From 8b7edd4eaf0d196e674a085f744d1a69260a6c91 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 16 Nov 2023 06:02:05 +0100 Subject: Update pkgdown docs to bootstrap 5 with search --- docs/articles/web_only/dimethenamid_2018.html | 295 +++++++++++--------------- 1 file changed, 119 insertions(+), 176 deletions(-) (limited to 'docs/articles/web_only/dimethenamid_2018.html') diff --git a/docs/articles/web_only/dimethenamid_2018.html b/docs/articles/web_only/dimethenamid_2018.html index 1cffd561..4221ba07 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -4,145 +4,104 @@ - + + Example evaluations of the dimethenamid data from 2018 • mkin - - - + + + - - + - + + -

Example evaluations of the dimethenamid data -from 2018

+ +

@@ -225,12 +184,12 @@ least in some datasets the degradation slows down towards later time points, and that the scatter of the residuals error is smaller for smaller values (panel to the right):

-plot(mixed(f_parent_mkin_const["SFO", ]))

+plot(mixed(f_parent_mkin_const["SFO", ]))

Using biexponential decline (DFOP) results in a slightly more random scatter of the residuals:

-plot(mixed(f_parent_mkin_const["DFOP", ]))

+plot(mixed(f_parent_mkin_const["DFOP", ]))

The population curve (bold line) in the above plot results from taking the mean of the individual transformed parameters, i.e. of log k1 @@ -242,7 +201,7 @@ dominates the average. This is alleviated if only rate constants that pass the t-test for significant difference from zero (on the untransformed scale) are considered in the averaging:

-plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)

+plot(mixed(f_parent_mkin_const["DFOP", ]), test_log_parms = TRUE)

While this is visually much more satisfactory, such an average procedure could introduce a bias, as not all results from the individual @@ -254,7 +213,7 @@ degradation model and the error model (see below).

predicted residues is reduced by using the two-component error model:

-plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)

+plot(mixed(f_parent_mkin_tc["DFOP", ]), test_log_parms = TRUE)

However, note that in the case of using this error model, the fits to the Flaach and BBA 2.3 datasets appear to be ill-defined, indicated by @@ -344,7 +303,7 @@ effects does not improve the fits.

The selected model (DFOP with two-component error) fitted to the data assuming no correlations between random effects is shown below.

-plot(f_parent_nlme_dfop_tc)

+plot(f_parent_nlme_dfop_tc)

@@ -361,17 +320,8 @@ implemented in the saemix package, the convergence plots need to be manually checked for every fit. We define control settings that work well for all the parent data fits shown in this vignette.

-library(saemix)

Loading required package: npde

Package saemix, version 3.2
-  please direct bugs, questions and feedback to emmanuelle.comets@inserm.fr


-Attaching package: 'saemix'

The following objects are masked from 'package:npde':
-
-    kurtosis, skewness

-saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
+library(saemix)
+saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
 saemix_control_moreiter <- saemixControl(nbiter.saemix = c(1600, 300), nb.chains = 15,
     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)
@@ -379,7 +329,7 @@ Attaching package: 'saemix'


     print = FALSE, save = FALSE, save.graphs = FALSE, displayProgress = FALSE)

The convergence plot for the SFO model using constant variance is shown below.

+ f_parent_saemix_sfo_const <- mkin::saem(f_parent_mkin_const["SFO", ], quiet = TRUE,
   control = saemix_control, transformations = "saemix")
 plot(f_parent_saemix_sfo_const$so, plot.type = "convergence")
@@ -387,19 +337,19 @@ shown below.
 Obviously the selected number of iterations is sufficient to reach
 convergence. This can also be said for the SFO fit using the
 two-component error model.
-+ f_parent_saemix_sfo_tc <- mkin::saem(f_parent_mkin_tc["SFO", ], quiet = TRUE,
   control = saemix_control, transformations = "saemix")
 plot(f_parent_saemix_sfo_tc$so, plot.type = "convergence")
 
 When fitting the DFOP model with constant variance (see below),
 parameter convergence is not as unambiguous.
-+ f_parent_saemix_dfop_const <- mkin::saem(f_parent_mkin_const["DFOP", ], quiet = TRUE,
   control = saemix_control, transformations = "saemix")
 plot(f_parent_saemix_dfop_const$so, plot.type = "convergence")
 
