From 194659fcaccdd1ee37851725b8c72e99daa3a8cf Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Wed, 10 Apr 2019 10:17:35 +0200 Subject: Adapt tests, vignettes and examples - Write the NEWS - Static documentation rebuilt by pkgdown - Adapt mkinerrmin - Fix (hopefully all) remaining problems in mkinfit --- docs/reference/AIC.mmkin.html | 24 +- docs/reference/CAKE_export.html | 2 +- docs/reference/DFOP.solution.html | 2 +- docs/reference/Extract.mmkin.html | 22 +- docs/reference/FOCUS_2006_DFOP_ref_A_to_B.html | 2 +- docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_HS_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_SFO_ref_A_to_F.html | 2 +- docs/reference/FOCUS_2006_datasets.html | 2 +- docs/reference/FOMC.solution.html | 2 +- docs/reference/HS.solution.html | 2 +- docs/reference/IORE.solution.html | 26 +- docs/reference/NAFTA_SOP_2015.html | 30 +- docs/reference/NAFTA_SOP_Attachment-1.png | Bin 42277 -> 42277 bytes docs/reference/NAFTA_SOP_Attachment.html | 31 +- docs/reference/SFO.solution.html | 2 +- docs/reference/SFORB.solution.html | 2 +- docs/reference/add_err-1.png | Bin 89876 -> 89854 bytes docs/reference/add_err-2.png | Bin 51188 -> 51200 bytes docs/reference/add_err-3.png | Bin 52612 -> 52620 bytes docs/reference/add_err.html | 10 +- docs/reference/endpoints.html | 2 +- docs/reference/geometric_mean.html | 2 +- docs/reference/ilr.html | 2 +- docs/reference/index.html | 2 +- docs/reference/logLik.mkinfit.html | 51 +- docs/reference/logistic.solution.html | 24 +- docs/reference/max_twa_parent.html | 2 +- docs/reference/mccall81_245T-1.png | Bin 0 -> 58660 bytes docs/reference/mccall81_245T.html | 75 +- docs/reference/mkin_long_to_wide.html | 14 +- docs/reference/mkin_wide_to_long.html | 8 +- docs/reference/mkinds.html | 2 +- docs/reference/mkinerrmin.html | 5 +- docs/reference/mkinfit.html | 1147 ++++++++---------------- docs/reference/mkinmod.html | 4 +- docs/reference/mkinparplot-1.png | Bin 16495 -> 16555 bytes docs/reference/mkinparplot.html | 6 +- docs/reference/mkinplot.html | 2 +- docs/reference/mkinpredict.html | 76 +- docs/reference/mkinresplot-1.png | Bin 15020 -> 14889 bytes docs/reference/mkinresplot.html | 5 +- docs/reference/mkinsub.html | 2 +- docs/reference/mmkin-3.png | Bin 85755 -> 85751 bytes docs/reference/mmkin-5.png | Bin 57629 -> 57629 bytes docs/reference/mmkin.html | 12 +- docs/reference/nafta.html | 30 +- docs/reference/plot.mkinfit-1.png | Bin 45512 -> 45173 bytes docs/reference/plot.mkinfit-2.png | Bin 53185 -> 52916 bytes docs/reference/plot.mkinfit-3.png | Bin 43963 -> 43743 bytes docs/reference/plot.mkinfit-4.png | Bin 60143 -> 59806 bytes docs/reference/plot.mkinfit.html | 7 +- docs/reference/plot.mmkin-1.png | Bin 34395 -> 34400 bytes docs/reference/plot.mmkin-2.png | Bin 34836 -> 34840 bytes docs/reference/plot.mmkin-3.png | Bin 25453 -> 25445 bytes docs/reference/plot.mmkin.html | 6 +- docs/reference/plot.nafta.html | 2 +- docs/reference/print.mkinds.html | 2 +- docs/reference/print.mkinmod.html | 2 +- docs/reference/print.nafta.html | 2 +- docs/reference/schaefer07_complex_case-1.png | Bin 55656 -> 55662 bytes docs/reference/schaefer07_complex_case.html | 12 +- docs/reference/sigma_twocomp.html | 2 +- docs/reference/summary.mkinfit.html | 64 +- docs/reference/synthetic_data_for_UBA.html | 2 +- docs/reference/test_data_from_UBA_2014-1.png | Bin 53543 -> 53400 bytes docs/reference/test_data_from_UBA_2014-2.png | Bin 69982 -> 69202 bytes docs/reference/test_data_from_UBA_2014.html | 70 +- docs/reference/transform_odeparms.html | 82 +- 69 files changed, 721 insertions(+), 1170 deletions(-) create mode 100644 docs/reference/mccall81_245T-1.png (limited to 'docs/reference') diff --git a/docs/reference/AIC.mmkin.html b/docs/reference/AIC.mmkin.html index 1224fbe6..d5386145 100644 --- a/docs/reference/AIC.mmkin.html +++ b/docs/reference/AIC.mmkin.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -163,22 +163,22 @@

Examples

  f <- mmkin(c("SFO", "FOMC", "DFOP"),
     list("FOCUS A" = FOCUS_2006_A,
-         "FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
#> Warning: Optimisation by method Port did not converge:
-#> false convergence (8)
  AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
#> [1] 55.32452

+         "FOCUS C" = FOCUS_2006_C), cores = 1, quiet = TRUE)
#> Warning: Optimisation did not converge:
+#> false convergence (8)
  AIC(f[1, "FOCUS A"]) # We get a single number for a single fit
#> [1] 55.28197

   # For FOCUS A, the models fit almost equally well, so the higher the number
   # of parameters, the higher (worse) the AIC
   AIC(f[, "FOCUS A"])
#>      df      AIC
-#> SFO   3 55.32452
-#> FOMC  4 57.32477
-#> DFOP  5 59.32452
  AIC(f[, "FOCUS A"], k = 0) # If we do not penalize additional parameters, we get nearly the same
#>      df      AIC
-#> SFO   3 49.32452
-#> FOMC  4 49.32477
-#> DFOP  5 49.32452

+#> SFO   3 55.28197
+#> FOMC  4 57.28198
+#> 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 49.28198
+#> DFOP  5 49.28197

   # For FOCUS C, the more complex models fit better
   AIC(f[, "FOCUS C"])
#>      df      AIC
-#> SFO   3 59.84675
-#> FOMC  4 44.70584
-#> DFOP  5 29.08369
+#> SFO 3 59.29336 +#> FOMC 4 44.68652 +#> DFOP 5 29.02372 diff --git a/docs/reference/DFOP.solution.html b/docs/reference/DFOP.solution.html index 378d6eb2..335c3a80 100644 --- a/docs/reference/DFOP.solution.html +++ b/docs/reference/DFOP.solution.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/Extract.mmkin.html b/docs/reference/Extract.mmkin.html index 3567af48..77eff52e 100644 --- a/docs/reference/Extract.mmkin.html +++ b/docs/reference/Extract.mmkin.html @@ -63,7 +63,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -172,26 +172,26 @@ cores = 1, quiet = TRUE) fits["FOMC", ]
#> dataset #> model B C -#> FOMC List,45 List,45 +#> FOMC List,36 List,36 #> attr(,"class") #> [1] "mmkin"
fits[, "B"]
#> dataset #> model B -#> SFO List,45 -#> FOMC List,45 +#> SFO List,36 +#> FOMC List,36 #> attr(,"class") #> [1] "mmkin"
fits["SFO", "B"]
#> dataset #> model B -#> SFO List,45 +#> SFO List,36 #> attr(,"class") #> [1] "mmkin"
head( # This extracts an mkinfit object with lots of components fits[["FOMC", "B"]] )
#> $par -#> parent_0 log_alpha log_beta -#> 99.666193 2.549849 5.050586 +#> parent_0 log_alpha log_beta sigma +#> 99.666193 2.549849 5.050586 1.890202 #> -#> $ssr +#> $objective #> [1] 28.58291 #> #> $convergence @@ -202,10 +202,10 @@ #> #> $evaluations #> function gradient -#> 25 78 +#> 25 76 #> -#> $counts -#> [1] "relative convergence (4)" +#> $message +#> [1] "both X-convergence and relative convergence (5)" #>
diff --git a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html index 0f51b1ce..cc3560ed 100644 --- a/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_FOMC_ref_A_to_F.html @@ -67,7 +67,7 @@ in this fit." /> mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html index e0b8d760..5f6fc197 100644 --- a/docs/reference/FOCUS_2006_HS_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_HS_ref_A_to_F.html @@ -67,7 +67,7 @@ in this fit." /> mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html index d07fb469..45d00237 100644 --- a/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html +++ b/docs/reference/FOCUS_2006_SFO_ref_A_to_F.html @@ -67,7 +67,7 @@ in this fit." /> mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/FOCUS_2006_datasets.html b/docs/reference/FOCUS_2006_datasets.html index b766c88b..2b060f47 100644 --- a/docs/reference/FOCUS_2006_datasets.html +++ b/docs/reference/FOCUS_2006_datasets.html @@ -63,7 +63,7 @@ mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/FOMC.solution.html b/docs/reference/FOMC.solution.html index 8763c878..90a4810c 100644 --- a/docs/reference/FOMC.solution.html +++ b/docs/reference/FOMC.solution.html @@ -67,7 +67,7 @@ The form given here differs slightly from the original reference by Gustafson mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/HS.solution.html b/docs/reference/HS.solution.html index 940d3186..1365e23f 100644 --- a/docs/reference/HS.solution.html +++ b/docs/reference/HS.solution.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/IORE.solution.html b/docs/reference/IORE.solution.html index 9a497f92..c9b79512 100644 --- a/docs/reference/IORE.solution.html +++ b/docs/reference/IORE.solution.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -176,20 +176,20 @@

Examples

- 
  plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2,
-                                       ylim = c(0, 100))
  fit.fomc <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-  fit.iore <- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE)
-  fit.iore.deS <- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE)
-
-  print(data.frame(coef(fit.fomc), coef(fit.iore), coef(fit.iore.deS),
-                   row.names = paste("model par", 1:3)))
#>             coef.fit.fomc. coef.fit.iore. coef.fit.iore.deS.
-#> model par 1    85.87489063      85.874890          85.874890
-#> model par 2     0.05192238      -4.826631          -4.826631
-#> model par 3     0.65096665       1.949403           1.949403
  print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,
-              iore.deS = endpoints(fit.iore)$distimes))
#>              DT50    DT90 DT50back
+    
  plot(function(x) IORE.solution(x, 100, 0.2, 1.3), 0, 2, ylim = c(0, 100))
  
    fit.fomc <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
+    fit.iore <- mkinfit("IORE", FOCUS_2006_C, quiet = TRUE)
+    fit.iore.deS <- mkinfit("IORE", FOCUS_2006_C, solution_type = "deSolve", quiet = TRUE)
+
+    print(data.frame(fit.fomc$par, fit.iore$par, fit.iore.deS$par,
+                     row.names = paste("model par", 1:4)))
#>             fit.fomc.par fit.iore.par fit.iore.deS.par
+#> model par 1  85.87488995    85.874890        85.874890
+#> model par 2   0.05192228    -4.826631        -4.826631
+#> model par 3   0.65096650     1.949403         1.949403
+#> model par 4   1.85744396     1.857444         1.857444
    print(rbind(fomc = endpoints(fit.fomc)$distimes, iore = endpoints(fit.iore)$distimes,
+                iore.deS = endpoints(fit.iore)$distimes))
#>              DT50    DT90 DT50back
 #> fomc     1.785233 15.1479 4.559973
 #> iore     1.785233 15.1479 4.559973
-#> iore.deS 1.785233 15.1479 4.559973

+#> iore.deS 1.785233 15.1479 4.559973
@@ -155,8 +155,7 @@

Examples

- 
  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
#> Warning: Calculation of the Jacobian failed for the cost function of the untransformed model.
-#> No t-test results will be available
#> 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)
#> 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 
 #> 
@@ -165,22 +164,25 @@
 #> 
 #> Parameters:
 #> $SFO
-#>               Estimate   Pr(>t)    Lower    Upper
-#> parent_0       83.7558 8.08e-15 76.92822 90.58328
-#> k_parent_sink   0.0017 7.45e-05  0.00111  0.00262
+#>               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
 #> 
 #> $IORE
 #>                     Estimate Pr(>t)    Lower    Upper
-#> parent_0            9.69e+01     NA 8.75e+01 1.06e+02
-#> k__iore_parent_sink 8.40e-14     NA 1.09e-19 6.47e-08
-#> N_parent            6.68e+00     NA 3.54e+00 9.83e+00
+#> 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
 #> 
 #> $DFOP
 #>          Estimate   Pr(>t)    Lower    Upper
-#> parent_0 9.76e+01 4.44e-13 8.88e+01 1.06e+02
-#> k1       4.24e-02 3.55e-02 1.41e-02 1.27e-01
-#> k2       8.24e-04 2.06e-02 3.17e-04 2.14e-03
-#> g        2.88e-01 1.31e-04 1.78e-01 4.30e-01
+#> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02
+#> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02
+#> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03
+#> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01
+#> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00
 #> 
 #> 
 #> DTx values:
@@ -190,7 +192,7 @@
 #> DFOP  429    2380      841
 #> 
 #> Representative half-life:
-#> [1] 841.4096
  plot(nafta_evaluation)

