 -#> Sum of squared residuals at call 70: 382.7813 -#> Sum of squared residuals at call 71: 378.9273 -#> Sum of squared residuals at call 73: 378.9273 -#> Sum of squared residuals at call 75: 378.9272 -#> Sum of squared residuals at call 76: 377.4847 -#> Sum of squared residuals at call 78: 377.4846 -#> Sum of squared residuals at call 81: 375.9738 -#> Sum of squared residuals at call 83: 375.9738 -#> Sum of squared residuals at call 86: 375.3387 -#> Sum of squared residuals at call 88: 375.3387 -#> Sum of squared residuals at call 91: 374.5774 -#> Sum of squared residuals at call 93: 374.5774 -#> Sum of squared residuals at call 95: 374.5774 -#> Sum of squared residuals at call 96: 373.5438 -#> Sum of squared residuals at call 100: 373.5438 -#> Sum of squared residuals at call 102: 373.265 -#> Sum of squared residuals at call 104: 373.265 -#> Sum of squared residuals at call 107: 372.6825 -#> Sum of squared residuals at call 111: 372.6825 -#> Sum of squared residuals at call 114: 372.6356 -#> Sum of squared residuals at call 116: 372.6356 -#> Sum of squared residuals at call 119: 372.6199 -#> Sum of squared residuals at call 121: 372.6199 -#> Sum of squared residuals at call 123: 372.6199 -#> Sum of squared residuals at call 124: 372.5881 -#> Sum of squared residuals at call 126: 372.5881 -#> Sum of squared residuals at call 129: 372.5418 -#> Sum of squared residuals at call 130: 372.4866 -#> Sum of squared residuals at call 131: 372.2242 -#> Sum of squared residuals at call 132: 371.5237 -#> Sum of squared residuals at call 134: 371.5237 -#> Sum of squared residuals at call 137: 371.292 -#> Sum of squared residuals at call 139: 371.292 -#> Sum of squared residuals at call 143: 371.2256 -#> Sum of squared residuals at call 144: 371.2256 -#> Sum of squared residuals at call 146: 371.2256 -#> Sum of squared residuals at call 149: 371.2194 -#> Sum of squared residuals at call 150: 371.2147 -#> Sum of squared residuals at call 153: 371.2147 -#> Sum of squared residuals at call 155: 371.2137 -#> Sum of squared residuals at call 156: 371.2137 -#> Sum of squared residuals at call 157: 371.2137 -#> Sum of squared residuals at call 160: 371.2134 -#> Sum of squared residuals at call 164: 371.2134 -#> Sum of squared residuals at call 165: 371.2134 -#> Sum of squared residuals at call 167: 371.2134 -#> Negative log-likelihood at call 177: 97.22429
#> Optimisation successfully terminated.
#> user system elapsed -#> 0.361 0.000 0.361
parms(fit.deSolve)
#> parent_0 k_parent k_m1 f_parent_to_m1 sigma -#> 99.598480300 0.098697739 0.005260651 0.514475968 3.125503874
endpoints(fit.deSolve)
#> $ff -#> parent_m1 parent_sink -#> 0.514476 0.485524 -#> -#> $distimes -#> DT50 DT90 -#> parent 7.022929 23.32966 -#> m1 131.760721 437.69964 -#>
# } - -# Use stepwise fitting, using optimised parameters from parent only fit, FOMC +#> DT50 DT90 +#> parent 6.89313 22.89848 +#> m1 134.15635 445.65776 +#>
+# We can show a quick (only one replication) benchmark for this case, as we +# have several alternative solution methods for the model. We skip +# uncompiled deSolve, as it is so slow. More benchmarks are found in the +# benchmark vignette +# \dontrun{ +if(require(rbenchmark)) { + benchmark(replications = 1, order = "relative", columns = c("test", "relative", "elapsed"), + deSolve_compiled = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc", + solution_type = "deSolve", use_compiled = TRUE), + eigen = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc", + solution_type = "eigen"), + analytical = mkinfit(SFO_SFO, FOCUS_D, quiet = TRUE, error_model = "tc", + solution_type = "analytical")) +}
#> test relative elapsed +#> 3 analytical 1.000 0.750 +#> 1 deSolve_compiled 2.288 1.716 +#> 2 eigen 2.821 2.116
# } + +# Use stepwise fitting, using optimised parameters from parent only fit, FOMC-SFO # \dontrun{ FOMC_SFO <- mkinmod( parent = mkinsub("FOMC", "m1"), - m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults -fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
# Use starting parameters from parent only FOMC fit -fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE) -fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_2006_D, quiet = TRUE, - parms.ini = fit.FOMC$bparms.ode)
#> Warning: Observations with value of zero were removed from the data
-# Use stepwise fitting, using optimised parameters from parent only fit, SFORB -SFORB_SFO <- mkinmod( - parent = list(type = "SFORB", to = "m1", sink = TRUE), - m1 = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults -fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve", - quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
# Use starting parameters from parent only SFORB fit (not really needed in this case) -fit.SFORB = mkinfit("SFORB", FOCUS_2006_D, quiet = TRUE) -fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Initial parameter(s) k_parent_free_sink not used in the model
# } - -# \dontrun{ -# Weighted fits, including IRLS (error_model = "obs") -SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"), - m1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.noweight)
#> mkin version used for fitting: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:50 2020 -#> Date of summary: Wed May 27 07:43:50 2020 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type analytical -#> -#> Fitted using 421 model solutions performed in 0.125 s -#> -#> Error model: Constant variance -#> -#> Error model algorithm: OLS -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Results: -#> -#> AIC BIC logLik -#> 204.4486 212.6365 -97.22429 -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.60000 1.57000 96.40000 102.8000 -#> log_k_parent -2.31600 0.04087 -2.39900 -2.2330 -#> log_k_m1 -5.24800 0.13320 -5.51800 -4.9770 -#> f_parent_ilr_1 0.04096 0.06312 -0.08746 0.1694 -#> sigma 3.12600 0.35850 2.39600 3.8550 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma -#> parent_0 1.000e+00 5.174e-01 -1.688e-01 -5.471e-01 -3.190e-07 -#> log_k_parent 5.174e-01 1.000e+00 -3.263e-01 -5.426e-01 3.168e-07 -#> log_k_m1 -1.688e-01 -3.263e-01 1.000e+00 7.478e-01 -1.406e-07 -#> f_parent_ilr_1 -5.471e-01 -5.426e-01 7.478e-01 1.000e+00 -1.587e-10 -#> sigma -3.190e-07 3.168e-07 -1.406e-07 -1.587e-10 1.000e+00 -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.600000 63.430 2.298e-36 96.400000 1.028e+02 -#> k_parent 0.098700 24.470 4.955e-23 0.090820 1.073e-01 -#> k_m1 0.005261 7.510 6.165e-09 0.004012 6.898e-03 -#> f_parent_to_m1 0.514500 23.070 3.104e-22 0.469100 5.596e-01 -#> sigma 3.126000 8.718 2.235e-10 2.396000 3.855e+00 -#> -#> FOCUS Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.459 2 7 -#> m1 4.690 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5145 -#> parent_sink 0.4855 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.023 23.33 -#> m1 131.761 437.70 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.59848 -1.385e-01 -#> 0 parent 102.04 99.59848 2.442e+00 -#> 1 parent 93.50 90.23787 3.262e+00 -#> 1 parent 92.50 90.23787 