@@ -170,12 +170,12 @@ 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):
The population curve (bold line) in the above plot results from
taking the mean of the individual transformed parameters, i.e. of log k1
@@ -187,7 +187,7 @@ 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:
While this is visually much more satisfactory, such an average
procedure could introduce a bias, as not all results from the individual
@@ -199,7 +199,7 @@ degradation model and the error model (see below).
predicted residues is reduced by using the two-component error
model:
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
@@ -289,7 +289,7 @@ 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.
@@ -306,17 +306,8 @@ 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.
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$so, plot.type ="convergence")
@@ -332,19 +323,19 @@ shown below.
Obviously the selected number of iterations is sufficient to reach
convergence. This can also be said for the SFO fit using the
two-component error model.
-
+
f_parent_saemix_sfo_tc<-mkin::saem(f_parent_mkin_tc["SFO", ], quiet =TRUE, control =saemix_control, transformations ="saemix")plot(f_parent_saemix_sfo_tc$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$so, plot.type ="convergence")
Kinetic nonlinear mixed-effects model fit by SAEM
Structural model:
@@ -380,14 +371,14 @@ this model.
also observe that the estimated variance of k2 becomes very small, while
being ill-defined, as illustrated by the excessive confidence interval
of SD.k2.
-
+
f_parent_saemix_dfop_tc<-mkin::saem(f_parent_mkin_tc["DFOP", ], quiet =TRUE, control =saemix_control, transformations ="saemix")f_parent_saemix_dfop_tc_moreiter<-mkin::saem(f_parent_mkin_tc["DFOP", ], quiet =TRUE, control =saemix_control_moreiter, transformations ="saemix")plot(f_parent_saemix_dfop_tc$so, plot.type ="convergence")
Kinetic nonlinear mixed-effects model fit by SAEM
Structural model:
@@ -429,7 +420,7 @@ 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,
@@ -437,7 +428,7 @@ comparison function of the saemix package:
f_parent_saemix_dfop_tc$so,f_parent_saemix_dfop_tc_moreiter$so)
@@ -451,7 +442,7 @@ 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(
@@ -475,7 +466,7 @@ 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)
@@ -495,7 +486,7 @@ 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).