+#> [1] 841.4094
  plot(nafta_evaluation)
@@ -152,7 +152,7 @@

Examples

- 
  nafta_att_p5a <- nafta(NAFTA_SOP_Attachment[["p5a"]], cores = 1)
#> Warning: Could not estimate covariance matrix; singular system.
#> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
#> The half-life obtained from the IORE model may be used
  print(nafta_att_p5a)
#> Sums of squares:
+    
  nafta_att_p5a <- nafta(NAFTA_SOP_Attachment[["p5a"]], cores = 1)
#> The SFO model is rejected as S_SFO is equal or higher than the critical value S_c
#> The half-life obtained from the IORE model may be used
  print(nafta_att_p5a)
#> Sums of squares:
 #>       SFO      IORE      DFOP 
 #> 465.21753  56.27506  32.06401 
 #> 
@@ -162,28 +162,31 @@
 #> Parameters:
 #> $SFO
 #>               Estimate   Pr(>t)  Lower   Upper
-#> parent_0       95.8401 1.10e-21 92.121 99.5597
-#> k_parent_sink   0.0102 1.71e-12  0.009  0.0117
+#> parent_0       95.8401 4.67e-21 92.245 99.4357
+#> k_parent_sink   0.0102 3.92e-12  0.009  0.0117
+#> sigma           4.8230 3.81e-06  3.214  6.4318
 #> 
 #> $IORE
-#>                     Estimate   Pr(>t)    Lower    Upper
-#> parent_0            1.01e+02 2.37e-26 9.89e+01 1.03e+02
-#> k__iore_parent_sink 1.54e-05 8.73e-02 3.48e-06 6.85e-05
-#> N_parent            2.57e+00 1.14e-11 2.22e+00 2.92e+00
+#>                     Estimate Pr(>t)    Lower    Upper
+#> parent_0            1.01e+02     NA 9.91e+01 1.02e+02
+#> k__iore_parent_sink 1.54e-05     NA 4.08e-06 5.84e-05
+#> N_parent            2.57e+00     NA 2.25e+00 2.89e+00
+#> sigma               1.68e+00     NA 1.12e+00 2.24e+00
 #> 
 #> $DFOP
-#>          Estimate   Pr(>t) Lower Upper
-#> parent_0 9.99e+01 4.33e-27    NA    NA
-#> k1       2.67e-02 3.17e-05    NA    NA
-#> k2       2.16e-12 5.00e-01    NA    NA
-#> g        6.47e-01 2.13e-05    NA    NA
+#>          Estimate   Pr(>t)   Lower    Upper
+#> parent_0 9.99e+01 1.41e-26 98.8116 101.0810
+#> k1       2.67e-02 5.05e-06  0.0243   0.0295
+#> k2       3.41e-12 5.00e-01  0.0000      Inf
+#> g        6.47e-01 3.67e-06  0.6248   0.6677
+#> sigma    1.27e+00 8.91e-06  0.8395   1.6929
 #> 
 #> 
 #> DTx values:
 #>      DT50     DT90 DT50_rep
 #> SFO  67.7 2.25e+02 6.77e+01
 #> IORE 58.2 1.07e+03 3.22e+02
-#> DFOP 55.5 5.84e+11 3.21e+11
+#> DFOP 55.5 3.70e+11 2.03e+11
 #> 
 #> Representative half-life:
 #> [1] 321.5119
  plot(nafta_att_p5a)

diff --git a/docs/reference/SFO.solution.html b/docs/reference/SFO.solution.html
index dea4527e..2eb3a9b0 100644
--- a/docs/reference/SFO.solution.html
+++ b/docs/reference/SFO.solution.html
@@ -63,7 +63,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/SFORB.solution.html b/docs/reference/SFORB.solution.html
index a09f3d94..f2743fe4 100644
--- a/docs/reference/SFORB.solution.html
+++ b/docs/reference/SFORB.solution.html
@@ -67,7 +67,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/add_err-1.png b/docs/reference/add_err-1.png
index d099898c..e2107cc2 100644
Binary files a/docs/reference/add_err-1.png and b/docs/reference/add_err-1.png differ
diff --git a/docs/reference/add_err-2.png b/docs/reference/add_err-2.png
index f6de95a0..2fab0ee4 100644
Binary files a/docs/reference/add_err-2.png and b/docs/reference/add_err-2.png differ
diff --git a/docs/reference/add_err-3.png b/docs/reference/add_err-3.png
index 13f54e65..6e6592a5 100644
Binary files a/docs/reference/add_err-3.png and b/docs/reference/add_err-3.png differ
diff --git a/docs/reference/add_err.html b/docs/reference/add_err.html
index 8b6668f1..2eee6131 100644
--- a/docs/reference/add_err.html
+++ b/docs/reference/add_err.html
@@ -65,7 +65,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
@@ -215,14 +215,12 @@
 # Name the datasets for nicer plotting
 names(d_SFO_SFO_err) <- paste("Dataset", 1:3)
 
-# Name the model in the list of models (with only one member in this case)
-# for nicer plotting later on.
-# Be quiet and use the faster Levenberg-Marquardt algorithm, as the datasets
-# are easy and examples are run often. Use only one core not to offend CRAN
+# Name the model in the list of models (with only one member in this case) for
+# nicer plotting later on.  Be quiet and use only one core not to offend CRAN
 # checks
 f_SFO_SFO <- mmkin(list("SFO-SFO" = m_SFO_SFO),
                    d_SFO_SFO_err, cores = 1,
-                   quiet = TRUE, method.modFit = "Marq")
+                   quiet = TRUE)
 
 plot(f_SFO_SFO)

 # We would like to inspect the fit for dataset 3 more closely
diff --git a/docs/reference/endpoints.html b/docs/reference/endpoints.html
index 4d777ce2..5f419781 100644
--- a/docs/reference/endpoints.html
+++ b/docs/reference/endpoints.html
@@ -66,7 +66,7 @@ with the advantage that the SFORB model can also be used for metabolites." />
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/geometric_mean.html b/docs/reference/geometric_mean.html
index f8beb987..908aa8cc 100644
--- a/docs/reference/geometric_mean.html
+++ b/docs/reference/geometric_mean.html
@@ -63,7 +63,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/ilr.html b/docs/reference/ilr.html
index 02c96b92..0a18d535 100644
--- a/docs/reference/ilr.html
+++ b/docs/reference/ilr.html
@@ -63,7 +63,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/index.html b/docs/reference/index.html
index e1f149e2..42028d60 100644
--- a/docs/reference/index.html
+++ b/docs/reference/index.html
@@ -60,7 +60,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/logLik.mkinfit.html b/docs/reference/logLik.mkinfit.html
index fc4193cb..0184d573 100644
--- a/docs/reference/logLik.mkinfit.html
+++ b/docs/reference/logLik.mkinfit.html
@@ -33,23 +33,12 @@
 
 
 
+  model parameters and the fitted error model parameters." />
 
 
 
@@ -80,7 +69,7 @@ In the case of iterative reweighting, the variances obtained by this
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
@@ -146,23 +135,12 @@ In the case of iterative reweighting, the variances obtained by this
     

     
     
This function simply calculates the product of the likelihood densities
-  calculated using dnorm, i.e. assuming normal distribution.

+  calculated using dnorm, i.e. assuming normal distribution,
+  with of the mean predicted by the degradation model, and the
+  standard deviation predicted by the error model.

 
The total number of estimated parameters returned with the value
   of the likelihood is calculated as the sum of fitted degradation
   model parameters and the fitted error model parameters.

-
For the case of unweighted least squares fitting, we calculate one
-  constant standard deviation from the residuals using sd
-  and add one to the number of fitted degradation model parameters.

-
For the case of manual weighting, we use the weight given for each
-  observation as standard deviation in calculating its likelihood
-  and the total number of estimated parameters is equal to the
-  number of fitted degradation model parameters.

-
In the case of iterative reweighting, the variances obtained by this
-  procedure are used in the likelihood calculations, and the number of
-  estimated parameters is obtained by the number of degradation model
-  parameters plus the number of variance model parameters, i.e. the number of
-  observed variables if the reweighting method is "obs", and two if the
-  reweighting method is "tc".

     
     

 
@@ -199,17 +177,10 @@ In the case of iterative reweighting, the variances obtained by this
     parent = mkinsub("SFO", to = "m1"),
     m1 = mkinsub("SFO")
   )
#> Successfully compiled differential equation model from auto-generated C code.
  d_t <- FOCUS_2006_D
-  d_t[23:24, "value"] <- c(NA, NA) # can't cope with zero values at the moment
-  f_nw <- mkinfit(sfo_sfo, d_t, quiet = TRUE) # no weighting (weights are unity)
-  f_obs <- mkinfit(sfo_sfo, d_t, reweight.method = "obs", quiet = TRUE)
-  f_tc <- mkinfit(sfo_sfo, d_t, reweight.method = "tc", quiet = TRUE)
-  d_t$err <- d_t$value # Manual weighting assuming sigma ~ y
-  f_man <- mkinfit(sfo_sfo, d_t, err = "err", quiet = TRUE)
-  AIC(f_nw, f_obs, f_tc, f_man)
#>       df      AIC
-#> f_nw   5 204.4619
+  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   5 204.4486
 #> f_obs  6 205.8727
-#> f_tc   6 143.8773
-#> f_man  4 291.8000
+#> f_tc 6 141.9656
@@ -205,17 +205,19 @@ n = 1, reps = 2, digits = 5, LOD = 0.1, seed = 123456)[[1]] m <- mkinfit("logistic", d_2_1, quiet = TRUE) - plot_sep(m)
summary(m)$bpar
#> Estimate se_notrans t value Pr(>t) Lower -#> parent_0 1.057896e+02 2.3743105248 44.5559374 6.656664e-16 1.006602e+02 -#> kmax 6.398190e-02 0.0193490291 3.3067243 2.836921e-03 3.329058e-02 -#> k0 1.612775e-04 0.0009640761 0.1672871 4.348592e-01 3.972250e-10 -#> r 2.263946e-01 0.2822811886 0.8020181 2.184792e-01 1.531165e-02 + plot_sep(m)
summary(m)$bpar
#> Estimate se_notrans t value Pr(>t) Lower +#> parent_0 1.057896e+02 1.9023449643 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 5.846687e-08 +#> r 2.263946e-01 0.1718110718 1.317695 1.061044e-01 4.335843e-02 +#> sigma 5.332935e+00 0.9145907310 5.830952 4.036926e-05 3.340213e+00 #> Upper -#> parent_0 110.9190170 -#> kmax 0.1229682 -#> k0 65.4803698 -#> r 3.3474197
endpoints(m)$distimes
#> DT50 DT90 DT50_k0 DT50_kmax -#> parent 36.86533 62.41511 4297.854 10.83349
+#> parent_0 109.9341588 +#> kmax 0.1041853 +#> k0 0.4448749 +#> r 1.1821121 +#> sigma 7.3256566
endpoints(m)$distimes
#> DT50 DT90 DT50_k0 DT50_kmax +#> parent 36.86533 62.41511 4297.853 10.83349
diff --git a/docs/reference/mccall81_245T-1.png b/docs/reference/mccall81_245T-1.png new file mode 100644 index 00000000..859f315a Binary files /dev/null and b/docs/reference/mccall81_245T-1.png differ diff --git a/docs/reference/mccall81_245T.html b/docs/reference/mccall81_245T.html index 47fa6482..a336a840 100644 --- a/docs/reference/mccall81_245T.html +++ b/docs/reference/mccall81_245T.html @@ -65,7 +65,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -141,10 +141,10 @@

Format

A dataframe containing the following variables.

-
name

the name of the compound observed. Note that T245 is used as +

name

the name of the compound observed. Note that T245 is used as an acronym for 2,4,5-T. T245 is a legitimate object name in R, which is necessary for specifying models using - mkinmod.

+ mkinmod.

time

a numeric vector containing sampling times in days after treatment

value

a numeric vector containing concentrations in percent of applied radioactivity

@@ -160,23 +160,24 @@

Examples

  SFO_SFO_SFO <- mkinmod(T245 = list(type = "SFO", to = "phenol"),
                          phenol = list(type = "SFO", to = "anisole"),
-                         anisole = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
  
    fit.1 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
-    summary(fit.1)$bpar
#> Warning: Could not estimate covariance matrix; singular system.
#>                      Estimate   se_notrans      t value       Pr(>t) Lower
-#> T245_0           1.038550e+02 2.4256088519 4.281607e+01 7.235908e-20    NA
-#> k_T245_sink      1.636106e-02 0.0183803090 8.901408e-01 1.925667e-01    NA
-#> k_T245_phenol    2.700936e-02 0.0179604385 1.503825e+00 7.498498e-02    NA
-#> k_phenol_sink    1.286034e-11 0.2810970202 4.575054e-11 5.000000e-01    NA
-#> k_phenol_anisole 4.050581e-01 0.1608928349 2.517564e+00 1.075371e-02    NA
-#> k_anisole_sink   6.678742e-03 0.0008199239 8.145563e+00 9.469402e-08    NA
-#>                  Upper
-#> T245_0              NA
-#> k_T245_sink         NA
-#> k_T245_phenol       NA
-#> k_phenol_sink       NA
-#> k_phenol_anisole    NA
-#> k_anisole_sink      NA
    endpoints(fit.1)
#> $ff
+                         anisole = list(type = "SFO"))
#> Successfully compiled differential equation model from auto-generated C code.
  
    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
#> Warning: NaNs wurden erzeugt
#>                      Estimate   se_notrans   t value       Pr(>t)        Lower
+#> T245_0           1.038550e+02 2.1508106806 48.286460 3.542223e-18 99.246062186
+#> k_T245_sink      1.636106e-02          NaN       NaN          NaN  0.012661557
+#> k_T245_phenol    2.700936e-02          NaN       NaN          NaN  0.024487315
+#> k_phenol_sink    1.054519e-10          NaN       NaN          NaN  0.000000000
+#> k_phenol_anisole 4.050581e-01 0.1053797258  3.843795 7.969973e-04  0.218013983
+#> k_anisole_sink   6.678742e-03 0.0006205825 10.762053 9.427693e-09  0.005370739
+#> sigma            2.514628e+00 0.3383657682  7.431685 1.054052e-06  1.706607296
+#>                         Upper
+#> T245_0           1.084640e+02
+#> k_T245_sink      2.114150e-02
+#> k_T245_phenol    2.979116e-02
+#> k_phenol_sink             Inf
+#> k_phenol_anisole 7.525759e-01
+#> k_anisole_sink   8.305299e-03
+#> sigma            3.322649e+00
    endpoints(fit.1)
#> $ff
 #>      T245_sink    T245_phenol    phenol_sink phenol_anisole   anisole_sink 
-#>   3.772401e-01   6.227599e-01   3.174937e-11   1.000000e+00   1.000000e+00 
+#>   3.772401e-01   6.227599e-01   2.603376e-10   1.000000e+00   1.000000e+00 
 #> 
 #> $SFORB
 #> logical(0)
@@ -185,26 +186,26 @@
 #>               DT50      DT90
 #> T245     15.982025  53.09114
 #> phenol    1.711229   5.68458
-#> anisole 103.784093 344.76330
-#> 
    # No convergence, no covariance matrix ...
-    # k_phenol_sink is really small, therefore fix it to zero
+#> anisole 103.784092 344.76329
+#> 
    # k_phenol_sink is really small, therefore fix it to zero
     fit.2 <- mkinfit(SFO_SFO_SFO, subset(mccall81_245T, soil == "Commerce"),
                    parms.ini = c(k_phenol_sink = 0),
-                   fixed_parms = "k_phenol_sink", quiet = TRUE)
-    summary(fit.2)$bpar
#>                      Estimate   se_notrans   t value       Pr(>t)        Lower
-#> T245_0           1.038550e+02 2.3517950656 44.159900 6.461715e-21 98.932670927
-#> k_T245_sink      1.636106e-02 0.0021685502  7.544701 1.978480e-07  0.012397413
-#> k_T245_phenol    2.700936e-02 0.0013511301 19.990199 1.606634e-14  0.024324422
-#> k_phenol_anisole 4.050581e-01 0.1238660786  3.270129 2.013627e-03  0.213574853
-#> k_anisole_sink   6.678742e-03 0.0007468908  8.942059 1.543812e-08  0.005284957
+                   fixed_parms = "k_phenol_sink", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
    summary(fit.2)$bpar
#>                      Estimate   se_notrans   t value       Pr(>t)        Lower
+#> T245_0           1.038550e+02 2.1623652988 48.028441 4.993105e-19 99.271024566
+#> k_T245_sink      1.636106e-02 0.0019676253  8.315129 1.673677e-07  0.012679144
+#> k_T245_phenol    2.700936e-02 0.0012421965 21.743225 1.314080e-13  0.024500318
+#> k_phenol_anisole 4.050581e-01 0.1177235385  3.440757 1.679236e-03  0.218746681
+#> k_anisole_sink   6.678743e-03 0.0006829745  9.778904 1.872891e-08  0.005377084
+#> sigma            2.514628e+00 0.3790944250  6.633250 2.875782e-06  1.710983655
 #>                         Upper
-#> T245_0           1.087774e+02
-#> k_T245_sink      2.159195e-02
-#> k_T245_phenol    2.999066e-02
-#> k_phenol_anisole 7.682180e-01
-#> k_anisole_sink   8.440105e-03
    endpoints(fit.1)
#> $ff
+#> T245_0           1.084390e+02
+#> k_T245_sink      2.111217e-02
+#> k_T245_phenol    2.977534e-02
+#> k_phenol_anisole 7.500550e-01
+#> k_anisole_sink   8.295501e-03
+#> sigma            3.318272e+00
    endpoints(fit.1)
#> $ff
 #>      T245_sink    T245_phenol    phenol_sink phenol_anisole   anisole_sink 
-#>   3.772401e-01   6.227599e-01   3.174937e-11   1.000000e+00   1.000000e+00 
+#>   3.772401e-01   6.227599e-01   2.603376e-10   1.000000e+00   1.000000e+00 
 #> 
 #> $SFORB
 #> logical(0)
@@ -213,8 +214,8 @@
 #>               DT50      DT90
 #> T245     15.982025  53.09114
 #> phenol    1.711229   5.68458
-#> anisole 103.784093 344.76330
-#>
+#> anisole 103.784092 344.76329 +#>
plot_sep(fit.2)
@@ -130,9 +130,9 @@
-

This function takes a dataframe in the long form as required by modCost - and converts it into a dataframe with one independent variable and several - dependent variables as columns.