-+ print(f_parent_saemix_dfop_const)
 Kinetic nonlinear mixed-effects model fit by SAEM
 Structural model:
@@ -435,14 +385,14 @@ this model.
 also observe that the estimated variance of k2 becomes very small, while
 being ill-defined, as illustrated by the excessive confidence interval
 of SD.k2.
-+ f_parent_saemix_dfop_tc <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemix_control, transformations = "saemix")
 f_parent_saemix_dfop_tc_moreiter <- mkin::saem(f_parent_mkin_tc["DFOP", ], quiet = TRUE,
   control = saemix_control_moreiter, transformations = "saemix")
 plot(f_parent_saemix_dfop_tc$so, plot.type = "convergence")
 
-+ print(f_parent_saemix_dfop_tc)
 Kinetic nonlinear mixed-effects model fit by SAEM
 Structural model:
@@ -484,7 +434,7 @@ message.
 The four combinations (SFO/const, SFO/tc, DFOP/const and DFOP/tc) and
 the version with increased iterations can be compared using the model
 comparison function of the saemix package:
-+ AIC_parent_saemix <- saemix::compare.saemix(
   f_parent_saemix_sfo_const$so,
   f_parent_saemix_sfo_tc$so,
@@ -492,7 +442,7 @@ comparison function of the saemix package:
   f_parent_saemix_dfop_tc$so,
   f_parent_saemix_dfop_tc_moreiter$so)
 Likelihoods calculated by importance sampling
-+ rownames(AIC_parent_saemix) <- c(
   "SFO const", "SFO tc", "DFOP const", "DFOP tc", "DFOP tc more iterations")
 print(AIC_parent_saemix)
@@ -506,7 +456,7 @@ DFOP tc more iterations 665.85 663.76
 algorithms implemented in saemix, the likelihood from Gaussian
 quadrature is added to the best fit, and the AIC values obtained from
 the three methods are compared.
-+ f_parent_saemix_dfop_tc$so <-
   saemix::llgq.saemix(f_parent_saemix_dfop_tc$so)
 AIC_parent_saemix_methods <- c(
@@ -530,7 +480,7 @@ iterations makes a lot of difference. When using the LAPACK version
 coming with Debian Bullseye, the AIC based on Gaussian quadrature is
 almost the same as the one obtained with the other methods, also when
 using defaults for the fit.
-+ f_parent_saemix_dfop_tc_defaults <- mkin::saem(f_parent_mkin_tc["DFOP", ])
 f_parent_saemix_dfop_tc_defaults$so <-
   saemix::llgq.saemix(f_parent_saemix_dfop_tc_defaults$so)
@@ -550,7 +500,7 @@ using defaults for the fit.
 The following table gives the AIC values obtained with both backend
 packages using the same control parameters (800 iterations burn-in, 300
 iterations second phase, 15 chains).
-+ AIC_all <- data.frame(
   check.names = FALSE,
   "Degradation model" = c("SFO", "SFO", "DFOP", "DFOP"),
@@ -561,7 +511,7 @@ iterations second phase, 15 chains).
   saemix_is = sapply(list(f_parent_saemix_sfo_const$so, f_parent_saemix_sfo_tc$so,
     f_parent_saemix_dfop_const$so, f_parent_saemix_dfop_tc$so), AIC, method = "is")
 )
-kable(AIC_all)
+kable(AIC_all)
 
@@ -624,48 +574,48 @@ satisfactory precision.