2.262e+00 -#> 3 parent 63.23 74.07319 -1.084e+01 -#> 3 parent 68.99 74.07319 -5.083e+00 -#> 7 parent 52.32 49.91206 2.408e+00 -#> 7 parent 55.13 49.91206 5.218e+00 -#> 14 parent 27.27 25.01257 2.257e+00 -#> 14 parent 26.64 25.01257 1.627e+00 -#> 21 parent 11.50 12.53462 -1.035e+00 -#> 21 parent 11.64 12.53462 -8.946e-01 -#> 35 parent 2.85 3.14787 -2.979e-01 -#> 35 parent 2.91 3.14787 -2.379e-01 -#> 50 parent 0.69 0.71624 -2.624e-02 -#> 50 parent 0.63 0.71624 -8.624e-02 -#> 75 parent 0.05 0.06074 -1.074e-02 -#> 75 parent 0.06 0.06074 -7.381e-04 -#> 1 m1 4.84 4.80296 3.704e-02 -#> 1 m1 5.64 4.80296 8.370e-01 -#> 3 m1 12.91 13.02400 -1.140e-01 -#> 3 m1 12.96 13.02400 -6.400e-02 -#> 7 m1 22.97 25.04476 -2.075e+00 -#> 7 m1 24.47 25.04476 -5.748e-01 -#> 14 m1 41.69 36.69002 5.000e+00 -#> 14 m1 33.21 36.69002 -3.480e+00 -#> 21 m1 44.37 41.65310 2.717e+00 -#> 21 m1 46.44 41.65310 4.787e+00 -#> 35 m1 41.22 43.31312 -2.093e+00 -#> 35 m1 37.95 43.31312 -5.363e+00 -#> 50 m1 41.19 41.21831 -2.831e-02 -#> 50 m1 40.01 41.21831 -1.208e+00 -#> 75 m1 40.09 36.44703 3.643e+00 -#> 75 m1 33.85 36.44703 -2.597e+00 -#> 100 m1 31.04 31.98163 -9.416e-01 -#> 100 m1 33.13 31.98163 1.148e+00 -#> 120 m1 25.15 28.78984 -3.640e+00 -#> 120 m1 33.31 28.78984 4.520e+00
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: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:51 2020 -#> Date of summary: Wed May 27 07:43:51 2020 + m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0499
# Again, we get a warning and try a more sophisticated error model +fit.FOMC_SFO.tc <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, error_model = "tc") +# This model has a higher likelihood, but not significantly so +lrtest(fit.tc, fit.FOMC_SFO.tc)
#> Likelihood ratio test +#> +#> Model 1: FOMC_SFO with error model tc and fixed parameter(s) m1_0 +#> Model 2: SFO_SFO with error model tc and fixed parameter(s) m1_0 +#> #Df LogLik Df Chisq Pr(>Chisq) +#> 1 7 -64.829 +#> 2 6 -64.983 -1 0.3075 0.5792
# Also, the missing standard error for log_beta and the t-tests for alpha +# and beta indicate overparameterisation +summary(fit.FOMC_SFO.tc, data = FALSE)
#> Warning: NaNs produced
#> Warning: NaNs produced
#> Warning: diag(.) had 0 or NA entries; non-finite result is doubtful
#> mkin version used for fitting: 0.9.50.3 +#> R version used for fitting: 4.0.2 +#> Date of fit: Thu Oct 8 09:12:29 2020 +#> Date of summary: Thu Oct 8 09:12:29 2020 #> #> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 -#> -#> Model predictions using solution type analytical -#> -#> Fitted using 978 model solutions performed in 0.413 s -#> -#> Error model: Variance unique to each observed variable -#> -#> Error model algorithm: d_3 -#> Direct fitting and three-step fitting yield approximately the same likelihood -#> -#> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> sigma_parent 3.0000 error -#> sigma_m1 3.0000 error -#> -#> Starting values for the transformed parameters actually optimised: -#> value lower upper -#> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf -#> f_parent_ilr_1 0.000000 -Inf Inf -#> sigma_parent 3.000000 0 Inf -#> sigma_m1 3.000000 0 Inf -#> -#> Fixed parameter values: -#> value type -#> m1_0 0 state -#> -#> Results: -#> -#> AIC BIC logLik -#> 205.8727 215.6982 -96.93634 -#> -#> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 99.65000 1.70200 96.19000 103.1000 -#> log_k_parent -2.31300 0.04376 -2.40200 -2.2240 -#> log_k_m1 -5.25000 0.12430 -5.50400 -4.9970 -#> f_parent_ilr_1 0.03861 0.06171 -0.08708 0.1643 -#> sigma_parent 3.40100 0.56820 2.24400 4.5590 -#> sigma_m1 2.85500 0.45240 1.93400 3.7770 -#> -#> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_parent -#> parent_0 1.00000 0.51078 -0.19133 -0.59997 0.035670 -#> log_k_parent 0.51078 1.00000 -0.37458 -0.59239 0.069833 -#> log_k_m1 -0.19133 -0.37458 1.00000 0.74398 -0.026158 -#> f_parent_ilr_1 -0.59997 -0.59239 0.74398 1.00000 -0.041369 -#> sigma_parent 0.03567 0.06983 -0.02616 -0.04137 1.000000 -#> sigma_m1 -0.03385 -0.06627 0.02482 0.03926 -0.004628 -#> sigma_m1 -#> parent_0 -0.033847 -#> log_k_parent -0.066265 -#> log_k_m1 0.024823 -#> f_parent_ilr_1 0.039256 -#> sigma_parent -0.004628 -#> sigma_m1 1.000000 -#> -#> Backtransformed parameters: -#> Confidence intervals for internally transformed parameters are asymmetric. -#> t-test (unrealistically) based on the assumption of normal distribution -#> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 99.650000 58.560 2.004e-34 96.190000 1.031e+02 -#> k_parent 0.098970 22.850 1.099e-21 0.090530 1.082e-01 -#> k_m1 0.005245 8.046 1.732e-09 0.004072 6.756e-03 -#> f_parent_to_m1 0.513600 23.560 4.352e-22 0.469300 5.578e-01 -#> sigma_parent 3.401000 5.985 5.662e-07 2.244000 4.559e+00 -#> sigma_m1 2.855000 6.311 2.215e-07 1.934000 3.777e+00 -#> -#> FOCUS Chi2 error levels in percent: -#> err.min n.optim df -#> All data 6.398 4 15 -#> parent 6.464 2 7 -#> m1 4.682 2 8 -#> -#> Resulting formation fractions: -#> ff -#> parent_m1 0.5136 -#> parent_sink 0.4864 -#> -#> Estimated disappearance times: -#> DT50 DT90 -#> parent 7.003 23.26 -#> m1 132.154 439.01 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 99.65417 -1.942e-01 -#> 0 parent 102.04 99.65417 2.386e+00 -#> 1 parent 93.50 90.26332 3.237e+00 -#> 1 parent 92.50 90.26332 2.237e+00 -#> 3 parent 63.23 74.05306 -1.082e+01 -#> 3 parent 68.99 74.05306 -5.063e+00 -#> 7 parent 52.32 49.84325 2.477e+00 -#> 7 parent 55.13 49.84325 5.287e+00 -#> 14 parent 27.27 24.92971 2.340e+00 -#> 14 parent 26.64 24.92971 1.710e+00 -#> 21 parent 11.50 12.46890 -9.689e-01 -#> 21 parent 11.64 12.46890 -8.289e-01 -#> 35 parent 2.85 3.11925 -2.692e-01 -#> 35 parent 2.91 3.11925 -2.092e-01 -#> 50 parent 0.69 0.70679 -1.679e-02 -#> 50 parent 0.63 0.70679 -7.679e-02 -#> 75 parent 0.05 0.05952 -9.523e-03 -#> 75 parent 0.06 0.05952 4.772e-04 -#> 1 m1 4.84 4.81075 2.925e-02 -#> 1 m1 5.64 4.81075 8.292e-01 -#> 3 m1 12.91 13.04196 -1.320e-01 -#> 3 m1 12.96 13.04196 -8.196e-02 -#> 7 m1 22.97 25.06847 -2.098e+00 -#> 7 m1 24.47 25.06847 -5.985e-01 -#> 14 m1 41.69 36.70308 4.987e+00 -#> 14 m1 33.21 36.70308 -3.493e+00 -#> 21 m1 44.37 41.65115 2.719e+00 -#> 21 m1 46.44 41.65115 4.789e+00 -#> 35 m1 41.22 43.29465 -2.075e+00 -#> 35 m1 37.95 43.29465 -5.345e+00 -#> 50 m1 41.19 41.19948 -9.479e-03 -#> 50 m1 40.01 41.19948 -1.189e+00 -#> 75 m1 40.09 36.44035 3.650e+00 -#> 75 m1 33.85 36.44035 -2.590e+00 -#> 100 m1 31.04 31.98773 -9.477e-01 -#> 100 m1 33.13 31.98773 1.142e+00 -#> 120 m1 25.15 28.80429 -3.654e+00 -#> 120 m1 33.31 28.80429 4.506e+00