+

This function takes a dataframe in the long form, i.e. with a row + for each observed value, and converts it into a dataframe with one + independent variable and several dependent variables as columns.

diff --git a/docs/reference/mkin_wide_to_long.html b/docs/reference/mkin_wide_to_long.html index f2b9bef7..4fc0e2ed 100644 --- a/docs/reference/mkin_wide_to_long.html +++ b/docs/reference/mkin_wide_to_long.html @@ -33,7 +33,7 @@ + dependent variable and converts it into the long form as required by mkinfit." /> @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -130,7 +130,7 @@

This function simply takes a dataframe with one independent variable and several - dependent variable and converts it into the long form as required by modCost.

+ dependent variable and converts it into the long form as required by mkinfit.

@@ -152,7 +152,7 @@

Value

-

Dataframe in long format as needed for modCost.

+

Dataframe in long format as needed for mkinfit.

Examples

diff --git a/docs/reference/mkinds.html b/docs/reference/mkinds.html index 3be872e8..2c01be70 100644 --- a/docs/reference/mkinds.html +++ b/docs/reference/mkinds.html @@ -63,7 +63,7 @@ mkin - 0.9.48.1 + 0.9.49.4 diff --git a/docs/reference/mkinerrmin.html b/docs/reference/mkinerrmin.html index 60769486..3ebea1e4 100644 --- a/docs/reference/mkinerrmin.html +++ b/docs/reference/mkinerrmin.html @@ -64,7 +64,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006." /> mkin - 0.9.48.1 + 0.9.49.4 @@ -179,8 +179,7 @@ chi-squared test as defined in the FOCUS kinetics report from 2006.

SFO_SFO = mkinmod(parent = mkinsub("SFO", to = "m1"),
                   m1 = mkinsub("SFO"),
                   use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

-fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
-round(mkinerrmin(fit_FOCUS_D), 4)
#>          err.min n.optim df
+fit_FOCUS_D = mkinfit(SFO_SFO, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
round(mkinerrmin(fit_FOCUS_D), 4)
#>          err.min n.optim df
 #> All data  0.0640       4 15
 #> parent    0.0646       2  7
 #> m1        0.0469       2  8
  fit_FOCUS_E = mkinfit(SFO_SFO, FOCUS_2006_E, quiet = TRUE)
diff --git a/docs/reference/mkinfit.html b/docs/reference/mkinfit.html
index df30ef09..d3a826b9 100644
--- a/docs/reference/mkinfit.html
+++ b/docs/reference/mkinfit.html
@@ -32,17 +32,15 @@
 
 
 
-
+
 
 
 
@@ -73,7 +71,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
@@ -138,17 +136,15 @@
 
     

     
-    
This function uses the Flexible Modelling Environment package
-  FME to create a function calculating the model cost, i.e. the
-  deviation between the kinetic model and the observed data. This model cost is
-  then minimised using the Port algorithm nlminb,
-  using the specified initial or fixed parameters and starting values.
-  Per default, parameters in the kinetic models are internally transformed in order
-  to better satisfy the assumption of a normal distribution of their estimators.
-  In each step of the optimsation, the kinetic model is solved using the
-  function mkinpredict. The variance of the residuals for each
-  observed variable can optionally be iteratively reweighted until convergence
-  using the argument reweight.method = "obs".

+    
This function maximises the likelihood of the observed data using
+  the Port algorithm nlminb, and the specified initial or fixed
+  parameters and starting values.  In each step of the optimsation, the kinetic
+  model is solved using the function mkinpredict. The parameters
+  of the selected error model are fitted simultaneously with the degradation
+  model parameters, as both of them are arguments of the likelihood function.

+
Per default, parameters in the kinetic models are internally transformed in
+  order to better satisfy the assumption of a normal distribution of their
+  estimators.

     
     

 
@@ -160,18 +156,12 @@
   solution_type = c("auto", "analytical", "eigen", "deSolve"),
   method.ode = "lsoda",
   use_compiled = "auto",
-  method.modFit = c("Port", "Marq", "SANN", "Nelder-Mead", "BFGS", "CG", "L-BFGS-B"),
-  maxit.modFit = "auto",
-  control.modFit = list(),
+  control = list(eval.max = 300, iter.max = 200),
   transform_rates = TRUE,
   transform_fractions = TRUE,
-  plot = FALSE, quiet = FALSE, err = NULL,
-  weight = c("none", "manual", "std", "mean", "tc"),
-  tc = c(sigma_low = 0.5, rsd_high = 0.07),
-  scaleVar = FALSE,
+  quiet = FALSE,
   atol = 1e-8, rtol = 1e-10, n.outtimes = 100,
-  reweight.method = NULL,
-  reweight.tol = 1e-8, reweight.max.iter = 10,
+  error_model = c("const", "obs", "tc"),
   trace_parms = FALSE, ...)

Arguments

@@ -181,20 +171,20 @@ mkinmod

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

observed -

The observed data. It has to be in the long format as described in - modFit, i.e. the first column called "name" must contain the - name of the observed variable for each data point. The second column must - contain the times of observation, named "time". The third column must be - named "value" and contain the observed values. Optionally, a further column - can contain weights for each data point. Its name must be passed as a - further argument named err which is then passed on to - modFit.

+

A dataframe with the observed data. The first column called "name" must + contain the name of the observed variable for each data point. The second + column must contain the times of observation, named "time". The third + column must be named "value" and contain the observed values. Zero values + in the "value" column will be removed, with a warning, in order to + avoid problems with fitting the two-component error model. This is not + expected to be a problem, because in general, values of zero are not + observed in degradation data, because there is a lower limit of detection.

parms.ini @@ -247,10 +237,10 @@ solution of the model is used. This is only implemented for simple degradation experiments with only one state variable, i.e. with no metabolites. The default is "auto", which uses "analytical" if possible, - otherwise "eigen" if the model can be expressed using eigenvalues and - eigenvectors, and finally "deSolve" for the remaining models (time - dependence of degradation rates and metabolites). This argument is passed - on to the helper function mkinpredict.

+ otherwise "deSolve" if a compiler is present, and "eigen" if no + compiler is present and the model can be expressed using eigenvalues and + eigenvectors. This argument is passed on to the helper function + mkinpredict.

method.ode @@ -261,36 +251,12 @@ use_compiled

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

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

- method.modFit -

The optimisation method passed to modFit.

-

In order to optimally deal with problems where local minima occur, the - "Port" algorithm is now used per default as it is less prone to get trapped - in local minima and depends less on starting values for parameters than - the Levenberg Marquardt variant selected by "Marq". However, "Port" needs - more iterations.

-

The former default "Marq" is the Levenberg Marquardt algorithm - nls.lm from the package minpack.lm and usually needs - the least number of iterations.

-

The "Pseudo" algorithm is not included because it needs finite parameter bounds - which are currently not supported.

-

The "Newton" algorithm is not included because its number of iterations - can not be controlled by control.modFit and it does not appear - to provide advantages over the other algorithms.

- - - maxit.modFit -

Maximum number of iterations in the optimisation. If not "auto", this will - be passed to the method called by modFit, overriding - what may be specified in the next argument control.modFit.

- - - control.modFit -

Additional arguments passed to the optimisation method used by - modFit.

+ control +

A list of control arguments passed to nlminb.

transform_rates @@ -299,8 +265,8 @@ assumption of normal distribution of the estimator. If TRUE, also alpha and beta parameters of the FOMC model are log-transformed, as well as k1 and k2 rate constants for the DFOP and HS models and the break point - tb of the HS model. - If FALSE, zero is used as a lower bound for the rates in the optimisation.

+ tb of the HS model. If FALSE, zero is used as a lower bound for the rates + in the optimisation.

transform_fractions @@ -312,37 +278,10 @@ data. The transformation used for these transformations is the ilr transformation.

- - plot -

Should the observed values and the numerical solutions be plotted at each - stage of the optimisation?

- quiet -

Suppress printing out the current model cost after each improvement?

- - - err -

either NULL, or the name of the column with the - error estimates, used to weigh the residuals (see details of - modCost); if NULL, then the residuals are not weighed.

- - - weight -

only if err=NULL: how to weight the residuals, one of "none", - "std", "mean", see details of modCost, or "tc" for the - two component error model. The option "manual" is available for - the case that err!=NULL, but it is not necessary to specify it.

- - - tc -

The two components of the error model as used for (initial) - weighting

- - - scaleVar -

Will be passed to modCost. Default is not to scale Variables - according to the number of observations.

+

Suppress printing out the current value of the negative log-likelihood + after each improvement?

atol @@ -362,35 +301,17 @@ The default value is 100.

- reweight.method -

The method used for iteratively reweighting residuals, also known - as iteratively reweighted least squares (IRLS). Default is NULL, - i.e. no iterative weighting. - The first reweighting method is called "obs", meaning that each - observed variable is assumed to have its own variance. This variance - is estimated from the fit (mean squared residuals) and used for weighting - the residuals in each iteration until convergence of this estimate up to - reweight.tol or up to the maximum number of iterations - specified by reweight.max.iter. - The second reweighting method is called "tc" (two-component error model). - When using this method, the two components of an error model similar to - the one described by - Rocke and Lorenzato (1995) are estimated from the fit and the resulting - variances are used for weighting the residuals in each iteration until - convergence of these components or up to the maximum number of iterations - specified. Note that this method 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.

- - - reweight.tol -

Tolerance for convergence criterion for the variance components - in IRLS fits.

- - - reweight.max.iter -

Maximum iterations in IRLS fits.

+ error_model +

If the error model is "const", a constant standard deviation + is assumed.

+

If the error model is "obs", each observed variable is assumed to have its + 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.

trace_parms @@ -398,19 +319,18 @@ … -

Further arguments that will be passed to modFit.

+

Further arguments that will be passed on to deSolve.

Value

-

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

+

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

See also

-

Plotting methods plot.mkinfit and - mkinparplot.

+

Plotting methods plot.mkinfit and mkinparplot.

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

Fitting of several models to several datasets in a single call to @@ -418,13 +338,6 @@

Note

-

The implementation of iteratively reweighted least squares is inspired by the - work of the KinGUII team at Bayer Crop Science (Walter Schmitt and Zhenglei - Gao). A similar implemention can also be found in CAKE 2.0, which is the - other GUI derivative of mkin, sponsored by Syngenta.

