From b7901aac76df753ec1213cb02bebea055965ee87 Mon Sep 17 00:00:00 2001 From: Ranke Johannes Date: Mon, 30 Oct 2023 17:09:21 +0100 Subject: Update static docs --- docs/articles/web_only/dimethenamid_2018.html | 154 ++++++++++++++------------ 1 file changed, 83 insertions(+), 71 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 a89631a2..1cffd561 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -33,7 +33,7 @@ mkin - 1.2.4 + 1.2.6 @@ -73,6 +73,9 @@
  • Testing hierarchical pathway kinetics with residue data on dimethenamid and dimethenamid-P
    • +
  • + Testing covariate modelling in hierarchical parent degradation kinetics with residue data on mesotrione +
  • Testing hierarchical pathway kinetics with residue data on cyantraniliprole
    • @@ -135,7 +138,7 @@ from 2018 Ranke

    Last change 1 July 2022, -built on 19 May 2023

    +built on 30 Oct 2023 Source: vignettes/web_only/dimethenamid_2018.rmd @@ -222,12 +225,12 @@ 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 @@ -239,7 +242,7 @@ 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 @@ -251,7 +254,7 @@ 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 @@ -263,7 +266,7 @@ Status of individual fits: dataset model Calke Borstel Flaach BBA 2.2 BBA 2.3 Elliot - DFOP OK OK C OK C OK + DFOP OK OK OK OK C OK C: Optimisation did not converge: iteration limit reached without convergence (10) @@ -319,7 +322,7 @@ indicates that this difference is significant as the p-value is below 

                            Model df    AIC    BIC  logLik   Test L.Ratio p-value
 f_parent_nlme_sfo_const     1  5 796.60 811.82 -393.30                       
 f_parent_nlme_sfo_tc        2  6 798.60 816.86 -393.30 1 vs 2    0.00   0.998
-f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
    +f_parent_nlme_dfop_tc 3 10 671.91 702.34 -325.95 2 vs 3 134.69 <.0001

    In addition to these fits, attempts were also made to include correlations between random effects by using the log Cholesky parameterisation of the matrix specifying them. The code used for these @@ -341,7 +344,7 @@ 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)

    @@ -358,8 +361,17 @@ 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)
-saemix_control <- saemixControl(nbiter.saemix = c(800, 300), nb.chains = 15,
+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,
     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)
@@ -367,7 +379,7 @@ well for all the parent data fits shown in this vignette.
    
     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")
    
@@ -375,19 +387,19 @@ 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:
@@ -408,11 +420,11 @@ DMTA_0    97.99583 96.50079 99.4909
 k1         0.06377  0.03432  0.0932
 k2         0.00848  0.00444  0.0125
 g          0.95701  0.91313  1.0009
-a.1        1.82141  1.65122  1.9916
-SD.DMTA_0  1.64787  0.45772  2.8380
+a.1        1.82141  1.60516  2.0377
+SD.DMTA_0  1.64787  0.45729  2.8384
 SD.k1      0.57439  0.24731  0.9015
-SD.k2      0.03296 -2.50195  2.5679
-SD.g       1.10266  0.32369  1.8816
    
+SD.k2      0.03296 -2.50524  2.5712
+SD.g       1.10266  0.32354  1.8818
    
 
    While the other parameters converge to credible values, the variance
 of k2 (omega2.k2) converges to a very small value. The
 printout of the saem.mmkin model shows that the estimated
@@ -423,14 +435,14 @@ 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:
@@ -446,17 +458,17 @@ Likelihood computed by importance sampling
   666 664   -323
 
 Fitted parameters:
-          estimate    lower    upper
-DMTA_0    98.27617  96.3088 100.2436
-k1         0.06437   0.0337   0.0950
-k2         0.00880   0.0063   0.0113
-g          0.95249   0.9100   0.9949
-a.1        1.06161   0.8625   1.2607
-b.1        0.02967   0.0226   0.0367
-SD.DMTA_0  2.06075   0.4187   3.7028
-SD.k1      0.59357   0.2561   0.9310
-SD.k2      0.00292 -10.2960  10.3019
-SD.g       1.05725   0.3808   1.7337
    
+          estimate     lower    upper
+DMTA_0    98.24165  96.29190 100.1914
+k1         0.06421   0.03352   0.0949
+k2         0.00866   0.00617   0.0111
+g          0.95340   0.91218   0.9946
+a.1        1.06463   0.86503   1.2642
+b.1        0.02964   0.02259   0.0367
+SD.DMTA_0  2.03611   0.40416   3.6681
+SD.k1      0.59534   0.25692   0.9338
+SD.k2      0.00042 -73.01372  73.0146
+SD.g       1.04234   0.37189   1.7128
    