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: 0.9.50.3 -#> R version used for fitting: 4.0.0 -#> Date of fit: Wed May 27 07:43:52 2020 -#> Date of summary: Wed May 27 07:43:52 2020 -#> -#> Equations: -#> d_parent/dt = - k_parent * parent -#> d_m1/dt = + f_parent_to_m1 * k_parent * parent - k_m1 * m1 +#> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent +#> d_m1/dt = + f_parent_to_m1 * (alpha/beta) * 1/((time/beta) + 1) * +#> parent - k_m1 * m1 #> -#> Model predictions using solution type analytical +#> Model predictions using solution type deSolve #> -#> Fitted using 2088 model solutions performed in 0.714 s +#> Fitted using 3611 model solutions performed in 2.61 s #> #> Error model: Two-component variance function #> #> Error model algorithm: d_3 -#> Direct fitting and three-step fitting yield approximately the same likelihood +#> Three-step fitting yielded a higher likelihood than direct fitting #> #> Starting values for parameters to be optimised: -#> value type -#> parent_0 100.7500 state -#> k_parent 0.1000 deparm -#> k_m1 0.1001 deparm -#> f_parent_to_m1 0.5000 deparm -#> sigma_low 0.1000 error -#> rsd_high 0.1000 error +#> value type +#> parent_0 100.75 state +#> alpha 1.00 deparm +#> beta 10.00 deparm +#> k_m1 0.10 deparm +#> f_parent_to_m1 0.50 deparm +#> sigma_low 0.10 error +#> rsd_high 0.10 error #> #> Starting values for the transformed parameters actually optimised: #> value lower upper #> parent_0 100.750000 -Inf Inf -#> log_k_parent -2.302585 -Inf Inf -#> log_k_m1 -2.301586 -Inf Inf +#> log_k_m1 -2.302585 -Inf Inf #> f_parent_ilr_1 0.000000 -Inf Inf +#> log_alpha 0.000000 -Inf Inf +#> log_beta 2.302585 -Inf Inf #> sigma_low 0.100000 0 Inf #> rsd_high 0.100000 0 Inf #> @@ -932,98 +624,70 @@ Degradation Data. Environments 6(12) 124 #> #> Results: #> -#> AIC BIC logLik -#> 141.9656 151.7911 -64.98278 +#> AIC BIC logLik +#> 143.658 155.1211 -64.82902 #> #> Optimised, transformed parameters with symmetric confidence intervals: -#> Estimate Std. Error Lower Upper -#> parent_0 100.70000 2.621000 95.400000 106.10000 -#> log_k_parent -2.29700 0.008862 -2.315000 -2.27900 -#> log_k_m1 -5.26600 0.091310 -5.452000 -5.08000 -#> f_parent_ilr_1 0.02374 0.055300 -0.088900 0.13640 -#> sigma_low 0.00305 0.004829 -0.006786 0.01289 -#> rsd_high 0.07928 0.009418 0.060100 0.09847 +#> Estimate Std. Error Lower Upper +#> parent_0 101.600000 2.6390000 96.240000 107.000000 +#> log_k_m1 -5.284000 0.0928900 -5.473000 -5.095000 +#> f_parent_ilr_1 0.001008 0.0541900 -0.109500 0.111500 +#> log_alpha 5.522000 0.0077300 5.506000 5.538000 +#> log_beta 7.806000 NaN NaN NaN +#> sigma_low 0.002488 0.0002431 0.001992 0.002984 +#> rsd_high 0.079210 0.0093280 0.060180 0.098230 #> #> Parameter correlation: -#> parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_low rsd_high -#> parent_0 1.00000 0.67644 -0.10215 -0.76822 0.14294 -0.08783 -#> log_k_parent 0.67644 1.00000 -0.15102 -0.59491 0.34611 -0.08125 -#> log_k_m1 -0.10215 -0.15102 1.00000 0.51808 -0.05236 0.01240 -#> f_parent_ilr_1 -0.76822 -0.59491 0.51808 1.00000 -0.13900 0.03248 -#> sigma_low 0.14294 0.34611 -0.05236 -0.13900 1.00000 -0.16546 -#> rsd_high -0.08783 -0.08125 0.01240 0.03248 -0.16546 1.00000 +#> parent_0 log_k_m1 f_parent_ilr_1 log_alpha log_beta sigma_low +#> parent_0 1.000000 -0.094697 -0.76654 0.70525 NaN 0.016099 +#> log_k_m1 -0.094697 1.000000 0.51404 -0.14347 NaN 0.001576 +#> f_parent_ilr_1 -0.766543 0.514038 1.00000 -0.61368 NaN 0.015465 +#> log_alpha 0.705247 -0.143468 -0.61368 1.00000 NaN 5.871780 +#> log_beta NaN NaN NaN NaN 1 NaN +#> sigma_low 0.016099 0.001576 0.01546 5.87178 NaN 1.000000 +#> rsd_high 0.006566 -0.011662 -0.05353 0.04845 NaN -0.652554 +#> rsd_high +#> parent_0 0.006566 +#> log_k_m1 -0.011662 +#> f_parent_ilr_1 -0.053525 +#> log_alpha 0.048451 +#> log_beta NaN +#> sigma_low -0.652554 +#> rsd_high 1.000000 #> #> Backtransformed parameters: #> Confidence intervals for internally transformed parameters are asymmetric. #> t-test (unrealistically) based on the assumption of normal distribution #> for estimators of untransformed parameters. -#> Estimate t value Pr(>t) Lower Upper -#> parent_0 1.007e+02 38.4300 1.180e-28 95.400000 1.061e+02 -#> k_parent 1.006e-01 112.8000 1.718e-43 0.098760 1.024e-01 -#> k_m1 5.167e-03 10.9500 1.171e-12 0.004290 6.223e-03 -#> f_parent_to_m1 5.084e-01 26.0100 2.146e-23 0.468600 5.481e-01 -#> sigma_low 3.050e-03 0.6314 2.661e-01 -0.006786 1.289e-02 -#> rsd_high 7.928e-02 8.4170 6.418e-10 0.060100 9.847e-02 +#> Estimate t value Pr(>t) Lower Upper +#> parent_0 1.016e+02 32.7800 6.312e-26 9.624e+01 1.070e+02 +#> k_m1 5.072e-03 10.1200 1.216e-11 4.197e-03 6.130e-03 +#> f_parent_to_m1 5.004e-01 20.8300 4.318e-20 4.614e-01 5.394e-01 +#> alpha 2.502e+02 0.5624 2.889e-01 2.463e+02 2.542e+02 +#> beta 2.455e+03 0.5549 2.915e-01 NA NA +#> sigma_low 2.488e-03 0.4843 3.158e-01 1.992e-03 2.984e-03 +#> rsd_high 7.921e-02 8.4300 8.001e-10 6.018e-02 9.823e-02 #> #> FOCUS Chi2 error levels in percent: #> err.min n.optim df -#> All data 6.475 4 15 -#> parent 6.573 2 7 -#> m1 4.671 2 8 +#> All data 6.781 5 14 +#> parent 7.141 3 6 +#> m1 4.640 2 8 #> #> Resulting formation fractions: #> ff -#> parent_m1 0.5084 -#> parent_sink 0.4916 +#> parent_m1 0.5004 +#> parent_sink 0.4996 #> #> Estimated disappearance times: -#> DT50 DT90 -#> parent 6.893 22.9 -#> m1 134.156 445.7 -#> -#> Data: -#> time variable observed predicted residual -#> 0 parent 99.46 100.73434 -1.274340 -#> 0 parent 102.04 100.73434 1.305660 -#> 1 parent 93.50 91.09751 2.402486 -#> 1 parent 92.50 91.09751 1.402486 -#> 3 parent 63.23 74.50141 -11.271410 -#> 3 parent 68.99 74.50141 -5.511410 -#> 7 parent 52.32 49.82880 2.491200 -#> 7 parent 55.13 49.82880 5.301200 -#> 14 parent 27.27 24.64809 2.621908 -#> 14 parent 26.64 24.64809 1.991908 -#> 21 parent 11.50 12.19232 -0.692315 -#> 21 parent 11.64 12.19232 -0.552315 -#> 35 parent 2.85 2.98327 -0.133266 -#> 35 parent 2.91 2.98327 -0.073266 -#> 50 parent 0.69 0.66013 0.029874 -#> 50 parent 0.63 0.66013 -0.030126 -#> 75 parent 0.05 0.05344 -0.003438 -#> 75 parent 0.06 0.05344 0.006562 -#> 1 m1 4.84 4.88645 -0.046451 -#> 1 m1 5.64 4.88645 0.753549 -#> 3 m1 12.91 13.22867 -0.318669 -#> 3 m1 12.96 13.22867 -0.268669 -#> 7 m1 22.97 25.36417 -2.394166 -#> 7 m1 24.47 25.36417 -0.894166 -#> 14 m1 41.69 37.00974 4.680263 -#> 14 m1 33.21 37.00974 -3.799737 -#> 21 m1 44.37 41.90133 2.468669 -#> 21 m1 46.44 41.90133 4.538669 -#> 35 m1 41.22 43.45691 -2.236913 -#> 35 m1 37.95 43.45691 -5.506913 -#> 50 m1 41.19 41.34199 -0.151985 -#> 50 m1 40.01 41.34199 -1.331985 -#> 75 m1 40.09 36.61471 3.475295 -#> 75 m1 33.85 36.61471 -2.764705 -#> 100 m1 31.04 32.20082 -1.160823 -#> 100 m1 33.13 32.20082 0.929177 -#> 120 m1 25.15 29.04130 -3.891304 -#> 120 m1 33.31 29.04130 4.268696
# } - - -
+#> DT50 DT90 DT50back +#> parent 6.812 22.7 6.834 +#> m1 136.661 454.0 NA
+# We can easily use starting parameters from the parent only fit (only for illustration) +fit.FOMC = mkinfit("FOMC", FOCUS_2006_D, quiet = TRUE, error_model = "tc") +fit.FOMC_SFO <- mkinfit(FOMC_SFO, FOCUS_D, quiet = TRUE, + parms.ini = fit.FOMC$bparms.ode, error_model = "tc") +# }
#> Compilation argument: -#> /usr/lib/R/bin/R CMD SHLIB file15d25f6867c9.c 2> file15d25f6867c9.c.err.txt +#> /usr/lib/R/bin/R CMD SHLIB file306f74383fd2.c 2> file306f74383fd2.c.err.txt #> Program source: #> 1: #include <R.h> #> 2: @@ -301,7 +301,7 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