- -

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 @@ -439,57 +352,61 @@

Examples

# Use shorthand notation for parent only degradation
 fit <- mkinfit("FOMC", FOCUS_2006_C, quiet = TRUE)
-summary(fit)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:12 2019 
-#> Date of summary: Mon Mar  4 14:05:12 2019 
+summary(fit)
#> mkin version used for fitting:    0.9.49.4 
+#> R version used for fitting:       3.5.3 
+#> Date of fit:     Wed Apr 10 10:10:01 2019 
+#> Date of summary: Wed Apr 10 10:10:01 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - (alpha/beta) * 1/((time/beta) + 1) * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted with method Port using 64 model solutions performed in 0.159 s
+#> Fitted with method using 221 model solutions performed in 0.508 s
 #> 
-#> Weighting: none
+#> Error model:
+#> NULL
 #> 
 #> Starting values for parameters to be optimised:
-#>          value   type
-#> parent_0  85.1  state
-#> alpha      1.0 deparm
-#> beta      10.0 deparm
+#>              value   type
+#> parent_0 85.100000  state
+#> alpha     1.000000 deparm
+#> beta     10.000000 deparm
+#> sigma     1.857444  error
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>               value lower upper
 #> parent_0  85.100000  -Inf   Inf
 #> log_alpha  0.000000  -Inf   Inf
 #> log_beta   2.302585  -Inf   Inf
+#> sigma      1.857444     0   Inf
 #> 
 #> Fixed parameter values:
 #> None
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>           Estimate Std. Error    Lower   Upper
-#> parent_0  85.87000     2.2460 80.38000 91.3700
-#> log_alpha  0.05192     0.1605 -0.34080  0.4446
-#> log_beta   0.65100     0.2801 -0.03452  1.3360
+#> parent_0  85.87000     1.8070 81.23000 90.5200
+#> log_alpha  0.05192     0.1353 -0.29580  0.3996
+#> log_beta   0.65100     0.2287  0.06315  1.2390
+#> sigma      1.85700     0.4378  0.73200  2.9830
 #> 
 #> Parameter correlation:
-#>           parent_0 log_alpha log_beta
-#> parent_0    1.0000   -0.2033  -0.3624
-#> log_alpha  -0.2033    1.0000   0.9547
-#> log_beta   -0.3624    0.9547   1.0000
-#> 
-#> Residual standard error: 2.275 on 6 degrees of freedom
+#>             parent_0  log_alpha   log_beta      sigma
+#> parent_0   1.000e+00 -1.565e-01 -3.142e-01 -1.313e-07
+#> log_alpha -1.565e-01  1.000e+00  9.564e-01 -2.634e-07
+#> log_beta  -3.142e-01  9.564e-01  1.000e+00 -2.200e-07
+#> sigma     -1.313e-07 -2.634e-07 -2.200e-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   85.870  38.230 1.069e-08 80.3800 91.370
-#> alpha       1.053   6.231 3.953e-04  0.7112  1.560
-#> beta        1.917   3.570 5.895e-03  0.9661  3.806
+#> parent_0   85.870  47.530 3.893e-08 81.2300 90.520
+#> alpha       1.053   7.393 3.562e-04  0.7439  1.491
+#> beta        1.917   4.373 3.601e-03  1.0650  3.451
+#> sigma       1.857   4.243 4.074e-03  0.7320  2.983
 #> 
 #> Chi2 error levels in percent:
 #>          err.min n.optim df
@@ -517,9 +434,8 @@
   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)))
#>        User      System verstrichen 
-#>       1.013       0.000       1.014 
coef(fit)
#>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
-#>          99.59848          -3.03822          -2.98030          -5.24750 
endpoints(fit)
#> $ff
+                           solution_type = "eigen", quiet = TRUE)))
#> Warning: Observations with value of zero were removed from the data
#>        User      System verstrichen 
+#>       1.653       0.000       1.653 
coef(fit)
#> NULL
endpoints(fit)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -528,73 +444,75 @@
 #> 
 #> $distimes
 #>              DT50      DT90
-#> parent   7.022929  23.32967
-#> m1     131.760712 437.69961
+#> parent   7.022928  23.32966
+#> m1     131.760715 437.69962
 #> 
# 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")))
#> Model cost at call  1 :  18915.53 
-#> Model cost at call  2 :  18915.53 
-#> Model cost at call  6 :  11424.02 
-#> Model cost at call  10 :  11424 
-#> Model cost at call  12 :  4094.396 
-#> Model cost at call  16 :  4094.396 
-#> Model cost at call  19 :  1340.595 
-#> Model cost at call  20 :  1340.593 
-#> Model cost at call  25 :  1072.239 
-#> Model cost at call  28 :  1072.236 
-#> Model cost at call  30 :  874.2615 
-#> Model cost at call  33 :  874.2611 
-#> Model cost at call  35 :  616.2377 
-#> Model cost at call  37 :  616.2372 
-#> Model cost at call  40 :  467.4386 
-#> Model cost at call  42 :  467.4381 
-#> Model cost at call  46 :  398.2914 
-#> Model cost at call  48 :  398.2914 
-#> Model cost at call  49 :  398.2913 
-#> Model cost at call  51 :  395.0712 
-#> Model cost at call  54 :  395.0711 
-#> Model cost at call  56 :  378.3298 
-#> Model cost at call  59 :  378.3298 
-#> Model cost at call  62 :  376.9812 
-#> Model cost at call  64 :  376.9811 
-#> Model cost at call  67 :  375.2085 
-#> Model cost at call  69 :  375.2085 
-#> Model cost at call  70 :  375.2085 
-#> Model cost at call  71 :  375.2085 
-#> Model cost at call  72 :  374.5723 
-#> Model cost at call  74 :  374.5723 
-#> Model cost at call  77 :  374.0075 
-#> Model cost at call  79 :  374.0075 
-#> Model cost at call  80 :  374.0075 
-#> Model cost at call  82 :  373.1711 
-#> Model cost at call  84 :  373.1711 
-#> Model cost at call  87 :  372.6445 
-#> Model cost at call  88 :  372.1614 
-#> Model cost at call  90 :  372.1614 
-#> Model cost at call  91 :  372.1614 
-#> Model cost at call  94 :  371.6464 
-#> Model cost at call  99 :  371.4299 
-#> Model cost at call  101 :  371.4299 
-#> Model cost at call  104 :  371.4071 
-#> Model cost at call  106 :  371.4071 
-#> Model cost at call  107 :  371.4071 
-#> Model cost at call  109 :  371.2524 
-#> Model cost at call  113 :  371.2524 
-#> Model cost at call  114 :  371.2136 
-#> Model cost at call  115 :  371.2136 
-#> Model cost at call  116 :  371.2136 
-#> Model cost at call  119 :  371.2134 
-#> Model cost at call  120 :  371.2134 
-#> Model cost at call  122 :  371.2134 
-#> Model cost at call  123 :  371.2134 
-#> Model cost at call  125 :  371.2134 
-#> Model cost at call  126 :  371.2134 
-#> Model cost at call  135 :  371.2134 
-#> Model cost at call  146 :  371.2134 
-#> Optimisation by method Port successfully terminated.
+                           solution_type = "deSolve")))
#> Warning: Observations with value of zero were removed from the data
#> Negative log-likelihood at call 1: 18915.53
+#> Negative log-likelihood at call 2: 18915.53
+#> Negative log-likelihood at call 6: 11424.02
+#> Negative log-likelihood at call 10: 11424
+#> Negative log-likelihood at call 13: 2367.052
+#> Negative log-likelihood at call 14: 2367.05
+#> Negative log-likelihood at call 19: 1314.716
+#> Negative log-likelihood at call 22: 1314.714
+#> Negative log-likelihood at call 25: 991.8311
+#> Negative log-likelihood at call 28: 991.8305
+#> Negative log-likelihood at call 30: 893.6462
+#> Negative log-likelihood at call 33: 893.6457
+#> Negative log-likelihood at call 35: 569.4049
+#> Negative log-likelihood at call 38: 569.4047
+#> Negative log-likelihood at call 40: 565.0651
+#> Negative log-likelihood at call 41: 565.065
+#> Negative log-likelihood at call 42: 565.0637
+#> Negative log-likelihood at call 45: 428.0188
+#> Negative log-likelihood at call 46: 428.0185
+#> Negative log-likelihood at call 50: 406.732
+#> Negative log-likelihood at call 52: 406.732
+#> Negative log-likelihood at call 55: 398.9115
+#> Negative log-likelihood at call 57: 398.9113
+#> Negative log-likelihood at call 60: 394.5943
+#> Negative log-likelihood at call 62: 394.5943
+#> Negative log-likelihood at call 66: 385.26
+#> Negative log-likelihood at call 67: 385.2599
+#> Negative log-likelihood at call 69: 385.2599
+#> Negative log-likelihood at call 70: 385.2597
+#> Negative log-likelihood at call 71: 374.7604
+#> Negative log-likelihood at call 72: 374.7603
+#> Negative log-likelihood at call 76: 373.199
+#> Negative log-likelihood at call 79: 373.199
+#> Negative log-likelihood at call 80: 373.199
+#> Negative log-likelihood at call 81: 372.3772
+#> Negative log-likelihood at call 84: 372.3772
+#> Negative log-likelihood at call 86: 371.2615
+#> Negative log-likelihood at call 89: 371.2615
+#> Negative log-likelihood at call 90: 371.2615
+#> Negative log-likelihood at call 92: 371.2439
+#> Negative log-likelihood at call 93: 371.2439
+#> Negative log-likelihood at call 94: 371.2439
+#> Negative log-likelihood at call 97: 371.2198
+#> Negative log-likelihood at call 98: 371.2198
+#> Negative log-likelihood at call 102: 371.2174
+#> Negative log-likelihood at call 104: 371.2174
+#> Negative log-likelihood at call 107: 371.2147
+#> Negative log-likelihood at call 110: 371.2147
+#> Negative log-likelihood at call 111: 371.2147
+#> Negative log-likelihood at call 112: 371.2145
+#> Negative log-likelihood at call 113: 371.2145
+#> Negative log-likelihood at call 116: 371.2145
+#> Negative log-likelihood at call 119: 371.2135
+#> Negative log-likelihood at call 121: 371.2135
+#> Negative log-likelihood at call 124: 371.2135
+#> Negative log-likelihood at call 126: 371.2135
+#> Negative log-likelihood at call 127: 371.2135
+#> Negative log-likelihood at call 133: 371.2134
+#> Negative log-likelihood at call 135: 371.2134
+#> Negative log-likelihood at call 138: 371.2134
+#> Negative log-likelihood at call 142: 371.2134
+#> Negative log-likelihood at call 152: 97.22429
+#> Optimisation successfully terminated.
 #>        User      System verstrichen 
-#>       0.821       0.000       0.822 
coef(fit.deSolve)
#>          parent_0 log_k_parent_sink   log_k_parent_m1     log_k_m1_sink 
-#>          99.59848          -3.03822          -2.98030          -5.24750 
endpoints(fit.deSolve)
#> $ff
+#>       1.136       0.000       1.135 
coef(fit.deSolve)
#> NULL
endpoints(fit.deSolve)
#> $ff
 #> parent_sink   parent_m1     m1_sink 
 #>    0.485524    0.514476    1.000000 
 #> 
@@ -603,36 +521,31 @@
 #> 
 #> $distimes
 #>              DT50      DT90
-#> parent   7.022929  23.32967
-#> m1     131.760711 437.69961
+#> parent   7.022928  23.32966
+#> m1     131.760710 437.69961
 #> 

 # Use stepwise fitting, using optimised parameters from parent only fit, FOMC
 
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)
-# Use starting parameters from parent only FOMC fit
+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$bparms.ode)
-
+  parms.ini = fit.FOMC$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)
-fit.SFORB_SFO.deSolve <- mkinfit(SFORB_SFO, FOCUS_2006_D, solution_type = "deSolve",
-                                 quiet = TRUE)
-# Use starting parameters from parent only SFORB fit (not really needed in this case)
+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)

+fit.SFORB_SFO <- mkinfit(SFORB_SFO, FOCUS_2006_D, parms.ini = fit.SFORB$bparms.ode, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data

