From 5a21dc4016b0208264e22e6a7e36af62397c13b6 Mon Sep 17 00:00:00 2001 From: Johannes Ranke Date: Thu, 30 Jun 2022 10:29:05 +0200 Subject: Remove stray sentence from vignette, update docs --- docs/articles/web_only/dimethenamid_2018.html | 14 +++++++------- 1 file changed, 7 insertions(+), 7 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 09aa5150..32653b5d 100644 --- a/docs/articles/web_only/dimethenamid_2018.html +++ b/docs/articles/web_only/dimethenamid_2018.html @@ -105,7 +105,7 @@

Example evaluations of the dimethenamid data from 2018

Johannes Ranke

-

Last change 7 March 2022, built on 16 Mar 2022

+

Last change 7 March 2022, built on 30 Jun 2022

Source: vignettes/web_only/dimethenamid_2018.rmd @@ -155,20 +155,20 @@ 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)

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 the fact that they did not converge:

@@ -222,7 +222,7 @@ f_parent_nlme_dfop_tc       3 10 671.91 702.34 -325.96 2 vs 3  134.69  <.0001
 
While the SFO variants converge fast, the additional parameters introduced by this lead to convergence warnings for the DFOP model. The model comparison clearly show that adding correlations between random 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)

@@ -369,7 +369,7 @@ DFOP tc more iterations 665.88 663.80

Comparison

-

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). Note that

+

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,
-- 
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