#> }) #> return(predicted) #> } -#> <environment: 0x5555576161e0>
+#> <environment: 0x55555ad56ea0>
# If we have several parallel metabolites # (compare tests/testthat/test_synthetic_data_for_UBA_2014.R) m_synth_DFOP_par <- mkinmod( @@ -312,8 +312,9 @@ Evaluating and Calculating Degradation Kinetics in Environmental Media

fit_DFOP_par_c <- mkinfit(m_synth_DFOP_par, synthetic_data_for_UBA_2014[[12]]$data, - quiet = TRUE) -# }
+ quiet = TRUE)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.000174
# } + +
diff --git a/docs/dev/reference/mkinpredict.html b/docs/dev/reference/mkinpredict.html index 6d83e56f..15699c02 100644 --- a/docs/dev/reference/mkinpredict.html +++ b/docs/dev/reference/mkinpredict.html @@ -74,7 +74,7 @@ kinetic parameters and initial values for the state variables." /> mkin - 0.9.50.3 + 0.9.50.3 @@ -401,9 +401,9 @@ solver is used.

solution_type = "analytical", use_compiled = FALSE)[201,]) }
#> test relative elapsed #> 2 deSolve_compiled 1.0 0.005 -#> 4 analytical 1.0 0.005 +#> 4 analytical 1.8 0.009 #> 1 eigen 4.0 0.020 -#> 3 deSolve 45.6 0.228
+#> 3 deSolve 44.6 0.223
# \dontrun{ # Predict from a fitted model f <- mkinfit(SFO_SFO, FOCUS_2006_C, quiet = TRUE) diff --git a/docs/dev/reference/mkinresplot.html b/docs/dev/reference/mkinresplot.html index 11e0914e..5591d26f 100644 --- a/docs/dev/reference/mkinresplot.html +++ b/docs/dev/reference/mkinresplot.html @@ -75,7 +75,7 @@ argument show_residuals = TRUE." /> mkin - 0.9.50.3 + 0.9.50.3
@@ -243,7 +243,7 @@ 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")

+model <- mkinmod(parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(model, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
mkinresplot(fit, "m1")
diff --git a/docs/dev/reference/mmkin-3.png b/docs/dev/reference/mmkin-3.png index e80448ab..bce34531 100644 Binary files a/docs/dev/reference/mmkin-3.png and b/docs/dev/reference/mmkin-3.png differ diff --git a/docs/dev/reference/mmkin-5.png b/docs/dev/reference/mmkin-5.png index 4c771bc9..56750342 100644 Binary files a/docs/dev/reference/mmkin-5.png and b/docs/dev/reference/mmkin-5.png differ diff --git a/docs/dev/reference/mmkin.html b/docs/dev/reference/mmkin.html index 3daf16e1..a5d7ba42 100644 --- a/docs/dev/reference/mmkin.html +++ b/docs/dev/reference/mmkin.html @@ -196,8 +196,9 @@ for parallel execution.

