Age | Commit message (Collapse) | Author | Files | Lines |
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plot.mixed.mmkin did not reset graphical parameters at all. The other
plotting functions did not use on.exit, so this change should make the
use of the plotting functions safer.
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Also after the upgrade from buster to bullseye of my local system, some
test results for saemix have changed.
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Platform dependence also revealed after upgrade to bullseye
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Remove tests relying on non-convergence of the FOMC fit to the FOCUS A
dataset, as this is platform dependent. After the upgrade, the fit
converges on this system as well, although neither ATLAS is used, nor R
was configured disabling long doubles (these were the conditions under
which the tests failed on CRAN).
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- Improve authorship and copyright information
- Prepare pkgdown config
- Remove dependence on saemix as we need the development version which
is not ready for CRAN
- Temporarily remove saemix interface to check code coverage of the rest
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See NEWS.md. Closes #12
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Address release critical check and test issues
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But could not find one.
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But skip the test as it takes too long to always run
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I can test on R-devel locally for preparing releases
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The default is pdDiag again, as we often have a small number of datasets
in degradation kinetics.
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As can be seen in the miniscule change seen on R-devel in the reference
plot updated with this commit
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Also make the endpoints function work for saem objects.
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The reasons for this decision were
- Creating an saemix generic in the saemix package caused problems with
roxygen, because functions like saemix.plot.xy were documented in
their help files as S3 methods, although explicitly exported with
@export
- Creating an saemix generic in this package is possible, but would
make it necessary to load samix with exclude = "saemix" in order to
avoid overwriting the generic when loading saemix.
- The return object of such an saemix generic in this package cannot
be an S3 class with class attribute c("saemix.mmkin", "SaemixObject")
similar to nlme.mmkin, as saemix returns an S4 class.
- Extending the S4 class SaemixObject using simple inheritance to
a class SaemixMmkinObject with additional slots did not work
as expected. When the initialize method was left untouched, it
prevented creation of an SaemixMmkinObject even if it was based
on an initialised SaemixObject, as the initialize method seems
to always be called by new(). This could potentially be circumvented
by a coerce method. If an alternative initialize method was
used, an SaemixMmkinObject could be created. However, the methods
written for SaemixObjects only worked in some instances, either
because they checked for the class, and not for class inheritance
(like compare.saemix), or because the initialize method was called
for some reason. Therefore, the idea of creating a derived S4 class
was abandoned.
- A side effect of this decision is that the introduction of the saem
generic opens the possibility to use the same generic also for other
backends like nlmixr with the SAEM algorithm.
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Also, use logit transformation for g and for solitary formation
fractions, addressing #10.
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Improve and update docs
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- Reduce significant digits for the objective function output in
mkinfit(..., quiet = FALSE) as R and R-devel gave different output on my
system
- Add makefile target 'devtest' for testing with R-devel, in order
to fix problems showing up with R-devel on Travis
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following the arguments of Xavier Robin https://github.com/r-lib/vdiffr/issues/86#issuecomment-636447231
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Vignette FOCUS_L failed as I had introduced a bug in the handling of
warnings.
Current vdiffr only runs visual tests if R < 4.1.0, skipping r-devel for now,
see https://github.com/r-lib/vdiffr/commit/630a29d013361fd63fea242f531e2db6aef37919
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This revealed a bug in the data returned in mkinfit$data in the case
of the d_3 algorithm, which also affected the residual plot - the
data from the direct fitting was not returned even if this was
the better method.
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Also, use more intelligent starting values for the variance of the
random effects for saemix. While this does not appear to speed up
the convergence, it shows where this variance is greatly reduced
by using mixed-effects models as opposed to the separate independent
fits.
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