 Session Info
 
-+ sessionInfo()
-R version 4.3.1 (2023-06-16)
+R version 4.3.2 (2023-10-31)
 Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Ubuntu 22.04.3 LTS
+Running under: Debian GNU/Linux 12 (bookworm)
 
 Matrix products: default
-BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.10.0 
-LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
+BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.11.0 
+LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.11.0
 
 locale:
- [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
- [3] LC_TIME=C                  LC_COLLATE=en_US.UTF-8    
- [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
- [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
+ [1] LC_CTYPE=de_DE.UTF-8       LC_NUMERIC=C              
+ [3] LC_TIME=C                  LC_COLLATE=de_DE.UTF-8    
+ [5] LC_MONETARY=de_DE.UTF-8    LC_MESSAGES=de_DE.UTF-8   
+ [7] LC_PAPER=de_DE.UTF-8       LC_NAME=C                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Zurich
+time zone: Europe/Berlin
 tzcode source: system (glibc)
 
 attached base packages:
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] saemix_3.2   npde_3.3     nlme_3.1-163 mkin_1.2.6   knitr_1.44  
+[1] saemix_3.2   npde_3.3     nlme_3.1-163 mkin_1.2.6   knitr_1.42  
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.7        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
- [5] lattice_0.21-9    digest_0.6.33     magrittr_2.0.3    evaluate_0.22    
- [9] grid_4.3.1        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.7   
-[13] mclust_6.0.0      gridExtra_2.3     purrr_1.0.1       fansi_1.0.4      
-[17] scales_1.2.1      codetools_0.2-19  textshaping_0.3.6 jquerylib_0.1.4  
-[21] cli_3.6.1         rlang_1.1.1       munsell_0.5.0     cachem_1.0.8     
-[25] yaml_2.3.7        tools_4.3.1       parallel_4.3.1    memoise_2.0.1    
-[29] dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2     vctrs_0.6.3      
-[33] R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3   stringr_1.5.0    
-[37] fs_1.6.3          MASS_7.3-60       ragg_1.2.5        pkgconfig_2.0.3  
-[41] desc_1.4.2        pkgdown_2.0.7     bslib_0.5.1       pillar_1.9.0     
-[45] gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4 xfun_0.40        
-[49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.0  rstudioapi_0.15.0
-[53] htmltools_0.5.6.1 rmarkdown_2.23    compiler_4.3.1   
+ [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    stringi_1.7.12   
+ [5] lattice_0.21-8    digest_0.6.31     magrittr_2.0.3    evaluate_0.21    
+ [9] grid_4.3.2        fastmap_1.1.1     rprojroot_2.0.3   jsonlite_1.8.4   
+[13] DBI_1.1.3         mclust_6.0.0      gridExtra_2.3     purrr_1.0.1      
+[17] fansi_1.0.4       scales_1.2.1      codetools_0.2-19  textshaping_0.3.6
+[21] jquerylib_0.1.4   cli_3.6.1         rlang_1.1.1       munsell_0.5.0    
+[25] cachem_1.0.8      yaml_2.3.7        tools_4.3.2       parallel_4.3.2   
+[29] memoise_2.0.1     dplyr_1.1.2       colorspace_2.1-0  ggplot2_3.4.2    
+[33] vctrs_0.6.2       R6_2.5.1          zoo_1.8-12        lifecycle_1.0.3  
+[37] stringr_1.5.0     fs_1.6.2          MASS_7.3-60       ragg_1.2.5       
+[41] pkgconfig_2.0.3   desc_1.4.2        pkgdown_2.0.7     bslib_0.4.2      
+[45] pillar_1.9.0      gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4
+[49] highr_0.10        xfun_0.39         tibble_3.2.1      lmtest_0.9-40    
+[53] tidyselect_1.2.0  htmltools_0.5.5   rmarkdown_2.21    compiler_4.3.2   
 
 
 References
@@ -690,34 +640,27 @@ November 2017.” 
-
-        Contents
-    
-

-
+  On this page
+    
 

 
 
 
-      
+    
   
 Developed by Johannes Ranke.
 
 
-
+
   
 Site built with pkgdown 2.0.7.
 
 
-      
+    
 
   
 
-
   
 
   
-- 
cgit v1.2.1


 
 Degradation model

Example evaluations with (generalised) nonlinear least squares

Example evaluations with hierarchical models (nonlinear mixed-effects models)

Performance

Miscellaneous

Example evaluations of the dimethenamid data -from 2018

Example evaluations of the dimethenamid data from 2018

Johannes Ranke

Last change 1 July 2022, -built on 30 Oct 2023

Session Info

References @@ -690,34 +640,27 @@ November 2017.” - - Contents - -

References @@ -690,34 +640,27 @@ November 2017.” - -
Contents
-
-