Value

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

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

See also

[.mmkin for subsetting, plot.mmkin for @@ -218,19 +219,19 @@ plotting.

time_default <- system.time(fits.0 <- mmkin(models, datasets, quiet = TRUE)) time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE))
#> Warning: Optimisation did not converge: -#> false convergence (8)
+#> false convergence (8)
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0117
#> Warning: Shapiro-Wilk test for standardized residuals: p = 0.0174
time_default
#> user system elapsed -#> 4.499 0.456 1.983
time_1
#> user system elapsed -#> 5.771 0.003 5.777
+#> 4.500 0.399 1.311
time_1
#> user system elapsed +#> 5.154 0.008 5.165
endpoints(fits.0[["SFO_lin", 2]])
#> $ff #> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340479 0.2659521 0.7505687 0.2494313 +#> 0.7340478 0.2659522 0.7505691 0.2494309 #> #> $distimes #> DT50 DT90 #> parent 0.8777688 2.915885 -#> M1 2.3257457 7.725960 -#> M2 33.7200848 112.015697 +#> M1 2.3257466 7.725963 +#> M2 33.7200800 112.015681 #>
# plot.mkinfit handles rows or columns of mmkin result objects plot(fits.0[1, ])
plot(fits.0[1, ], obs_var = c("M1", "M2"))
plot(fits.0[, 1])
# Use double brackets to extract a single mkinfit object, which will be plotted diff --git a/docs/dev/reference/nafta.html b/docs/dev/reference/nafta.html index fe802c1b..690e4827 100644 --- a/docs/dev/reference/nafta.html +++ b/docs/dev/reference/nafta.html @@ -76,7 +76,7 @@ order of increasing model complexity, i.e. SFO, then IORE, and finally DFOP." /> mkin - 0.9.50.3 + 0.9.50.3
@@ -214,7 +214,7 @@ list element "data" contains the dataset used in the fits.

Examples


-  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
#> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
#> The representative half-life of the IORE model is longer than the one corresponding
#> to the terminal degradation rate found with the DFOP model.
#> The representative half-life obtained from the DFOP model may be used
  print(nafta_evaluation)
#> Sums of squares:
+  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00192
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00258
#> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
#> The representative half-life of the IORE model is longer than the one corresponding
#> to the terminal degradation rate found with the DFOP model.
#> The representative half-life obtained from the DFOP model may be used
  print(nafta_evaluation)
#> Sums of squares:
 #>       SFO      IORE      DFOP 
 #> 1378.6832  615.7730  517.8836 
 #> 
@@ -223,17 +223,17 @@ list element "data" contains the dataset used in the fits.

 #> 
 #> Parameters:
 #> $SFO
-#>               Estimate   Pr(>t)    Lower   Upper
-#> parent_0       83.7558 1.80e-14 77.18268 90.3288
-#> k_parent_sink   0.0017 7.43e-05  0.00112  0.0026
-#> sigma           8.7518 1.22e-05  5.64278 11.8608
+#>          Estimate   Pr(>t)    Lower   Upper
+#> parent_0  83.7558 1.80e-14 77.18268 90.3288
+#> k_parent   0.0017 7.43e-05  0.00112  0.0026
+#> sigma      8.7518 1.22e-05  5.64278 11.8608
 #> 
 #> $IORE
-#>                     Estimate Pr(>t)    Lower    Upper
-#> parent_0            9.69e+01     NA 8.88e+01 1.05e+02
-#> k__iore_parent_sink 8.40e-14     NA 1.79e-18 3.94e-09
-#> N_parent            6.68e+00     NA 4.19e+00 9.17e+00
-#> sigma               5.85e+00     NA 3.76e+00 7.94e+00
+#>                Estimate Pr(>t)    Lower    Upper
+#> parent_0       9.69e+01     NA 8.88e+01 1.05e+02
+#> k__iore_parent 8.40e-14     NA 1.79e-18 3.94e-09
+#> N_parent       6.68e+00     NA 4.19e+00 9.17e+00
+#> sigma          5.85e+00     NA 3.76e+00 7.94e+00
 #> 
 #> $DFOP
 #>          Estimate   Pr(>t)    Lower    Upper
diff --git a/docs/dev/reference/nlme-1.png b/docs/dev/reference/nlme-1.png
index 68ccb43f..8db1f999 100644
Binary files a/docs/dev/reference/nlme-1.png and b/docs/dev/reference/nlme-1.png differ
diff --git a/docs/dev/reference/nlme.html b/docs/dev/reference/nlme.html
index 28a9f0a5..af5a151a 100644
--- a/docs/dev/reference/nlme.html
+++ b/docs/dev/reference/nlme.html
@@ -225,28 +225,28 @@ nlme for the case of a single grouping variable ds.

 #>   Model: value ~ nlme_f(name, time, parent_0, log_k_parent_sink) 
 #>  Data: grouped_data 
 #>        AIC      BIC    logLik
-#>   298.2781 307.7372 -144.1391
+#>   252.7798 262.1358 -121.3899
 #> 
 #> Random effects:
 #>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>          parent_0 log_k_parent_sink Residual
-#> StdDev: 0.9374809         0.7098104 3.835429
+#>             parent_0 log_k_parent_sink Residual
+#> StdDev: 0.0006768135         0.6800777 2.489397
 #> 
 #> Fixed effects: parent_0 + log_k_parent_sink ~ 1 
-#>                       Value Std.Error DF  t-value p-value
-#> parent_0          101.76838 1.1445465 45 88.91589       0
-#> log_k_parent_sink  -3.05444 0.4195622 45 -7.28008       0
+#>                       Value Std.Error DF   t-value p-value
+#> parent_0          101.74884 0.6456014 44 157.60321       0
+#> log_k_parent_sink  -3.05575 0.4015811 44  -7.60929       0
 #>  Correlation: 
 #>                   prnt_0
-#> log_k_parent_sink 0.034 
+#> log_k_parent_sink 0.026 
 #> 
 #> Standardized Within-Group Residuals:
-#>         Min          Q1         Med          Q3         Max 
-#> -2.61693660 -0.21853517  0.05740766  0.57209378  3.04598387 
+#>        Min         Q1        Med         Q3        Max 
+#> -2.1317488 -0.6878121  0.0828385  0.8592270  2.9529864 
 #> 
-#> Number of Observations: 49
+#> Number of Observations: 48
 #> Number of Groups: 3 
plot(augPred(m_nlme, level = 0:1), layout = c(3, 1))
# augPred does not seem to work on fits with more than one state
 # variable
 
diff --git a/docs/dev/reference/nlme.mmkin.html b/docs/dev/reference/nlme.mmkin.html
index c7db9c23..16df54af 100644
--- a/docs/dev/reference/nlme.mmkin.html
+++ b/docs/dev/reference/nlme.mmkin.html
@@ -262,45 +262,44 @@ with additional elements