 
# Weighted fits, including IRLS
 SFO_SFO.ff <- mkinmod(parent = mkinsub("SFO", "m1"),
-                      m1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
-summary(f.noweight)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:24 2019 
-#> Date of summary: Mon Mar  4 14:05:24 2019 
+                      m1 = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
f.noweight <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.noweight)
#> mkin version used for fitting:    0.9.49.4 
+#> R version used for fitting:       3.5.3 
+#> Date of fit:     Wed Apr 10 10:10:17 2019 
+#> Date of summary: Wed Apr 10 10:10:17 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -640,16 +553,18 @@
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted with method Port using 186 model solutions performed in 0.841 s
+#> Fitted with method using 404 model solutions performed in 1.105 s
 #> 
-#> Weighting: none
+#> Error model:
+#> NULL
 #> 
 #> 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
+#>                     value   type
+#> parent_0       100.750000  state
+#> k_parent         0.100000 deparm
+#> k_m1             0.100100 deparm
+#> f_parent_to_m1   0.500000 deparm
+#> sigma            3.125504  error
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>                     value lower upper
@@ -657,36 +572,38 @@
 #> log_k_parent    -2.302585  -Inf   Inf
 #> log_k_m1        -2.301586  -Inf   Inf
 #> f_parent_ilr_1   0.000000  -Inf   Inf
+#> sigma            3.125504     0   Inf
 #> 
 #> Fixed parameter values:
 #>      value  type
 #> m1_0     0 state
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error   Lower    Upper
-#> parent_0       99.60000    1.61400 96.3300 102.9000
-#> log_k_parent   -2.31600    0.04187 -2.4010  -2.2310
-#> log_k_m1       -5.24800    0.13610 -5.5230  -4.9720
-#> f_parent_ilr_1  0.04096    0.06477 -0.0904   0.1723
+#>                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
-#> parent_0         1.0000       0.5178  -0.1701        -0.5489
-#> log_k_parent     0.5178       1.0000  -0.3285        -0.5451
-#> log_k_m1        -0.1701      -0.3285   1.0000         0.7466
-#> f_parent_ilr_1  -0.5489      -0.5451   0.7466         1.0000
-#> 
-#> Residual standard error: 3.211 on 36 degrees of freedom
+#>                  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 -5.940e-09
+#> log_k_parent    5.174e-01    1.000e+00 -3.263e-01     -5.426e-01 -1.406e-08
+#> log_k_m1       -1.688e-01   -3.263e-01  1.000e+00      7.478e-01 -2.306e-08
+#> f_parent_ilr_1 -5.471e-01   -5.426e-01  7.478e-01      1.000e+00 -6.664e-09
+#> sigma          -5.940e-09   -1.406e-08 -2.306e-08     -6.664e-09  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  61.720 2.024e-38 96.330000 1.029e+02
-#> k_parent        0.098700  23.880 5.700e-24  0.090660 1.074e-01
-#> k_m1            0.005261   7.349 5.758e-09  0.003992 6.933e-03
-#> f_parent_to_m1  0.514500  22.490 4.375e-23  0.468100 5.606e-01
+#> 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
 #> 
 #> Chi2 error levels in percent:
 #>          err.min n.optim df
@@ -710,10 +627,10 @@
 #>     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
+#>     3   parent    63.23  74.07320 -1.084e+01
+#>     3   parent    68.99  74.07320 -5.083e+00
+#>     7   parent    52.32  49.91207  2.408e+00
+#>     7   parent    55.13  49.91207  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
@@ -723,9 +640,7 @@
 #>    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
-#>     0       m1     0.00   0.00000  0.000e+00
-#>     0       m1     0.00   0.00000  0.000e+00
+#>    75   parent     0.06   0.06074 -7.382e-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
@@ -745,11 +660,10 @@
 #>   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.irls <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, reweight.method = "obs", quiet = TRUE)
-summary(f.irls)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:26 2019 
-#> Date of summary: Mon Mar  4 14:05:26 2019 
+#>   120       m1    33.31  28.78984  4.520e+00
f.obs <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "obs", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.obs)
#> mkin version used for fitting:    0.9.49.4 
+#> R version used for fitting:       3.5.3 
+#> Date of fit:     Wed Apr 10 10:10:19 2019 
+#> Date of summary: Wed Apr 10 10:10:19 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -757,131 +671,10 @@
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted with method Port using 551 model solutions performed in 2.517 s
+#> Fitted with method using 558 model solutions performed in 1.602 s
 #> 
-#> Weighting: none
-#> 
-#> Iterative reweighting with method obs 
-#> Final mean squared residuals of observed variables:
-#>    parent        m1 
-#> 11.573407  7.407845 
-#> 
-#> 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
-#> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error    Lower   Upper
-#> parent_0       99.67000    1.79200 96.04000 103.300
-#> log_k_parent   -2.31200    0.04560 -2.40400  -2.219
-#> log_k_m1       -5.25100    0.12510 -5.50500  -4.998
-#> f_parent_ilr_1  0.03785    0.06318 -0.09027   0.166
-#> 
-#> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#> parent_0         1.0000       0.5083  -0.1979        -0.6148
-#> log_k_parent     0.5083       1.0000  -0.3894        -0.6062
-#> log_k_m1        -0.1979      -0.3894   1.0000         0.7417
-#> f_parent_ilr_1  -0.6148      -0.6062   0.7417         1.0000
-#> 
-#> Residual standard error: 1.054 on 36 degrees of freedom
-#> 
-#> 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       99.67000  55.630 8.185e-37 96.040000 1.033e+02
-#> k_parent        0.09906  21.930 1.016e-22  0.090310 1.087e-01
-#> k_m1            0.00524   7.996 8.486e-10  0.004066 6.753e-03
-#> f_parent_to_m1  0.51340  23.000 2.038e-23  0.468100 5.584e-01
-#> 
-#> Chi2 error levels in percent:
-#>          err.min n.optim df
-#> All data   6.399       4 15
-#> parent     6.466       2  7
-#> m1         4.679       2  8
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.5134
-#> parent_sink 0.4866
-#> 
-#> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   6.997  23.24
-#> m1     132.282 439.43
-#> 
-#> Data:
-#>  time variable observed predicted   residual   err
-#>     0   parent    99.46  99.67218 -2.122e-01 3.402
-#>     0   parent   102.04  99.67218  2.368e+00 3.402
-#>     1   parent    93.50  90.27153  3.228e+00 3.402
-#>     1   parent    92.50  90.27153  2.228e+00 3.402
-#>     3   parent    63.23  74.04648 -1.082e+01 3.402
-#>     3   parent    68.99  74.04648 -5.056e+00 3.402
-#>     7   parent    52.32  49.82092  2.499e+00 3.402
-#>     7   parent    55.13  49.82092  5.309e+00 3.402
-#>    14   parent    27.27  24.90288  2.367e+00 3.402
-#>    14   parent    26.64  24.90288  1.737e+00 3.402
-#>    21   parent    11.50  12.44765 -9.476e-01 3.402
-#>    21   parent    11.64  12.44765 -8.076e-01 3.402
-#>    35   parent     2.85   3.11002 -2.600e-01 3.402
-#>    35   parent     2.91   3.11002 -2.000e-01 3.402
-#>    50   parent     0.69   0.70374 -1.374e-02 3.402
-#>    50   parent     0.63   0.70374 -7.374e-02 3.402
-#>    75   parent     0.05   0.05913 -9.134e-03 3.402
-#>    75   parent     0.06   0.05913  8.662e-04 3.402
-#>     0       m1     0.00   0.00000  0.000e+00 2.722
-#>     0       m1     0.00   0.00000  0.000e+00 2.722
-#>     1       m1     4.84   4.81328  2.672e-02 2.722
-#>     1       m1     5.64   4.81328  8.267e-01 2.722
-#>     3       m1    12.91  13.04779 -1.378e-01 2.722
-#>     3       m1    12.96  13.04779 -8.779e-02 2.722
-#>     7       m1    22.97  25.07615 -2.106e+00 2.722
-#>     7       m1    24.47  25.07615 -6.062e-01 2.722
-#>    14       m1    41.69  36.70729  4.983e+00 2.722
-#>    14       m1    33.21  36.70729 -3.497e+00 2.722
-#>    21       m1    44.37  41.65050  2.720e+00 2.722
-#>    21       m1    46.44  41.65050  4.790e+00 2.722
-#>    35       m1    41.22  43.28866 -2.069e+00 2.722
-#>    35       m1    37.95  43.28866 -5.339e+00 2.722
-#>    50       m1    41.19  41.19339 -3.386e-03 2.722
-#>    50       m1    40.01  41.19339 -1.183e+00 2.722
-#>    75       m1    40.09  36.43820  3.652e+00 2.722
-#>    75       m1    33.85  36.43820 -2.588e+00 2.722
-#>   100       m1    31.04  31.98971 -9.497e-01 2.722
-#>   100       m1    33.13  31.98971  1.140e+00 2.722
-#>   120       m1    25.15  28.80898 -3.659e+00 2.722
-#>   120       m1    33.31  28.80898  4.501e+00 2.722
f.w.mean <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, weight = "mean", quiet = TRUE)
-summary(f.w.mean)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:27 2019 
-#> Date of summary: Mon Mar  4 14:05:27 2019 
-#> 
-#> 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 deSolve 
-#> 
-#> Fitted with method Port using 155 model solutions performed in 0.704 s
-#> 
-#> Weighting: mean
+#> Error model:
+#> NULL
 #> 
 #> Starting values for parameters to be optimised:
 #>                   value   type
@@ -889,6 +682,8 @@
 #> 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
@@ -896,6 +691,8 @@
 #> 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
@@ -903,331 +700,100 @@
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>                Estimate Std. Error    Lower    Upper
-#> parent_0        99.7300    1.93200 95.81000 103.6000
-#> log_k_parent    -2.3090    0.04837 -2.40700  -2.2110
-#> log_k_m1        -5.2550    0.12070 -5.49900  -5.0100
-#> f_parent_ilr_1   0.0354    0.06344 -0.09327   0.1641
+#> 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
-#> parent_0         1.0000       0.5004  -0.2143        -0.6514
-#> log_k_parent     0.5004       1.0000  -0.4282        -0.6383
-#> log_k_m1        -0.2143      -0.4282   1.0000         0.7390
-#> f_parent_ilr_1  -0.6514      -0.6383   0.7390         1.0000
-#> 
-#> Residual standard error: 0.09829 on 36 degrees of freedom
-#> 
-#> 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       99.730000  51.630 1.166e-35 95.81000 1.036e+02
-#> k_parent        0.099360  20.670 7.304e-22  0.09007 1.096e-01
-#> k_m1            0.005224   8.287 3.649e-10  0.00409 6.672e-03
-#> f_parent_to_m1  0.512500  22.860 2.497e-23  0.46710 5.578e-01
-#> 
-#> Chi2 error levels in percent:
-#>          err.min n.optim df
-#> All data   6.401       4 15
-#> parent     6.473       2  7
-#> m1         4.671       2  8
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.5125
-#> parent_sink 0.4875
-#> 
-#> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   6.976  23.18
-#> m1     132.696 440.81
-#> 
-#> Data:
-#>  time variable observed predicted   residual
-#>     0   parent    99.46  99.73057  -0.270570
-#>     0   parent   102.04  99.73057   2.309430
-#>     1   parent    93.50  90.29805   3.201945
-#>     1   parent    92.50  90.29805   2.201945
-#>     3   parent    63.23  74.02503 -10.795028
-#>     3   parent    68.99  74.02503  -5.035028
-#>     7   parent    52.32  49.74838   2.571618
-#>     7   parent    55.13  49.74838   5.381618
-#>    14   parent    27.27  24.81588   2.454124
-#>    14   parent    26.64  24.81588   1.824124
-#>    21   parent    11.50  12.37885  -0.878849
-#>    21   parent    11.64  12.37885  -0.738849
-#>    35   parent     2.85   3.08022  -0.230219
-#>    35   parent     2.91   3.08022  -0.170219
-#>    50   parent     0.69   0.69396  -0.003958
-#>    50   parent     0.63   0.69396  -0.063958
-#>    75   parent     0.05   0.05789  -0.007888
-#>    75   parent     0.06   0.05789   0.002112
-#>     0       m1     0.00   0.00000   0.000000
-#>     0       m1     0.00   0.00000   0.000000
-#>     1       m1     4.84   4.82149   0.018512
-#>     1       m1     5.64   4.82149   0.818512
-#>     3       m1    12.91  13.06669  -0.156692
-#>     3       m1    12.96  13.06669  -0.106692
-#>     7       m1    22.97  25.10106  -2.131058
-#>     7       m1    24.47  25.10106  -0.631058
-#>    14       m1    41.69  36.72092   4.969077
-#>    14       m1    33.21  36.72092  -3.510923
-#>    21       m1    44.37  41.64835   2.721647
-#>    21       m1    46.44  41.64835   4.791647
-#>    35       m1    41.22  43.26923  -2.049225
-#>    35       m1    37.95  43.26923  -5.319225
-#>    50       m1    41.19  41.17364   0.016361
-#>    50       m1    40.01  41.17364  -1.163639
-#>    75       m1    40.09  36.43122   3.658776
-#>    75       m1    33.85  36.43122  -2.581224
-#>   100       m1    31.04  31.99612  -0.956124
-#>   100       m1    33.13  31.99612   1.133876
-#>   120       m1    25.15  28.82413  -3.674128
-#>   120       m1    33.31  28.82413   4.485872
f.w.value <- mkinfit(SFO_SFO.ff, subset(FOCUS_2006_D, value != 0), err = "value",
-                     quiet = TRUE)
-summary(f.w.value)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:28 2019 
-#> Date of summary: Mon Mar  4 14:05:28 2019 
-#> 
-#> 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 deSolve 
-#> 
-#> Fitted with method Port using 174 model solutions performed in 0.866 s
-#> 
-#> Weighting: manual
-#> 
-#> 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
-#> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error    Lower    Upper
-#> parent_0        99.6600   2.712000 94.14000 105.2000
-#> log_k_parent    -2.2980   0.008118 -2.31500  -2.2820
-#> log_k_m1        -5.2410   0.096690 -5.43800  -5.0450
-#> f_parent_ilr_1   0.0231   0.057990 -0.09474   0.1409
-#> 
-#> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#> parent_0        1.00000       0.6843 -0.08687        -0.7564
-#> log_k_parent    0.68435       1.0000 -0.12695        -0.5812
-#> log_k_m1       -0.08687      -0.1269  1.00000         0.5195
-#> f_parent_ilr_1 -0.75644      -0.5812  0.51952         1.0000
-#> 
-#> Residual standard error: 0.08396 on 34 degrees of freedom
-#> 
-#> 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       99.660000   36.75 2.957e-29 94.14000 1.052e+02
-#> k_parent        0.100400  123.20 5.927e-47  0.09878 1.021e-01
-#> k_m1            0.005295   10.34 2.447e-12  0.00435 6.444e-03
-#> f_parent_to_m1  0.508200   24.79 1.184e-23  0.46660 5.497e-01
-#> 
-#> Chi2 error levels in percent:
-#>          err.min n.optim df
-#> All data   6.461       4 15
-#> parent     6.520       2  7
-#> m1         4.744       2  8
-#> 
-#> Resulting formation fractions:
-#>                 ff
-#> parent_m1   0.5082
-#> parent_sink 0.4918
-#> 
-#> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   6.902  22.93
-#> m1     130.916 434.89
-#> 
-#> Data:
-#>  time variable observed predicted   residual    err
-#>     0   parent    99.46  99.65571  -0.195715  99.46
-#>     0   parent   102.04  99.65571   2.384285 102.04
-#>     1   parent    93.50  90.13383   3.366170  93.50
-#>     1   parent    92.50  90.13383   2.366170  92.50
-#>     3   parent    63.23  73.73252 -10.502518  63.23
-#>     3   parent    68.99  73.73252  -4.742518  68.99
-#>     7   parent    52.32  49.34027   2.979728  52.32
-#>     7   parent    55.13  49.34027   5.789728  55.13
-#>    14   parent    27.27  24.42873   2.841271  27.27
-#>    14   parent    26.64  24.42873   2.211271  26.64
-#>    21   parent    11.50  12.09484  -0.594842  11.50
-#>    21   parent    11.64  12.09484  -0.454842  11.64
-#>    35   parent     2.85   2.96482  -0.114824   2.85
-#>    35   parent     2.91   2.96482  -0.054824   2.91
-#>    50   parent     0.69   0.65733   0.032670   0.69
-#>    50   parent     0.63   0.65733  -0.027330   0.63
-#>    75   parent     0.05   0.05339  -0.003386   0.05
-#>    75   parent     0.06   0.05339   0.006614   0.06
-#>     1       m1     4.84   4.82570   0.014301   4.84
-#>     1       m1     5.64   4.82570   0.814301   5.64
-#>     3       m1    12.91  13.06402  -0.154020  12.91
-#>     3       m1    12.96  13.06402  -0.104020  12.96
-#>     7       m1    22.97  25.04656  -2.076564  22.97
-#>     7       m1    24.47  25.04656  -0.576564  24.47
-#>    14       m1    41.69  36.53601   5.153988  41.69
-#>    14       m1    33.21  36.53601  -3.326012  33.21
-#>    21       m1    44.37  41.34639   3.023609  44.37
-#>    21       m1    46.44  41.34639   5.093609  46.44
-#>    35       m1    41.22  42.82669  -1.606690  41.22
-#>    35       m1    37.95  42.82669  -4.876690  37.95
-#>    50       m1    41.19  40.67342   0.516578  41.19
-#>    50       m1    40.01  40.67342  -0.663422  40.01
-#>    75       m1    40.09  35.91105   4.178947  40.09
-#>    75       m1    33.85  35.91105  -2.061053  33.85
-#>   100       m1    31.04  31.48161  -0.441612  31.04
-#>   100       m1    33.13  31.48161   1.648388  33.13
-#>   120       m1    25.15  28.32018  -3.170181  25.15
-#>   120       m1    33.31  28.32018   4.989819  33.31

