From 137612045c23198f10d6e8612c32e266c2a6c00e Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 29 Jul 2021 12:17:56 +0200 Subject: Go back to 1.0.x version, update docs --- docs/dev/articles/web_only/dimethenamid_2018.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) (limited to 'docs/dev/articles/web_only/dimethenamid_2018.html') diff --git a/docs/dev/articles/web_only/dimethenamid_2018.html b/docs/dev/articles/web_only/dimethenamid_2018.html index 7648f75a..34d882a4 100644 --- a/docs/dev/articles/web_only/dimethenamid_2018.html +++ b/docs/dev/articles/web_only/dimethenamid_2018.html @@ -32,7 +32,7 @@ mkin - 1.1.0 + 1.0.5 @@ -101,7 +101,7 @@

Example evaluations of the dimethenamid data from 2018

Johannes Ranke

-

Last change 27 July 2021, built on 27 Jul 2021

+

Last change 27 July 2021, built on 29 Jul 2021

Source: vignettes/web_only/dimethenamid_2018.rmd @@ -154,20 +154,20 @@ error_model = "tc", quiet = TRUE)

The plot of the individual SFO fits shown below suggests that at 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 and log k2, as well as of the logit of the g parameter of the DFOP model). Here, this procedure does not result in parameters that represent the degradation well, because in some datasets the fitted value for k2 is extremely close to zero, leading to a log k2 value that 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 fits enter the population curve with the same weight. This is where nonlinear mixed-effects models can help out by treating all datasets with equally by fitting a parameter distribution model together with the degradation model and the error model (see below).

The remaining trend of the residuals to be higher for higher 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)

@@ -205,7 +205,7 @@ f_parent_nlme_sfo_tc 2 6 820.61 839.06 -404.31 1 vs 2 0.014 0.9049 f_parent_nlme_dfop_tc 3 10 687.84 718.59 -333.92 2 vs 3 140.771 <.0001

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)

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