     
Examples

     
ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
-library(nlme)
+f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
library(nlme)
 endpoints(f[[1]])
#> $distimes
 #>            DT50     DT90
 #> parent 11.96183 39.73634
 #> 
f_nlme <- nlme(f)
 print(f_nlme)
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) 
+#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) 
 #>   Data: "Not shown" 
 #>   Log-likelihood: -307.5269
-#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
-#>          parent_0 log_k_parent_sink 
-#>         85.540979         -3.229602 
+#>   Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) 
+#>     parent_0 log_k_parent 
+#>    85.541149    -3.229596 
 #> 
 #> Random effects:
-#>  Formula: list(parent_0 ~ 1, log_k_parent_sink ~ 1)
+#>  Formula: list(parent_0 ~ 1, log_k_parent ~ 1)
 #>  Level: ds
 #>  Structure: Diagonal
-#>         parent_0 log_k_parent_sink Residual
-#> StdDev: 1.308245          1.288586 6.304923
+#>         parent_0 log_k_parent Residual
+#> StdDev:  1.30857     1.288591 6.304906
 #> 
 #> Number of Observations: 90
 #> Number of Groups: 5 
endpoints(f_nlme)
#> $distimes
 #>            DT50     DT90
-#> parent 17.51556 58.18543
+#> parent 17.51545 58.18505
 #> 
# \dontrun{
   f_nlme_2 <- nlme(f, start = c(parent_0 = 100, log_k_parent_sink = 0.1))
   update(f_nlme_2, random = parent_0 ~ 1)
#> Nonlinear mixed-effects model fit by maximum likelihood
-#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent_sink) 
+#>   Model: value ~ (mkin::get_deg_func())(name, time, parent_0, log_k_parent) 
 #>   Data: "Not shown" 
 #>   Log-likelihood: -404.3729
-#>   Fixed: list(parent_0 ~ 1, log_k_parent_sink ~ 1) 
-#>          parent_0 log_k_parent_sink 
-#>         75.933480         -3.555983 
+#>   Fixed: list(parent_0 ~ 1, log_k_parent ~ 1) 
+#>     parent_0 log_k_parent 
+#>    75.933480    -3.555983 
 #> 
 #> Random effects:
 #>  Formula: parent_0 ~ 1 | ds
 #>            parent_0 Residual
-#> StdDev: 0.002416802 21.63027
+#> StdDev: 0.002416792 21.63027
 #> 
 #> Number of Observations: 90
 #> Number of Groups: 5 
  # Test on some real data
@@ -332,88 +331,88 @@ with additional elements

   f_nlme_fomc_sfo <- nlme(f_2["FOMC-SFO", ],
     control = list(pnlsMaxIter = 100, tolerance = 1e-4), verbose = TRUE)
#> 
 #> **Iteration 1
-#> LME step: Loglik: -394.1603, nlminb iterations: 2
+#> LME step: Loglik: -394.1603, nlminb iterations: 3
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5 
-#> -0.2079863  0.8563823  1.7454253  1.0917707  1.2756955 
+#> -0.2079793  0.8563830  1.7454105  1.0917354  1.2756825 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.8814 
-#>  fixed effects: 94.17379  -5.473189  -0.6970234  -0.202509  2.103883  
+#> PNLS step: RSS =  643.8803 
+#>  fixed effects: 94.17379  -5.473193  -0.6970236  -0.2025091  2.103883  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>     fixed  reStruct 
-#> 0.7959873 0.1447512 
+#> 0.7960134 0.1447728 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -396.3824, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -1.712406e-01 -2.278541e-05  1.842120e+00  1.073975e+00  1.322924e+00 
+#> -1.712404e-01 -2.432655e-05  1.842120e+00  1.073975e+00  1.322925e+00 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.8025 
-#>  fixed effects: 94.17385  -5.473491  -0.6970406  -0.2025139  2.103871  
+#> PNLS step: RSS =  643.8035 
+#>  fixed effects: 94.17385  -5.473487  -0.6970404  -0.2025137  2.103871  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
-#>       fixed    reStruct 
-#> 5.51758e-05 1.26861e-03 
+#>        fixed     reStruct 
+#> 5.382757e-05 1.236667e-03 
 #> 
 #> **Iteration 3
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -0.1712500923 -0.0001515734  1.8420972550  1.0739796967  1.3229177241 
+#> -0.1712499044 -0.0001499831  1.8420971364  1.0739799123  1.3229167796 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7941 
-#>  fixed effects: 94.17386  -5.473523  -0.6970424  -0.2025146  2.103869  
+#> PNLS step: RSS =  643.7948 
+#>  fixed effects: 94.17386  -5.473521  -0.6970422  -0.2025144  2.10387  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>        fixed     reStruct 
-#> 5.792621e-06 1.335434e-04 
+#> 6.072817e-06 1.400857e-04 
 #> 
 #> **Iteration 4
 #> LME step: Loglik: -396.3825, nlminb iterations: 7
 #> reStruct  parameters:
 #>           ds1           ds2           ds3           ds4           ds5 
-#> -0.1712517206 -0.0001651603  1.8420950864  1.0739800294  1.3229173529 
+#> -0.1712529502 -0.0001641277  1.8420957542  1.0739797181  1.3229173076 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  643.7949 
-#>  fixed effects: 94.17386  -5.473521  -0.6970423  -0.2025145  2.10387  
+#> PNLS step: RSS =  643.7936 
+#>  fixed effects: 94.17386  -5.473526  -0.6970426  -0.2025146  2.103869  
 #>  iterations: 100 
 #> Convergence crit. (must all become <= tolerance = 0.0001):
 #>        fixed     reStruct 
-#> 4.025781e-07 9.628656e-06 
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
+#> 1.027451e-06 2.275704e-05 
  f_nlme_dfop_sfo <- nlme(f_2["DFOP-SFO", ],
     control = list(pnlsMaxIter = 120, tolerance = 5e-4), verbose = TRUE)
#> 
 #> **Iteration 1
-#> LME step: Loglik: -404.9583, nlminb iterations: 1
+#> LME step: Loglik: -404.9582, nlminb iterations: 1
 #> reStruct  parameters:
 #>        ds1        ds2        ds3        ds4        ds5        ds6 
-#> -0.4114357  0.9798641  1.6990035  0.7293314  0.3354323  1.7113047 
+#> -0.4114355  0.9798697  1.6990037  0.7293315  0.3354323  1.7113046 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.3642 
-#>  fixed effects: 93.82269  -5.455991  -0.6788957  -1.862196  -4.199671  0.0553284  
+#> PNLS step: RSS =  630.3644 
+#>  fixed effects: 93.82269  -5.455991  -0.6788957  -1.862196  -4.199671  0.05532828  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>     fixed  reStruct 
-#> 0.7879730 0.5822574 
+#> 0.7885368 0.5822683 
 #> 
 #> **Iteration 2
 #> LME step: Loglik: -407.7755, nlminb iterations: 11
 #> reStruct  parameters:
 #>          ds1          ds2          ds3          ds4          ds5          ds6 
-#> -0.371224105  0.003056163  1.789939431  0.724671132  0.301602942  1.754200482 
+#> -0.371224133  0.003056179  1.789939402  0.724671158  0.301602977  1.754200729 
 #>  Beginning PNLS step: ..  completed fit_nlme() step.
-#> PNLS step: RSS =  630.364 
-#>  fixed effects: 93.82269  -5.455991  -0.6788958  -1.862196  -4.199671  0.05532834  
+#> PNLS step: RSS =  630.3633 
+#>  fixed effects: 93.82269  -5.455992  -0.6788958  -1.862196  -4.199671  0.05532831  
 #>  iterations: 120 
 #> Convergence crit. (must all become <= tolerance = 0.0005):
 #>        fixed     reStruct 
-#> 9.814652e-07 1.059239e-05 
  plot(f_2["FOMC-SFO", 3:4])
  plot(f_nlme_fomc_sfo, 3:4)