-
# Manual weighting
-dw <- FOCUS_2006_D
-errors <- c(parent = 2, m1 = 1)
-dw$err.man <- errors[FOCUS_2006_D$name]
-f.w.man <- mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE)
-summary(f.w.man)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:30 2019 
-#> Date of summary: Mon Mar  4 14:05:30 2019 
-#> 
-#> 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 deSolve 
-#> 
-#> Fitted with method Port using 270 model solutions performed in 1.23 s
-#> 
-#> Weighting: manual
-#> 
-#> 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
-#> 
-#> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error   Lower    Upper
-#> parent_0       99.49000    1.33200 96.7800 102.2000
-#> log_k_parent   -2.32100    0.03550 -2.3930  -2.2490
-#> log_k_m1       -5.24100    0.21280 -5.6730  -4.8100
-#> f_parent_ilr_1  0.04571    0.08966 -0.1361   0.2275
-#> 
-#> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#> parent_0        1.00000       0.5312 -0.09456        -0.3351
-#> log_k_parent    0.53123       1.0000 -0.17800        -0.3360
-#> log_k_m1       -0.09456      -0.1780  1.00000         0.7616
-#> f_parent_ilr_1 -0.33514      -0.3360  0.76156         1.0000
-#> 
-#> Residual standard error: 2.628 on 36 degrees of freedom
+#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1 sigma_parent
+#> parent_0        1.00000      0.51078 -0.19133       -0.59997     0.035671
+#> log_k_parent    0.51078      1.00000 -0.37458       -0.59239     0.069834
+#> 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        0.024822
+#> 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       99.490000   74.69 2.221e-41 96.780000 1.022e+02
-#> k_parent        0.098140   28.17 2.012e-26  0.091320 1.055e-01
-#> k_m1            0.005292    4.70 1.873e-05  0.003437 8.148e-03
-#> f_parent_to_m1  0.516200   16.30 1.686e-18  0.452000 5.798e-01
+#> 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
 #> 
 #> Chi2 error levels in percent:
 #>          err.min n.optim df
-#> All data   6.400       4 15
-#> parent     6.454       2  7
-#> m1         4.708       2  8
+#> All data   6.398       4 15
+#> parent     6.464       2  7
+#> m1         4.682       2  8
 #> 
 #> Resulting formation fractions:
 #>                 ff
-#> parent_m1   0.5162
-#> parent_sink 0.4838
+#> parent_m1   0.5136
+#> parent_sink 0.4864
 #> 
 #> Estimated disappearance times:
 #>           DT50   DT90
-#> parent   7.063  23.46
-#> m1     130.971 435.08
+#> parent   7.003  23.26
+#> m1     132.154 439.01
 #> 
 #> Data:
-#>  time variable observed predicted   residual err
-#>     0   parent    99.46  99.48598  -0.025979   1
-#>     0   parent   102.04  99.48598   2.554021   1
-#>     1   parent    93.50  90.18612   3.313880   1
-#>     1   parent    92.50  90.18612   2.313880   1
-#>     3   parent    63.23  74.11316 -10.883163   1
-#>     3   parent    68.99  74.11316  -5.123163   1
-#>     7   parent    52.32  50.05030   2.269705   1
-#>     7   parent    55.13  50.05030   5.079705   1
-#>    14   parent    27.27  25.17975   2.090250   1
-#>    14   parent    26.64  25.17975   1.460250   1
-#>    21   parent    11.50  12.66765  -1.167654   1
-#>    21   parent    11.64  12.66765  -1.027654   1
-#>    35   parent     2.85   3.20616  -0.356164   1
-#>    35   parent     2.91   3.20616  -0.296164   1
-#>    50   parent     0.69   0.73562  -0.045619   1
-#>    50   parent     0.63   0.73562  -0.105619   1
-#>    75   parent     0.05   0.06326  -0.013256   1
-#>    75   parent     0.06   0.06326  -0.003256   1
-#>     0       m1     0.00   0.00000   0.000000   2
-#>     0       m1     0.00   0.00000   0.000000   2
-#>     1       m1     4.84   4.78729   0.052713   2
-#>     1       m1     5.64   4.78729   0.852713   2
-#>     3       m1    12.91  12.98785  -0.077848   2
-#>     3       m1    12.96  12.98785  -0.027848   2
-#>     7       m1    22.97  24.99695  -2.026946   2
-#>     7       m1    24.47  24.99695  -0.526946   2
-#>    14       m1    41.69  36.66353   5.026472   2
-#>    14       m1    33.21  36.66353  -3.453528   2
-#>    21       m1    44.37  41.65681   2.713186   2
-#>    21       m1    46.44  41.65681   4.783186   2
-#>    35       m1    41.22  43.35031  -2.130314   2
-#>    35       m1    37.95  43.35031  -5.400314   2
-#>    50       m1    41.19  41.25637  -0.066368   2
-#>    50       m1    40.01  41.25637  -1.246368   2
-#>    75       m1    40.09  36.46057   3.629429   2
-#>    75       m1    33.85  36.46057  -2.610571   2
-#>   100       m1    31.04  31.96929  -0.929293   2
-#>   100       m1    33.13  31.96929   1.160707   2
-#>   120       m1    25.15  28.76062  -3.610621   2
-#>   120       m1    33.31  28.76062   4.549379   2
f.w.man.irls <- mkinfit(SFO_SFO.ff, dw, err = "err.man", quiet = TRUE,
-                       reweight.method = "obs")
-summary(f.w.man.irls)
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:05:33 2019 
-#> Date of summary: Mon Mar  4 14:05:33 2019 
+#>  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  90.26333  3.237e+00
+#>     1   parent    92.50  90.26333  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  13.04197 -1.320e-01
+#>     3       m1    12.96  13.04197 -8.197e-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 -9.482e-03
+#>    50       m1    40.01  41.19948 -1.189e+00
+#>    75       m1    40.09  36.44036  3.650e+00
+#>    75       m1    33.85  36.44036 -2.590e+00
+#>   100       m1    31.04  31.98774 -9.477e-01
+#>   100       m1    33.13  31.98774  1.142e+00
+#>   120       m1    25.15  28.80430 -3.654e+00
+#>   120       m1    33.31  28.80430  4.506e+00
f.tc <- mkinfit(SFO_SFO.ff, FOCUS_2006_D, error_model = "tc", quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
summary(f.tc)
#> mkin version used for fitting:    0.9.49.4 
+#> R version used for fitting:       3.5.3 
+#> Date of fit:     Wed Apr 10 10:10:22 2019 
+#> Date of summary: Wed Apr 10 10:10:22 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent * parent
@@ -1235,14 +801,10 @@
 #> 
 #> Model predictions using solution type deSolve 
 #> 
-#> Fitted with method Port using 692 model solutions performed in 3.197 s
-#> 
-#> Weighting: manual
+#> Fitted with method using 756 model solutions performed in 3.222 s
 #> 
-#> Iterative reweighting with method obs 
-#> Final mean squared residuals of observed variables:
-#>    parent        m1 
-#> 11.573406  7.407846 
+#> Error model:
+#> NULL
 #> 
 #> Starting values for parameters to be optimised:
 #>                   value   type
@@ -1250,6 +812,8 @@
 #> k_parent         0.1000 deparm
 #> k_m1             0.1001 deparm
 #> f_parent_to_m1   0.5000 deparm
+#> sigma_low        0.5000  error
+#> rsd_high         0.0700  error
 #> 
 #> Starting values for the transformed parameters actually optimised:
 #>                     value lower upper
@@ -1257,95 +821,100 @@
 #> log_k_parent    -2.302585  -Inf   Inf
 #> log_k_m1        -2.301586  -Inf   Inf
 #> f_parent_ilr_1   0.000000  -Inf   Inf
+#> sigma_low        0.500000     0   Inf
+#> rsd_high         0.070000     0   Inf
 #> 
 #> Fixed parameter values:
 #>      value  type
 #> m1_0     0 state
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
-#>                Estimate Std. Error    Lower   Upper
-#> parent_0       99.67000    1.79200 96.04000 103.300
-#> log_k_parent   -2.31200    0.04560 -2.40400  -2.220
-#> log_k_m1       -5.25100    0.12510 -5.50500  -4.998
-#> f_parent_ilr_1  0.03785    0.06318 -0.09027   0.166
+#>                 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
 #> 
 #> Parameter correlation:
-#>                parent_0 log_k_parent log_k_m1 f_parent_ilr_1
-#> parent_0         1.0000       0.5083  -0.1979        -0.6148
-#> log_k_parent     0.5083       1.0000  -0.3894        -0.6062
-#> log_k_m1        -0.1979      -0.3894   1.0000         0.7417
-#> f_parent_ilr_1  -0.6148      -0.6062   0.7417         1.0000
-#> 
-#> Residual standard error: 1.054 on 36 degrees of freedom
+#>                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
 #> 
 #> 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       99.67000  55.630 8.185e-37 96.040000 1.033e+02
-#> k_parent        0.09906  21.930 1.016e-22  0.090310 1.087e-01
-#> k_m1            0.00524   7.996 8.486e-10  0.004066 6.753e-03
-#> f_parent_to_m1  0.51340  23.000 2.039e-23  0.468100 5.584e-01
+#>                 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
 #> 
 #> Chi2 error levels in percent:
 #>          err.min n.optim df
-#> All data   6.399       4 15
-#> parent     6.466       2  7
-#> m1         4.679       2  8
+#> All data   6.475       4 15
+#> parent     6.573       2  7
+#> m1         4.671       2  8
 #> 
 #> Resulting formation fractions:
 #>                 ff
-#> parent_m1   0.5134
-#> parent_sink 0.4866
+#> parent_m1   0.5084
+#> parent_sink 0.4916
 #> 
 #> Estimated disappearance times:
-#>           DT50   DT90
-#> parent   6.997  23.24
-#> m1     132.282 439.43
+#>           DT50  DT90
+#> parent   6.893  22.9
+#> m1     134.156 445.7
 #> 
 #> Data:
-#>  time variable observed predicted   residual err.ini   err
-#>     0   parent    99.46  99.67217 -2.122e-01       1 3.402
-#>     0   parent   102.04  99.67217  2.368e+00       1 3.402
-#>     1   parent    93.50  90.27152  3.228e+00       1 3.402
-#>     1   parent    92.50  90.27152  2.228e+00       1 3.402
-#>     3   parent    63.23  74.04648 -1.082e+01       1 3.402
-#>     3   parent    68.99  74.04648 -5.056e+00       1 3.402
-#>     7   parent    52.32  49.82092  2.499e+00       1 3.402
-#>     7   parent    55.13  49.82092  5.309e+00       1 3.402
-#>    14   parent    27.27  24.90288  2.367e+00       1 3.402
-#>    14   parent    26.64  24.90288  1.737e+00       1 3.402
-#>    21   parent    11.50  12.44765 -9.477e-01       1 3.402
-#>    21   parent    11.64  12.44765 -8.077e-01       1 3.402
-#>    35   parent     2.85   3.11002 -2.600e-01       1 3.402
-#>    35   parent     2.91   3.11002 -2.000e-01       1 3.402
-#>    50   parent     0.69   0.70375 -1.375e-02       1 3.402
-#>    50   parent     0.63   0.70375 -7.375e-02       1 3.402
-#>    75   parent     0.05   0.05913 -9.134e-03       1 3.402
-#>    75   parent     0.06   0.05913  8.661e-04       1 3.402
-#>     0       m1     0.00   0.00000  0.000e+00       2 2.722
-#>     0       m1     0.00   0.00000  0.000e+00       2 2.722
-#>     1       m1     4.84   4.81328  2.672e-02       2 2.722
-#>     1       m1     5.64   4.81328  8.267e-01       2 2.722
-#>     3       m1    12.91  13.04779 -1.378e-01       2 2.722
-#>     3       m1    12.96  13.04779 -8.779e-02       2 2.722
-#>     7       m1    22.97  25.07615 -2.106e+00       2 2.722
-#>     7       m1    24.47  25.07615 -6.062e-01       2 2.722
-#>    14       m1    41.69  36.70729  4.983e+00       2 2.722
-#>    14       m1    33.21  36.70729 -3.497e+00       2 2.722
-#>    21       m1    44.37  41.65050  2.719e+00       2 2.722
-#>    21       m1    46.44  41.65050  4.789e+00       2 2.722
-#>    35       m1    41.22  43.28866 -2.069e+00       2 2.722
-#>    35       m1    37.95  43.28866 -5.339e+00       2 2.722
-#>    50       m1    41.19  41.19339 -3.387e-03       2 2.722
-#>    50       m1    40.01  41.19339 -1.183e+00       2 2.722
-#>    75       m1    40.09  36.43820  3.652e+00       2 2.722
-#>    75       m1    33.85  36.43820 -2.588e+00       2 2.722
-#>   100       m1    31.04  31.98971 -9.497e-01       2 2.722
-#>   100       m1    33.13  31.98971  1.140e+00       2 2.722
-#>   120       m1    25.15  28.80897 -3.659e+00       2 2.722
-#>   120       m1    33.31  28.80897  4.501e+00       2 2.722
+#> 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 -0.692316 +#> 21 parent 11.64 12.19232 -0.552316 +#> 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 -2.394167 +#> 7 m1 24.47 25.36417 -0.894167 +#> 14 m1 41.69 37.00974 4.680262 +#> 14 m1 33.21 37.00974 -3.799738 +#> 21 m1 44.37 41.90133 2.468668 +#> 21 m1 46.44 41.90133 4.538668 +#> 35 m1 41.22 43.45691 -2.236914 +#> 35 m1 37.95 43.45691 -5.506914 +#> 50 m1 41.19 41.34199 -0.151986 +#> 50 m1 40.01 41.34199 -1.331986 +#> 75 m1 40.09 36.61470 3.475295 +#> 75 m1 33.85 36.61470 -2.764705 +#> 100 m1 31.04 32.20082 -1.160823 +#> 100 m1 33.13 32.20082 0.929177 +#> 120 m1 25.15 29.04130 -3.891303 +#> 120 m1 33.31 29.04130 4.268697
+
@@ -234,7 +234,7 @@ For the definition of model types and their parameters, the equations given SFO_SFO <- mkinmod( parent = mkinsub("SFO", "m1"), m1 = mkinsub("SFO"), verbose = TRUE)
#> Compilation argument: -#> /usr/lib/R/bin/R CMD SHLIB filef43390002d2.c 2> filef43390002d2.c.err.txt +#> /usr/lib/R/bin/R CMD SHLIB file27ad440ff595.c 2> file27ad440ff595.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 ba7212c5..82b9c212 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 e819befe..c90acf4a 100644 --- a/docs/reference/mkinparplot.html +++ b/docs/reference/mkinparplot.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4
@@ -154,8 +154,8 @@ 
model <- mkinmod(
   T245 = mkinsub("SFO", to = c("phenol"), sink = FALSE),
   phenol = mkinsub("SFO", to = c("anisole")),
-  anisole = mkinsub("SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
fit <- mkinfit(model, subset(mccall81_245T, soil == "Commerce"), quiet = TRUE)
-mkinparplot(fit)
+ 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
#> Warning: Optimisation did not converge: +#> false convergence (8)
mkinparplot(fit)
diff --git a/docs/reference/mkinpredict.html b/docs/reference/mkinpredict.html index fb4afa9a..56c1ed27 100644 --- a/docs/reference/mkinpredict.html +++ b/docs/reference/mkinpredict.html @@ -65,7 +65,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -333,50 +333,48 @@ c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve")[201,]))
#> time parent m1 #> 201 20 4.978707 27.46227
#> User System verstrichen -#> 0.002 0.000 0.002
system.time( +#> 0.002 0.000 0.001
system.time( print(mkinpredict(SFO_SFO, c(k_parent_m1 = 0.05, k_parent_sink = 0.1, k_m1_sink = 0.01), c(parent = 100, m1 = 0), seq(0, 20, by = 0.1), solution_type = "deSolve", use_compiled = FALSE)[201,]))
#> time parent m1 #> 201 20 4.978707 27.46227
#> User System verstrichen -#> 0.039 0.000 0.039
+#> 0.04 0.00 0.04
# Predict from a fitted model - f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Model cost at call 1 : 552.5739 -#> Model cost at call 3 : 552.5739 -#> Model cost at call 4 : 552.5739 -#> Model cost at call 6 : 279.9345 -#> Model cost at call 7 : 279.9344 -#> Model cost at call 8 : 279.9294 -#> Model cost at call 9 : 279.9294 -#> Model cost at call 12 : 200.3629 -#> Model cost at call 13 : 200.3629 -#> Model cost at call 18 : 197.9039 -#> Model cost at call 23 : 197.9039 -#> Model cost at call 25 : 196.6754 -#> Model cost at call 27 : 196.6754 -#> Model cost at call 32 : 196.5742 -#> Model cost at call 33 : 196.5742 -#> Model cost at call 34 : 196.5742 -#> Model cost at call 38 : 196.5361 -#> Model cost at call 40 : 196.5361 -#> Model cost at call 44 : 196.5336 -#> Model cost at call 45 : 196.5336 -#> Model cost at call 50 : 196.5334 -#> Model cost at call 51 : 196.5334 -#> Model cost at call 52 : 196.5334 -#> Model cost at call 56 : 196.5334 -#> Model cost at call 58 : 196.5334 -#> Model cost at call 59 : 196.5334 -#> Model cost at call 65 : 196.5334 -#> Model cost at call 73 : 196.5334 -#> Model cost at call 78 : 196.5334 -#> Model cost at call 80 : 196.5334 -#> Optimisation by method Port successfully terminated.
head(mkinpredict(f))
#> time parent m1 + f <- mkinfit(SFO_SFO, FOCUS_2006_C)
#> Negative log-likelihood at call 1: 552.5739 +#> Negative log-likelihood at call 3: 552.5739 +#> Negative log-likelihood at call 4: 552.5739 +#> Negative log-likelihood at call 6: 279.9345 +#> Negative log-likelihood at call 7: 279.9344 +#> Negative log-likelihood at call 8: 279.9294 +#> Negative log-likelihood at call 9: 279.9294 +#> Negative log-likelihood at call 12: 200.3629 +#> Negative log-likelihood at call 13: 200.3629 +#> Negative log-likelihood at call 18: 197.9039 +#> Negative log-likelihood at call 25: 196.6754 +#> Negative log-likelihood at call 27: 196.6754 +#> Negative log-likelihood at call 32: 196.5742 +#> Negative log-likelihood at call 33: 196.5742 +#> Negative log-likelihood at call 34: 196.5742 +#> Negative log-likelihood at call 38: 196.5361 +#> Negative log-likelihood at call 40: 196.5361 +#> Negative log-likelihood at call 44: 196.5336 +#> Negative log-likelihood at call 45: 196.5336 +#> Negative log-likelihood at call 50: 196.5334 +#> Negative log-likelihood at call 51: 196.5334 +#> Negative log-likelihood at call 52: 196.5334 +#> Negative log-likelihood at call 56: 196.5334 +#> Negative log-likelihood at call 58: 196.5334 +#> Negative log-likelihood at call 59: 196.5334 +#> Negative log-likelihood at call 65: 196.5334 +#> Negative log-likelihood at call 75: 26.64668 +#> Negative log-likelihood at call 103: 26.64668 +#> Optimisation successfully terminated.
head(mkinpredict(f))
#> time parent m1 #> 1 0.0 82.49216 0.000000 -#> 2 0.1 80.00563 1.179955 -#> 3 0.2 77.59404 2.312580 -#> 4 0.3 75.25515 3.399419 -#> 5 0.4 72.98675 4.441969 -#> 6 0.5 70.78673 5.441679
+#> 2 0.1 80.00563 1.179956 +#> 3 0.2 77.59404 2.312583 +#> 4 0.3 75.25515 3.399424 +#> 5 0.4 72.98675 4.441974 +#> 6 0.5 70.78673 5.441685
@@ -199,8 +199,7 @@

