Age | Commit message (Collapse) | Author | Files | Lines |
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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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Also bump version to 1.0.3.
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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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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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- 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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This makes fitting with saem within parallel::mclapply much faster
and, surprisingly, much less hungry for RAM.
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Address release critical check and test issues
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I threw out mclapply as it did not play well with the linear algebra
routines used in the saemix code. Most of the change is actually
indentation in the code creating the model function. But there
is an important fix in mkinpredict which I had broken.
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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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Depends on inline >= 0.16.2 (including the bug fixes from
eddelbuettel/inline#18), which provides 'moveDLL' to store the DLL for a
compiled function in a safe place in case the argument 'dll_dir' is
specified in the call to 'mkinmod'.
Huge thanks to Dirk @eddelbuettel for his review and support
for the work on the inline package.
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With automatic reloading in mkinfit and mkinpredict in case the
DLL is not loaded and the original DLL path has been cleaned up.
Depends on jranke/inline@974bdea04fcedfafaab231e6f359c88270b56cb9
See inline#13
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By depending on parallel instead of importing it, functions to set up
and stop a cluster are always available when mkin is loaded.
The use of multicore processing in mmkin on Windows is now documented in
the help file, which brings mkin closer to a version 1.0 #9.
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- D24_2014 dataset on aerobic soil degradation of 2,4-D from the EU
assessment as mkindsg object with metadata
- f_time_norm_focus() to do time-step normalisation using the FOCUS
method
- focus_soil_moisture data with default moisture contents at pF1,
pF 2 and pF 2.5 for USDA soil types from FOCUS GW guidance
- Dataset generation scripts in inst/dataset_generation
- Depend on R >= 2.15.1 in order to facilitate the use of
utils::globalVariables()
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which is now the default
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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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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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Currently SFO-SFO and DFOP-SFO. Speed increase factor about 60
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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, exclude the saemix function when loading saemix in the example
code, to prevent overriding our generic
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This commit also defined saemix.mmkin for mmkin row objects.
This works fine, but if we set the class of the returned object
to c("saemix.mmkin", "saemix"), it is not an S4 class any more
which make it impossible to use saemix functions on it.
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Also, use logit transformation for g and for solitary formation
fractions, addressing #10.
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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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