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as obtained by mean_degparms(..., random = FALSE), instead
of the list obtained by mean_degparms(, ... random = TRUE) that
is passed by nlme.mmkin to nlme by default.
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- All degradation models are specified as ODE models. This appears to be
fast enough
- Error models are being translated to nlmixr as close to the mkin error
model as possible. When using the 'saem' backend, it appears not to be
possible to use the same error model for more than one observed variable
- No support yet for models with parallel formation of metabolites, where
the ilr transformation is used in mkin per default
- There is a bug in nlmixr which appears to be triggered if the data are
not balanced, see nlmixrdevelopment/nlmixr#530
- There is a print and a plot method, the summary method is not finished
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- fit_with_errors for saem()
- test_log_parms for mean_degparms() and saem()
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The errors in the example code were in the \dontrun sections, so they
were not caught by CRAN checks. In addition, the static help files
generated with pkgdown were cached, so I noticed the errors only
after completely regenerating the documentation for version 1.0.0.
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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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With a plot method. The class mixed.mmkin is currently only a virtual
class created to unify the plotting method.
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This avoids code duplication
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Also make the endpoints function work for saem objects.
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Do not give starting values for random effects in nlme.mmkin.
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Improve and update docs
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Update docs
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And add output for nlme fit translating the mkinfit error model "obs"
into nlme::varIdent().
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The experimental nlme version in my drat repository contains the
variance function structure varConstProp which makes it possible to use
the two-component error model in generalized nonlinear models using
nlme::gnls() and in mixed effects models using nlme::nlme().
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The method is no longer necessary, now that Bug 17761 is fixed upstream
https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761
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- mean_degparms() now optionally returns starting values for fixed and
random effects, which makes it possible to obtain acceptable fits
also in more difficult cases (with more parameters)
- Fix the anova method, as it is currently not enough to inherit from
lme: https://bugs.r-project.org/bugzilla/show_bug.cgi?id=17761
- Show fit information, and per default also errmin information
in plot.nlme.mmkin()
- Examples for nlme.mmkin: Decrease tolerance and increase the number of
iterations in the PNLS step in order to be able to fit FOMC-SFO and
DFOP-SFO
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