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If the likelihood computed by importance sampling, these methods failed.
Now they report "Not available" or NA, respectively.
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I think it is always preferable to be able to investigate the
saemix object returned.
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That parhist name was not the brightest idea, as it does
not show histograms.
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The bug was introduced by the changes in summary.saem.mmkin.R and
surfaced in the tests when using saemix transformations.
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Update docs
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This is achieved by introducing the argument 'no_random_effect' to the
saem function.
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We now directly import from rlang and vctrs, which were indirect
dependencies anyways. purrr::map_dfr is deprecated in the upcoming purrr
1.0, and depends on dplyr (since when?) which is only suggested by
purrr. This would lead new installations of mkin to fail if dplyr is not
installed as well.
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- 'R/mhmkin.R': New method for performing multiple hierarchical mkin fits in one function call, optionally in parallel.
- 'R/saem.R': 'logLik' and 'update' methods for 'saem.mmkin' objects.
- 'R/illparms.R': Add methods for 'saem.mmkin' and 'mhmkin' objects.
tests: Use 2 cores on travis, should work according to docs
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Also, support FOMC with saemix transformations
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Also increase the performance a bit (from about 210 s to about 140 s in
the case of DFOP with four chains and 300, 100 iterations).
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I am postponing my attempts to get the nlmixr interface to CRAN, given
some problems with nlmixr using R-devel under Windows, see
https://github.com/nlmixrdevelopment/nlmixr/issues/596
and
https://github.com/r-hub/rhub/issues/512,
which is fixed by the removal of nlmixr from the testsuite.
For the tests to be more platform independent, the biphasic mixed
effects models test dataset was defined in a way that fitting
should be more robust (less ill-defined).
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By actually passing dot arguments (that can include fixed.estim) to
saemixModel, as advertised in the docs.
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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 after the upgrade from buster to bullseye of my local system, some
test results for saemix have changed.
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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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This makes fitting with saem within parallel::mclapply much faster
and, surprisingly, much less hungry for RAM.
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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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Namely for SFO, DFOP, SFO-SFO and DFOP-SFO
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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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