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)
-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
mkinresplot(fit, "m1")
diff --git a/docs/reference/mmkin-3.png b/docs/reference/mmkin-3.png index 952e6af3..4743eabd 100644 Binary files a/docs/reference/mmkin-3.png and b/docs/reference/mmkin-3.png differ diff --git a/docs/reference/mmkin-5.png b/docs/reference/mmkin-5.png index 7ab171d1..5e1527fa 100644 Binary files a/docs/reference/mmkin-5.png and b/docs/reference/mmkin-5.png differ diff --git a/docs/reference/mmkin.html b/docs/reference/mmkin.html index af963d13..68451ca6 100644 --- a/docs/reference/mmkin.html +++ b/docs/reference/mmkin.html @@ -64,7 +64,7 @@ mkin - 0.9.48.1 + 0.9.49.4 @@ -194,11 +194,11 @@ time_1 <- system.time(fits.4 <- mmkin(models, datasets, cores = 1, quiet = TRUE)) time_default
#> User System verstrichen -#> 0.049 0.025 6.950
time_1
#> User System verstrichen -#> 22.404 0.000 22.417
+#> 0.034 0.041 5.124
time_1
#> User System verstrichen +#> 19.594 0.000 19.605
endpoints(fits.0[["SFO_lin", 2]])
#> $ff #> parent_M1 parent_sink M1_M2 M1_sink -#> 0.7340480 0.2659520 0.7505686 0.2494314 +#> 0.7340481 0.2659519 0.7505683 0.2494317 #> #> $SFORB #> logical(0) @@ -206,8 +206,8 @@ #> $distimes #> DT50 DT90 #> parent 0.8777689 2.915885 -#> M1 2.3257453 7.725959 -#> M2 33.7200874 112.015706 +#> M1 2.3257442 7.725955 +#> M2 33.7200941 112.015728 #>
# 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/reference/nafta.html b/docs/reference/nafta.html index f12e24e0..93bc3955 100644 --- a/docs/reference/nafta.html +++ b/docs/reference/nafta.html @@ -65,7 +65,7 @@ mkin - 0.9.48.1 + 0.9.49.4
@@ -182,8 +182,7 @@

Examples

- 
  nafta_evaluation <- nafta(NAFTA_SOP_Appendix_D, cores = 1)
#> Warning: Calculation of the Jacobian failed for the cost function of the untransformed model.
-#> No t-test results will be available
#> 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)
#> 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 
 #> 
@@ -192,22 +191,25 @@
 #> 
 #> Parameters:
 #> $SFO
-#>               Estimate   Pr(>t)    Lower    Upper
-#> parent_0       83.7558 8.08e-15 76.92822 90.58328
-#> k_parent_sink   0.0017 7.45e-05  0.00111  0.00262
+#>               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
 #> 
 #> $IORE
 #>                     Estimate Pr(>t)    Lower    Upper
-#> parent_0            9.69e+01     NA 8.75e+01 1.06e+02
-#> k__iore_parent_sink 8.40e-14     NA 1.09e-19 6.47e-08
-#> N_parent            6.68e+00     NA 3.54e+00 9.83e+00
+#> 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
 #> 
 #> $DFOP
 #>          Estimate   Pr(>t)    Lower    Upper
-#> parent_0 9.76e+01 4.44e-13 8.88e+01 1.06e+02
-#> k1       4.24e-02 3.55e-02 1.41e-02 1.27e-01
-#> k2       8.24e-04 2.06e-02 3.17e-04 2.14e-03
-#> g        2.88e-01 1.31e-04 1.78e-01 4.30e-01
+#> parent_0 9.76e+01 1.94e-13 9.02e+01 1.05e+02
+#> k1       4.24e-02 5.92e-03 2.03e-02 8.88e-02
+#> k2       8.24e-04 6.48e-03 3.89e-04 1.75e-03
+#> g        2.88e-01 2.47e-05 1.95e-01 4.03e-01
+#> sigma    5.36e+00 2.22e-05 3.43e+00 7.30e+00
 #> 
 #> 
 #> DTx values:
@@ -217,7 +219,7 @@
 #> DFOP  429    2380      841
 #> 
 #> Representative half-life:
-#> [1] 841.4096
  plot(nafta_evaluation)

+#> [1] 841.4094
  plot(nafta_evaluation)
@@ -261,10 +261,9 @@ plot_sep(fit, sep_obs = TRUE, show_residuals = TRUE, show_errmin = TRUE, …

Examples

# One parent compound, one metabolite, both single first order, path from
-# parent to sink included, use Levenberg-Marquardt for speed
+# parent to sink included
 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, method.modFit = "Marq")
-plot(fit)
plot(fit, show_residuals = TRUE)

+                   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
plot(fit)
plot(fit, show_residuals = TRUE)

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

 # Show the observed variables separately, with residuals
diff --git a/docs/reference/plot.mmkin-1.png b/docs/reference/plot.mmkin-1.png
index 2aa7aad9..60e602d4 100644
Binary files a/docs/reference/plot.mmkin-1.png and b/docs/reference/plot.mmkin-1.png differ
diff --git a/docs/reference/plot.mmkin-2.png b/docs/reference/plot.mmkin-2.png
index 94b1332a..f1e4318c 100644
Binary files a/docs/reference/plot.mmkin-2.png and b/docs/reference/plot.mmkin-2.png differ
diff --git a/docs/reference/plot.mmkin-3.png b/docs/reference/plot.mmkin-3.png
index ca78fa42..04762756 100644
Binary files a/docs/reference/plot.mmkin-3.png and b/docs/reference/plot.mmkin-3.png differ
diff --git a/docs/reference/plot.mmkin.html b/docs/reference/plot.mmkin.html
index e1651208..b3f2de66 100644
--- a/docs/reference/plot.mmkin.html
+++ b/docs/reference/plot.mmkin.html
@@ -67,7 +67,7 @@ If the current plot device is a tikz device,
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
@@ -187,10 +187,10 @@ If the current plot device is a tikz device,
     
 
     
Examples

-    
  # Only use one core not to offend CRAN checks, use Levenberg-Marquardt for speed
+    
  # Only use one core not to offend CRAN checks
   fits <- mmkin(c("FOMC", "HS"),
                 list("FOCUS B" = FOCUS_2006_B, "FOCUS C" = FOCUS_2006_C), # named list for titles
-                cores = 1, quiet = TRUE, method.modFit = "Marq")
+                cores = 1, quiet = TRUE)
   plot(fits[, "FOCUS C"])
  plot(fits["FOMC", ])

   # 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
diff --git a/docs/reference/plot.nafta.html b/docs/reference/plot.nafta.html
index 753cbd6a..1aa4485a 100644
--- a/docs/reference/plot.nafta.html
+++ b/docs/reference/plot.nafta.html
@@ -67,7 +67,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/print.mkinds.html b/docs/reference/print.mkinds.html
index 96607b02..2cc112aa 100644
--- a/docs/reference/print.mkinds.html
+++ b/docs/reference/print.mkinds.html
@@ -63,7 +63,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/print.mkinmod.html b/docs/reference/print.mkinmod.html
index 7e138697..d09b629e 100644
--- a/docs/reference/print.mkinmod.html
+++ b/docs/reference/print.mkinmod.html
@@ -63,7 +63,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/print.nafta.html b/docs/reference/print.nafta.html
index 1b35053f..28847afe 100644
--- a/docs/reference/print.nafta.html
+++ b/docs/reference/print.nafta.html
@@ -65,7 +65,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
diff --git a/docs/reference/schaefer07_complex_case-1.png b/docs/reference/schaefer07_complex_case-1.png
index 6a621369..49967dc9 100644
Binary files a/docs/reference/schaefer07_complex_case-1.png and b/docs/reference/schaefer07_complex_case-1.png differ
diff --git a/docs/reference/schaefer07_complex_case.html b/docs/reference/schaefer07_complex_case.html
index 1e8a6d9e..fec2a187 100644
--- a/docs/reference/schaefer07_complex_case.html
+++ b/docs/reference/schaefer07_complex_case.html
@@ -65,7 +65,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
@@ -167,7 +167,7 @@
   A2 = list(type = "SFO"), use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.
  
    fit <- mkinfit(model, data, quiet = TRUE)
     plot(fit)
    endpoints(fit)
#> $ff
 #>   parent_A1   parent_B1   parent_C1 parent_sink       A1_A2     A1_sink 
-#>   0.3809621   0.1954665   0.4235714   0.0000000   0.4479674   0.5520326 
+#>   0.3809619   0.1954667   0.4235714   0.0000000   0.4479605   0.5520395 
 #> 
 #> $SFORB
 #> logical(0)
@@ -175,10 +175,10 @@
 #> $distimes
 #>            DT50      DT90
 #> parent 13.95078  46.34350
-#> A1     49.75342 165.27728
-#> B1     37.26913 123.80536
-#> C1     11.23133  37.30968
-#> A2     28.50591  94.69457
+#> A1     49.75344 165.27734
+#> B1     37.26908 123.80520
+#> C1     11.23130  37.30958
+#> A2     28.50644  94.69634
 #> 
  
 # Compare with the results obtained in the original publication
  print(schaefer07_complex_results)
#>         compound          parameter  KinGUI ModelMaker deviation
 #> 1         parent   degradation rate  0.0496     0.0506       2.0
diff --git a/docs/reference/sigma_twocomp.html b/docs/reference/sigma_twocomp.html
index eca5deec..265c7d1f 100644
--- a/docs/reference/sigma_twocomp.html
+++ b/docs/reference/sigma_twocomp.html
@@ -68,7 +68,7 @@ This is the error model used for example by Werner et al. (1978). The model
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/summary.mkinfit.html b/docs/reference/summary.mkinfit.html
index f561e258..e5565990 100644
--- a/docs/reference/summary.mkinfit.html
+++ b/docs/reference/summary.mkinfit.html
@@ -32,10 +32,10 @@
 
 
 
-
+
 
 
 
@@ -66,7 +66,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     
 
@@ -131,10 +131,10 @@
 
     

     
-    
Lists model equations, the summary as returned by summary.modFit,
-  the chi2 error levels calculated according to FOCUS guidance (2006) as far
-  as defined therein, and optionally the data, consisting of observed, predicted
-  and residual values.

+    
Lists model equations, initial parameter values, optimised parameters with 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.

     
     

 
@@ -178,8 +178,7 @@
     
     
Value

 
-    
The summary function returns a list derived from
-  summary.modFit, with components, among others

+    
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

   
use_of_ff
Was maximum or minimum use made of formation fractions

@@ -193,6 +192,7 @@
   
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.