+#> 4.789774e-07 2.200661e-05 
  plot(f_2["FOMC-SFO", 3:4])
  plot(f_nlme_fomc_sfo, 3:4)

   plot(f_2["DFOP-SFO", 3:4])
  plot(f_nlme_dfop_sfo, 3:4)

   anova(f_nlme_dfop_sfo, f_nlme_fomc_sfo, f_nlme_sfo_sfo)
#>                 Model df       AIC       BIC    logLik   Test   L.Ratio p-value
-#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                         
-#> f_nlme_fomc_sfo     2 11  818.5151  853.0089 -398.2576 1 vs 2  21.33957  <.0001
-#> f_nlme_sfo_sfo      3  9 1085.1821 1113.4043 -533.5910 2 vs 3 270.66697  <.0001
  anova(f_nlme_dfop_sfo, f_nlme_sfo_sfo) # if we ignore FOMC
#>                 Model df       AIC       BIC    logLik   Test  L.Ratio p-value
-#> f_nlme_dfop_sfo     1 13  843.8547  884.6201 -408.9274                        
+#> 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 
@@ -428,9 +427,9 @@ with additional elements

 #>   0.2768574   0.7231426 
 #> 
 #> $distimes
-#>             DT50     DT90  DT50_k1  DT50_k2
-#> parent  11.07091 104.6320 4.462384 46.20825
-#> A1     162.30518 539.1661       NA       NA
+#>             DT50     DT90 DT50back  DT50_k1  DT50_k2
+#> parent  11.07091 104.6320 31.49738 4.462384 46.20825
+#> A1     162.30536 539.1667       NA       NA       NA
 #> 
# }
 

   

diff --git a/docs/dev/reference/parms.html b/docs/dev/reference/parms.html
index a50ca352..bd35d3c1 100644
--- a/docs/dev/reference/parms.html
+++ b/docs/dev/reference/parms.html
@@ -188,23 +188,22 @@ such matrices is returned.

     
Examples

     
# mkinfit objects
 fit <- mkinfit("SFO", FOCUS_2006_C, quiet = TRUE)
-parms(fit)
#>      parent_0 k_parent_sink         sigma 
-#>    82.4921598     0.3060633     4.6730124 
parms(fit, transformed = TRUE)
#>          parent_0 log_k_parent_sink             sigma 
-#>         82.492160         -1.183963          4.673012 

+parms(fit)
#>   parent_0   k_parent      sigma 
+#> 82.4921598  0.3060633  4.6730124 
parms(fit, transformed = TRUE)
#>     parent_0 log_k_parent        sigma 
+#>    82.492160    -1.183963     4.673012 

 # mmkin objects
 ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")]))
 names(ds) <- paste("Dataset", 6:10)
 # \dontrun{
-fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1)
-parms(fits["SFO", ])
#>                 Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
-#> parent_0      88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
-#> k_parent_sink  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
-#> sigma          5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
parms(fits[, 2])
#> $SFO
-#>                  Dataset 7
-#> parent_0      82.666781678
-#> k_parent_sink  0.009647805
-#> sigma          7.040168584
+fits <- mmkin(c("SFO", "FOMC", "DFOP"), ds, quiet = TRUE, cores = 1)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00408
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0492
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00985
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.00815
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
parms(fits["SFO", ])
#>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
+#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
+#> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
+#> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
parms(fits[, 2])
#> $SFO
+#>             Dataset 7
+#> parent_0 82.666781678
+#> k_parent  0.009647805
+#> sigma     7.040168584
 #> 
 #> $FOMC
 #>           Dataset 7
@@ -221,10 +220,10 @@ such matrices is returned.

 #> g         0.526942415
 #> sigma     2.221302196
 #> 
parms(fits)
#> $SFO
-#>                 Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
-#> parent_0      88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
-#> k_parent_sink  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
-#> sigma          5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
+#>            Dataset 6    Dataset 7  Dataset 8  Dataset 9  Dataset 10
+#> parent_0 88.52275400 82.666781678 86.8547308 91.7779306 82.14809450
+#> k_parent  0.05794659  0.009647805  0.2102974  0.1232258  0.00720421
+#> sigma     5.15274487  7.040168584  3.6769645  6.4669234  6.50457673
 #> 
 #> $FOMC
 #>          Dataset 6  Dataset 7 Dataset 8 Dataset 9 Dataset 10
@@ -241,15 +240,15 @@ such matrices is returned.

 #> g         0.44845068  0.526942415  0.66091965  0.65322767  0.342652880
 #> sigma     1.35690468  2.221302196  1.34169076  2.87159846  1.942067831
 #> 
parms(fits, transformed = TRUE)
#> $SFO
-#>                   Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10
-#> parent_0          88.522754 82.666782 86.854731 91.777931  82.148094
-#> log_k_parent_sink -2.848234 -4.641025 -1.559232 -2.093737  -4.933090
-#> sigma              5.152745  7.040169  3.676964  6.466923   6.504577
+#>              Dataset 6 Dataset 7 Dataset 8 Dataset 9 Dataset 10
+#> parent_0     88.522754 82.666782 86.854731 91.777931  82.148094
+#> log_k_parent -2.848234 -4.641025 -1.559232 -2.093737  -4.933090
+#> sigma         5.152745  7.040169  3.676964  6.466923   6.504577
 #> 
 #> $FOMC
 #>            Dataset 6  Dataset 7  Dataset 8   Dataset 9 Dataset 10
-#> parent_0  95.5585751 92.6837649 90.7197870 98.38393896  94.848146
-#> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181817  -1.271085
+#> parent_0  95.5585751 92.6837649 90.7197870 98.38393897  94.848146
+#> log_alpha  0.2916741 -0.6996015  0.4941466  0.07181816  -1.271085
 #> log_beta   2.5675088  2.6493701  1.6108523  1.48095106   1.932278
 #> sigma      1.8476712  1.9167519  1.0660627  3.14605557   1.622278
 #> 
diff --git a/docs/dev/reference/plot.mkinfit.html b/docs/dev/reference/plot.mkinfit.html
index b9331f1a..ffbd1206 100644
--- a/docs/dev/reference/plot.mkinfit.html
+++ b/docs/dev/reference/plot.mkinfit.html
@@ -74,7 +74,7 @@ observed data together with the solution of the fitted model." />
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     

 
@@ -338,7 +338,7 @@ latex is being used for the formatting of the chi2 error level, if
 # parent to sink included
 # \dontrun{
 SFO_SFO <- mkinmod(parent = mkinsub("SFO", "m1", full = "Parent"),
-                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
#> Warning: Observations with value of zero were removed from the data
plot(fit)
plot_res(fit)
plot_res(fit, standardized = FALSE)
plot_err(fit)

+                   m1 = mkinsub("SFO", full = "Metabolite M1" ))
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE, error_model = "tc")
#> Warning: Observations with value of zero were removed from the data
plot(fit)
plot_res(fit)
plot_res(fit, standardized = FALSE)
plot_err(fit)