 
    Doubling the number of iterations in the first phase of the algorithm
 leads to a slightly lower likelihood, and therefore to slightly higher
 AIC and BIC values. With even more iterations, the algorithm stops with
@@ -472,7 +484,7 @@ 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,
@@ -480,7 +492,7 @@ 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)
    
@@ -488,13 +500,13 @@ comparison function of the saemix package:
    
 SFO const               796.38 795.34
 SFO tc                  798.38 797.13
 DFOP const              705.75 703.88
-DFOP tc                 665.65 663.57
-DFOP tc more iterations 665.88 663.80
    
+DFOP tc                 665.67 663.59
+DFOP tc more iterations 665.85 663.76
    
 
    In order to check the influence of the likelihood calculation
 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(
@@ -504,7 +516,7 @@ the three methods are compared.
    
 )
 print(AIC_parent_saemix_methods)
    
 
        is     gq    lin 
-665.65 665.68 665.11 
    
+665.67 665.74 665.13 
    
 
    The AIC values based on importance sampling and Gaussian quadrature
 are very similar. Using linearisation is known to be less accurate, but
 still gives a similar value.
    
@@ -518,7 +530,7 @@ 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)
@@ -529,7 +541,7 @@ using defaults for the fit.
    
 )
 print(AIC_parent_saemix_methods_defaults)
    
 
        is     gq    lin 
-669.77 669.36 670.95 
    
+670.09 669.37 671.29 
    
 
    
 
    
 
    
@@ -538,7 +550,7 @@ 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"),
@@ -549,7 +561,7 @@ 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)
    
 
@@ -577,15 +589,15 @@ iterations second phase, 15 chains).
    
-
+
-
-
+
+
    
 
    
 Degradation model
 DFOP
 const
 NA709.26704.95
 705.75
 
    
 
    
 DFOP
 tc
 671.91665.11665.65665.13665.67
 
    
 
 
    
@@ -612,48 +624,48 @@ satisfactory precision.
    
 
    
 
    Session Info
 
    
-
    +
-
    R version 4.3.0 Patched (2023-05-18 r84448)
+
    R version 4.3.1 (2023-06-16)
 Platform: x86_64-pc-linux-gnu (64-bit)
-Running under: Debian GNU/Linux 12 (bookworm)
+Running under: Ubuntu 22.04.3 LTS
 
 Matrix products: default
-BLAS:   /home/jranke/svn/R/r-patched/build/lib/libRblas.so 
-LAPACK: /usr/lib/x86_64-linux-gnu/openblas-serial/liblapack.so.3;  LAPACK version 3.11.0
+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
 
 locale:
- [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                 
+ [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                 
  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
-[11] LC_MEASUREMENT=de_DE.UTF-8 LC_IDENTIFICATION=C       
+[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
 
-time zone: Europe/Berlin
+time zone: Europe/Zurich
 tzcode source: system (glibc)
 
 attached base packages:
 [1] stats     graphics  grDevices utils     datasets  methods   base     
 
 other attached packages:
-[1] nlme_3.1-162 mkin_1.2.4   knitr_1.42  
+[1] saemix_3.2   npde_3.3     nlme_3.1-163 mkin_1.2.6   knitr_1.44  
 
 loaded via a namespace (and not attached):
- [1] sass_0.4.6        utf8_1.2.3        generics_0.1.3    saemix_3.2       
- [5] stringi_1.7.12    lattice_0.21-8    digest_0.6.31     magrittr_2.0.3   
- [9] evaluate_0.21     grid_4.3.0        fastmap_1.1.1     rprojroot_2.0.3  
-[13] jsonlite_1.8.4    DBI_1.1.3         mclust_6.0.0      gridExtra_2.3    
-[17] purrr_1.0.1       fansi_1.0.4       scales_1.2.1      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_4.3.0       parallel_4.3.0   
-[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          ragg_1.2.5        pkgconfig_2.0.3  
-[41] desc_1.4.2        pkgdown_2.0.7     bslib_0.4.2       pillar_1.9.0     
-[45] gtable_0.3.3      glue_1.6.2        systemfonts_1.0.4 xfun_0.39        
-[49] tibble_3.2.1      lmtest_0.9-40     tidyselect_1.2.0  npde_3.3         
-[53] htmltools_0.5.5   rmarkdown_2.21    compiler_4.3.0   
    
+ [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   
    
 
    
 
    
 
    References
-- 
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