@@ -209,52 +209,56 @@
     
 
     
Examples

-    
  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.48.1 
-#> R version used for fitting:       3.5.2 
-#> Date of fit:     Mon Mar  4 14:06:25 2019 
-#> Date of summary: Mon Mar  4 14:06:25 2019 
+    
  summary(mkinfit(mkinmod(parent = mkinsub("SFO")), FOCUS_2006_A, quiet = TRUE))
#> mkin version used for fitting:    0.9.49.4 
+#> R version used for fitting:       3.5.3 
+#> Date of fit:     Wed Apr 10 10:11:15 2019 
+#> Date of summary: Wed Apr 10 10:11:15 2019 
 #> 
 #> Equations:
 #> d_parent/dt = - k_parent_sink * parent
 #> 
 #> Model predictions using solution type analytical 
 #> 
-#> Fitted with method Port using 35 model solutions performed in 0.085 s
+#> Fitted with method using 131 model solutions performed in 0.284 s
 #> 
-#> Weighting: none
+#> Error model:
+#> NULL
 #> 
 #> Starting values for parameters to be optimised:
-#>                value   type
-#> parent_0      101.24  state
-#> k_parent_sink   0.10 deparm
+#>                    value   type
+#> parent_0      101.240000  state
+#> k_parent_sink   0.100000 deparm
+#> sigma           5.265546  error
 #> 
 #> 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
+#> sigma               5.265546     0   Inf
 #> 
 #> Fixed parameter values:
 #> None
 #> 
 #> Optimised, transformed parameters with symmetric confidence intervals:
 #>                   Estimate Std. Error  Lower   Upper
-#> parent_0           109.200     4.3910 98.410 119.900
-#> log_k_parent_sink   -3.291     0.1152 -3.573  -3.009
+#> 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
 #> 
 #> Parameter correlation:
-#>                   parent_0 log_k_parent_sink
-#> parent_0             1.000             0.575
-#> log_k_parent_sink    0.575             1.000
-#> 
-#> Residual standard error: 6.08 on 6 degrees of freedom
+#>                    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
 #> 
 #> 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  24.860 1.394e-07 98.41000 119.90000
-#> k_parent_sink   0.03722   8.679 6.457e-05  0.02807   0.04934
+#>                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
 #> 
 #> Chi2 error levels in percent:
 #>          err.min n.optim df
diff --git a/docs/reference/synthetic_data_for_UBA.html b/docs/reference/synthetic_data_for_UBA.html
index 6e36e72f..2c2623e4 100644
--- a/docs/reference/synthetic_data_for_UBA.html
+++ b/docs/reference/synthetic_data_for_UBA.html
@@ -78,7 +78,7 @@ Compare also the code in the example section to see the degradation models." />
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
diff --git a/docs/reference/test_data_from_UBA_2014-1.png b/docs/reference/test_data_from_UBA_2014-1.png
index 47670389..9157a6a1 100644
Binary files a/docs/reference/test_data_from_UBA_2014-1.png and b/docs/reference/test_data_from_UBA_2014-1.png differ
diff --git a/docs/reference/test_data_from_UBA_2014-2.png b/docs/reference/test_data_from_UBA_2014-2.png
index 8d282842..528f3987 100644
Binary files a/docs/reference/test_data_from_UBA_2014-2.png and b/docs/reference/test_data_from_UBA_2014-2.png differ
diff --git a/docs/reference/test_data_from_UBA_2014.html b/docs/reference/test_data_from_UBA_2014.html
index 28e8b41c..dfb49619 100644
--- a/docs/reference/test_data_from_UBA_2014.html
+++ b/docs/reference/test_data_from_UBA_2014.html
@@ -64,7 +64,7 @@
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
@@ -158,23 +158,24 @@
   # large parameter correlations, among other reasons (e.g. the adequacy of the
   # model).
   m_ws <- mkinmod(parent_w = mkinsub("SFO", "parent_s"),
-                  parent_s = mkinsub("SFO", "parent_w"))
#> Successfully compiled differential equation model from auto-generated C code.
  f_river <- mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE)
-  plot_sep(f_river)

-  summary(f_river)$bpar
#> Warning: Could not estimate covariance matrix; singular system.
#>                         Estimate se_notrans      t value       Pr(>t) Lower
-#> parent_w_0          9.598567e+01 2.33959800 4.102657e+01 9.568967e-19    NA
-#> k_parent_w_sink     3.603743e-01 0.03497750 1.030303e+01 4.989002e-09    NA
-#> k_parent_w_parent_s 6.031371e-02 0.01746024 3.454346e+00 1.514723e-03    NA
-#> k_parent_s_sink     5.108539e-11 0.10382001 4.920572e-10 5.000000e-01    NA
-#> k_parent_s_parent_w 7.419672e-02 0.11338240 6.543936e-01 2.608069e-01    NA
-#>                     Upper
-#> parent_w_0             NA
-#> k_parent_w_sink        NA
-#> k_parent_w_parent_s    NA
-#> k_parent_s_sink        NA
-#> k_parent_s_parent_w    NA
  mkinerrmin(f_river)
#>             err.min n.optim df
+                  parent_s = mkinsub("SFO", "parent_w"))
#> Successfully compiled differential equation model from auto-generated C code.
  f_river <- mkinfit(m_ws, test_data_from_UBA_2014[[1]]$data, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
  plot_sep(f_river)

+  summary(f_river)$bpar
#>                         Estimate se_notrans      t value       Pr(>t)
+#> parent_w_0          9.598567e+01 2.12352039 4.520120e+01 9.476357e-18
+#> k_parent_w_sink     3.603743e-01 0.03149366 1.144276e+01 4.128096e-09
+#> k_parent_w_parent_s 6.031371e-02 0.01603582 3.761185e+00 9.436293e-04
+#> k_parent_s_sink     7.560341e-11 0.09483761 7.971881e-10 5.000000e-01
+#> k_parent_s_parent_w 7.419672e-02 0.10738374 6.909493e-01 2.500756e-01
+#> sigma               2.982879e+00 0.50546582 5.901247e+00 1.454824e-05
+#>                           Lower       Upper
+#> parent_w_0          91.48420501 100.4871438
+#> k_parent_w_sink      0.30668904   0.4234571
+#> k_parent_w_parent_s  0.03423904   0.1062455
+#> k_parent_s_sink      0.00000000         Inf
+#> k_parent_s_parent_w  0.02289956   0.2404043
+#> sigma                2.00184022   3.9639169
  mkinerrmin(f_river)
#>             err.min n.optim df
 #> All data 0.09246946       5  6
 #> parent_w 0.06377096       3  3
-#> parent_s 0.20882324       2  3

+#> parent_s 0.20882325       2  3

   # This is the evaluation used for the validation of software packages
   # in the expertise from 2014
   m_soil <- mkinmod(parent = mkinsub("SFO", c("M1", "M2")),
@@ -182,27 +183,28 @@
                     M2 = mkinsub("SFO", "M3"),
                     M3 = mkinsub("SFO"),
                     use_of_ff = "max")
#> Successfully compiled differential equation model from auto-generated C code.

-  f_soil <- mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE)
-  plot_sep(f_soil, lpos = c("topright", "topright", "topright", "bottomright"))
  summary(f_soil)$bpar
#>                   Estimate  se_notrans    t value       Pr(>t)        Lower
-#> parent_0       76.55425584 0.943443794 81.1434198 4.422336e-30 74.602593773
-#> k_parent        0.12081956 0.004815515 25.0896457 1.639665e-18  0.111257517
-#> k_M1            0.84258651 0.930121548  0.9058886 1.871938e-01  0.085876516
-#> k_M2            0.04210878 0.013729902  3.0669396 2.729137e-03  0.021450630
-#> k_M3            0.01122919 0.008044865  1.3958206 8.804912e-02  0.002550984
-#> f_parent_to_M1  0.32240199 0.278620579  1.1571363 1.295467e-01           NA
-#> f_parent_to_M2  0.16099854 0.030548888  5.2701931 1.196190e-05           NA
-#> f_M1_to_M3      0.27921500 0.314732842  0.8871492 1.920908e-01  0.015016983
-#> f_M2_to_M3      0.55641333 0.650246995  0.8556954 2.004966e-01  0.005360555
+  f_soil <- mkinfit(m_soil, test_data_from_UBA_2014[[3]]$data, quiet = TRUE)
#> Warning: Observations with value of zero were removed from the data
  plot_sep(f_soil, lpos = c("topright", "topright", "topright", "bottomright"))
  summary(f_soil)$bpar
#>                   Estimate  se_notrans    t value       Pr(>t)        Lower
+#> parent_0       76.55425584 0.859186614 89.1008479 1.113866e-26 74.755959751
+#> k_parent        0.12081956 0.004601922 26.2541548 1.077373e-16  0.111561582
+#> k_M1            0.84258651 0.806231481  1.0450925 1.545475e-01  0.113839756
+#> k_M2            0.04210878 0.017083049  2.4649452 1.170195e-02  0.018013807
+#> k_M3            0.01122919 0.007245890  1.5497322 6.885127e-02  0.002909463
+#> f_parent_to_M1  0.32240199 0.240803570  1.3388589 9.820821e-02           NA
+#> f_parent_to_M2  0.16099854 0.033691991  4.7785403 6.531225e-05           NA
+#> f_M1_to_M3      0.27921500 0.269443517  1.0362654 1.565440e-01  0.022992921
+#> f_M2_to_M3      0.55641333 0.595125456  0.9349513 1.807725e-01  0.008003316
+#> sigma           1.14005399 0.149696423  7.6157731 1.727024e-07  0.826735778
 #>                      Upper
-#> parent_0       78.50591790
-#> k_parent        0.13120341
-#> k_M1            8.26712661
-#> k_M2            0.08266188
-#> k_M3            0.04942981
+#> parent_0       78.35255192
+#> k_parent        0.13084582
+#> k_M1            6.23641562
+#> k_M2            0.09843279
+#> k_M3            0.04333950
 #> f_parent_to_M1          NA
 #> f_parent_to_M2          NA
-#> f_M1_to_M3      0.90777163
-#> f_M2_to_M3      0.99658633
  mkinerrmin(f_soil)
#>             err.min n.optim df
+#> f_M1_to_M3      0.86443090
+#> f_M2_to_M3      0.99489847
+#> sigma           1.45337221
  mkinerrmin(f_soil)
#>             err.min n.optim df
 #> All data 0.09649963       9 20
 #> parent   0.04721283       2  6
 #> M1       0.26551209       2  5
diff --git a/docs/reference/transform_odeparms.html b/docs/reference/transform_odeparms.html
index fbc792f1..7e05480e 100644
--- a/docs/reference/transform_odeparms.html
+++ b/docs/reference/transform_odeparms.html
@@ -71,7 +71,7 @@ The transformation of sets of formation fractions is fragile, as it supposes
       
       
         mkin
-        0.9.48.1
+        0.9.49.4
       
     

 
@@ -202,30 +202,32 @@ The transformation of sets of formation fractions is fragile, as it supposes
     
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)
-fit.s <- summary(fit)
+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$par, 3)
#>                   Estimate Std. Error Lower  Upper
-#> parent_0             99.60     1.6137 96.33 102.87
-#> log_k_parent_sink    -3.04     0.0783 -3.20  -2.88
-#> log_k_parent_m1      -2.98     0.0412 -3.06  -2.90
-#> log_k_m1_sink        -5.25     0.1361 -5.52  -4.97
print(fit.s$bpar, 3)
#>               Estimate se_notrans t value   Pr(>t)    Lower    Upper
-#> parent_0      99.59848   1.613712   61.72 2.02e-38 96.32572 1.03e+02
-#> k_parent_sink  0.04792   0.003750   12.78 3.05e-15  0.04089 5.62e-02
-#> k_parent_m1    0.05078   0.002094   24.25 3.41e-24  0.04670 5.52e-02
-#> k_m1_sink      0.00526   0.000716    7.35 5.76e-09  0.00399 6.93e-03

+#> 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

 
# Compare to the version without transforming rate parameters
-fit.2 <- mkinfit(SFO_SFO, FOCUS_2006_D, transform_rates = FALSE, quiet = TRUE)
-fit.2.s <- summary(fit.2)
+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$par, 3)
#>               Estimate Std. Error    Lower    Upper
-#> parent_0      99.59848   1.613710 96.32573 1.03e+02
-#> k_parent_sink  0.04792   0.003750  0.04031 5.55e-02
-#> k_parent_m1    0.05078   0.002094  0.04653 5.50e-02
-#> k_m1_sink      0.00526   0.000716  0.00381 6.71e-03
print(fit.2.s$bpar, 3)
#>               Estimate se_notrans t value   Pr(>t)    Lower    Upper
-#> parent_0      99.59848   1.613710   61.72 2.02e-38 96.32573 1.03e+02
-#> k_parent_sink  0.04792   0.003750   12.78 3.05e-15  0.04031 5.55e-02
-#> k_parent_m1    0.05078   0.002094   24.25 3.41e-24  0.04653 5.50e-02
-#> k_m1_sink      0.00526   0.000716    7.35 5.76e-09  0.00381 6.71e-03

+#> 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

 initials <- fit$start$value
 names(initials) <- rownames(fit$start)
 transformed <- fit$start_transformed$value
@@ -238,17 +240,18 @@ The transformation of sets of formation fractions is fragile, as it supposes
   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)
-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
fit.ff.s <- summary(fit.ff)
 print(fit.ff.s$par, 3)
#>                Estimate Std. Error   Lower   Upper
-#> parent_0         99.598     1.6137 96.3257 102.871
-#> log_k_parent     -2.316     0.0419 -2.4006  -2.231
-#> log_k_m1         -5.248     0.1361 -5.5235  -4.972
-#> f_parent_ilr_1    0.041     0.0648 -0.0904   0.172
print(fit.ff.s$bpar, 3)
#>                Estimate se_notrans t value   Pr(>t)    Lower    Upper
-#> parent_0       99.59848   1.613712   61.72 2.02e-38 96.32574 1.03e+02
-#> k_parent        0.09870   0.004132   23.88 5.70e-24  0.09066 1.07e-01
-#> k_m1            0.00526   0.000716    7.35 5.76e-09  0.00399 6.93e-03
-#> f_parent_to_m1  0.51448   0.022880   22.49 4.37e-23  0.46808 5.61e-01
initials <- c("f_parent_to_m1" = 0.5)
+#> 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.ff.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
initials <- c("f_parent_to_m1" = 0.5)
 transformed <- transform_odeparms(initials, SFO_SFO.ff)
 backtransform_odeparms(transformed, SFO_SFO.ff)
#> f_parent_to_m1 
 #>            0.5 

@@ -258,15 +261,16 @@ The transformation of sets of formation fractions is fragile, as it supposes
   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)
-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
fit.ff.2.s <- summary(fit.ff.2)
 print(fit.ff.2.s$par, 3)
#>              Estimate Std. Error Lower Upper
-#> parent_0        84.79     2.9651 78.78 90.80
-#> log_k_parent    -2.76     0.0809 -2.92 -2.59
-#> log_k_m1        -4.21     0.1115 -4.44 -3.99
print(fit.ff.2.s$bpar, 3)
#>          Estimate se_notrans t value   Pr(>t)   Lower   Upper
-#> parent_0  84.7916    2.96505   28.60 3.94e-27 78.7838 90.7994
-#> k_parent   0.0635    0.00514   12.36 5.24e-15  0.0539  0.0748
-#> k_m1       0.0148    0.00165    8.97 4.11e-11  0.0118  0.0185

+#> parent_0        84.79      3.012 78.67 90.91
+#> log_k_parent    -2.76      0.082 -2.92 -2.59
+#> log_k_m1        -4.21      0.123 -4.46 -3.96
+#> sigma            8.22      0.943  6.31 10.14
print(fit.ff.2.s$bpar, 3)
#>          Estimate se_notrans t value   Pr(>t)   Lower  Upper
+#> parent_0  84.7916    3.01203   28.15 1.92e-25 78.6704 90.913
+#> k_parent   0.0635    0.00521   12.19 2.91e-14  0.0538  0.075
+#> k_m1       0.0148    0.00182    8.13 8.81e-10  0.0115  0.019
+#> sigma      8.2229    0.94323    8.72 1.73e-10  6.3060 10.140