 # Show the observed variables separately, with residuals
 plot(fit, sep_obs = TRUE, show_residuals = TRUE, lpos = c("topright", "bottomright"),
      show_errmin = TRUE)

diff --git a/docs/dev/reference/plot.mmkin-1.png b/docs/dev/reference/plot.mmkin-1.png
index 8cf969c9..24fa6ca7 100644
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diff --git a/docs/dev/reference/plot.mmkin-2.png b/docs/dev/reference/plot.mmkin-2.png
index 45d67b55..377e50b5 100644
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diff --git a/docs/dev/reference/plot.mmkin-3.png b/docs/dev/reference/plot.mmkin-3.png
index c58b371a..3ea7b38a 100644
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diff --git a/docs/dev/reference/plot.mmkin-4.png b/docs/dev/reference/plot.mmkin-4.png
index 47cd7eec..017fbd1d 100644
Binary files a/docs/dev/reference/plot.mmkin-4.png and b/docs/dev/reference/plot.mmkin-4.png differ
diff --git a/docs/dev/reference/plot.mmkin-5.png b/docs/dev/reference/plot.mmkin-5.png
index 44037bb4..e7463916 100644
Binary files a/docs/dev/reference/plot.mmkin-5.png and b/docs/dev/reference/plot.mmkin-5.png differ
diff --git a/docs/dev/reference/plot.mmkin.html b/docs/dev/reference/plot.mmkin.html
index ca1ec266..f02e2ea6 100644
--- a/docs/dev/reference/plot.mmkin.html
+++ b/docs/dev/reference/plot.mmkin.html
@@ -76,7 +76,7 @@ the fit of at least one model to the same dataset is shown." />
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     

 
diff --git a/docs/dev/reference/plot.nlme.mmkin-2.png b/docs/dev/reference/plot.nlme.mmkin-2.png
index c82d0271..265fd2e0 100644
Binary files a/docs/dev/reference/plot.nlme.mmkin-2.png and b/docs/dev/reference/plot.nlme.mmkin-2.png differ
diff --git a/docs/dev/reference/plot.nlme.mmkin.html b/docs/dev/reference/plot.nlme.mmkin.html
index fd40b975..7e6124a1 100644
--- a/docs/dev/reference/plot.nlme.mmkin.html
+++ b/docs/dev/reference/plot.nlme.mmkin.html
@@ -72,7 +72,7 @@
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     
 
@@ -238,8 +238,7 @@ than two rows of plots are shown.

     
Examples

     
ds <- lapply(experimental_data_for_UBA_2019[6:10],
  function(x) subset(x$data[c("name", "time", "value")], name == "parent"))
-f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
-#plot(f) # too many panels for pkgdown
+f <- mmkin("SFO", ds, quiet = TRUE, cores = 1)
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0195
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.011
#plot(f) # too many panels for pkgdown
 plot(f[, 3:4])
library(nlme)
 f_nlme <- nlme(f)
 
diff --git a/docs/dev/reference/print.mkinds.html b/docs/dev/reference/print.mkinds.html
index 0539c7da..a8c0d808 100644
--- a/docs/dev/reference/print.mkinds.html
+++ b/docs/dev/reference/print.mkinds.html
@@ -72,7 +72,7 @@
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     

 
diff --git a/docs/dev/reference/sigma_twocomp.html b/docs/dev/reference/sigma_twocomp.html
index eac61a11..fd5c603e 100644
--- a/docs/dev/reference/sigma_twocomp.html
+++ b/docs/dev/reference/sigma_twocomp.html
@@ -73,7 +73,7 @@ dependence of the measured value \(y\):" />
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     
 
diff --git a/docs/dev/reference/summary.mkinfit.html b/docs/dev/reference/summary.mkinfit.html
index 99d7d7c4..f971fdf4 100644
--- a/docs/dev/reference/summary.mkinfit.html
+++ b/docs/dev/reference/summary.mkinfit.html
@@ -76,7 +76,7 @@ values." />
       
       
         mkin
-        0.9.50.3
+        0.9.50.3
       
     
 
@@ -233,9 +233,9 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
     
Examples

     

   summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.50.3 
-#> R version used for fitting:       4.0.0 
-#> Date of fit:     Wed May 27 06:02:05 2020 
-#> Date of summary: Wed May 27 06:02:05 2020 
+#> R version used for fitting:       4.0.2 
+#> Date of fit:     Thu Oct  8 09:13:59 2020 
+#> Date of summary: Thu Oct  8 09:13:59 2020 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -274,9 +274,9 @@ EC Document Reference Sanco/10058/2005 version 2.0, 434 pp,
 #> 
 #> Parameter correlation:
 #>               parent_0 log_k_parent     sigma
-#> parent_0     1.000e+00    5.428e-01 1.648e-07
-#> log_k_parent 5.428e-01    1.000e+00 2.513e-07
-#> sigma        1.648e-07    2.513e-07 1.000e+00
+#> parent_0     1.000e+00    5.428e-01 1.642e-07
+#> log_k_parent 5.428e-01    1.000e+00 2.507e-07
+#> sigma        1.642e-07    2.507e-07 1.000e+00
 #> 
 #> Backtransformed parameters:
 #> Confidence intervals for internally transformed parameters are asymmetric.
diff --git a/docs/dev/reference/transform_odeparms.html b/docs/dev/reference/transform_odeparms.html
index b0994d33..58a1e9a1 100644
--- a/docs/dev/reference/transform_odeparms.html
+++ b/docs/dev/reference/transform_odeparms.html
@@ -226,7 +226,7 @@ This is no problem for the internal use in mkinfit<
 SFO_SFO <- mkinmod(
   parent = list(type = "SFO", to = "m1", sink = TRUE),
   m1 = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
# Fit the model to the FOCUS example dataset D using defaults
-fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.s <- summary(fit)
+fit <- mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
fit.s <- summary(fit)
 # Transformed and backtransformed parameters
 print(fit.s$par, 3)
#>                Estimate Std. Error   Lower   Upper
 #> parent_0         99.598     1.5702 96.4038 102.793
@@ -241,7 +241,7 @@ This is no problem for the internal use in mkinfit<
 #> 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
#> Error in if (cost < cost.current) {    assign("cost.current", cost, inherits = TRUE)    if (!quiet)         cat(ifelse(OLS, "Sum of squared residuals", "Negative log-likelihood"),             " at call ", calls, ": ", cost.current, "\n", sep = "")}: missing value where TRUE/FALSE needed
#> Timing stopped at: 0 0.002 0.002
fit.2.s <- summary(fit.2)
#> Error in summary(fit.2): object 'fit.2' not found
print(fit.2.s$par, 3)
#> Error in print(fit.2.s$par, 3): object 'fit.2.s' not found
print(fit.2.s$bpar, 3)
#> Error in print(fit.2.s$bpar, 3): object 'fit.2.s' not found
# }
+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, ": ", signif(cost.current, 6),             "\n", sep = "")}: missing value where TRUE/FALSE needed
#> Timing stopped at: 0.002 0 0.003
fit.2.s <- summary(fit.2)
#> Error in summary(fit.2): object 'fit.2' not found
print(fit.2.s$par, 3)
#> Error in print(fit.2.s$par, 3): object 'fit.2.s' not found
print(fit.2.s$bpar, 3)
#> Error in print(fit.2.s$bpar, 3): object 'fit.2.s' not found
# }
 
 initials <- fit$start$value
 names(initials) <- rownames(fit$start)
@@ -256,7 +256,7 @@ This is no problem for the internal use in mkinfit<
   parent = list(type = "SFO", to = "m1", sink = TRUE),
   m1 = list(type = "SFO"),
   use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

-fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.ff.s <- summary(fit.ff)
+fit.ff <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0165
fit.ff.s <- summary(fit.ff)
 print(fit.ff.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
@@ -277,7 +277,7 @@ This is no problem for the internal use in mkinfit<
   m1 = list(type = "SFO"),
   use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

 
-fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
fit.ff.2.s <- summary(fit.ff.2)
+fit.ff.2 <- mkinfit(SFO_SFO.ff.2, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
#> Warning: Shapiro-Wilk test for standardized residuals: p =  0.0242
fit.ff.2.s <- summary(fit.ff.2)
 print(fit.ff.2.s$par, 3)
#>              Estimate Std. Error Lower Upper
 #> parent_0        84.79      3.012 78.67 90.91
 #> log_k_parent    -2.76      0.082 -2.92 -2.59
-- 
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