Age | Commit message (Collapse) | Author | Files | Lines |
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Static documentation rebuilt by pkgdown
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- Fix the respective error in the code
- Static documentation rebuilt by pkgdown
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Now we have a three stage fitting process for
nonconstant error models:
- Unweighted least squares
- Only optimize the error model
- Optimize both
Static documentation rebuilt by pkgdown
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- Also make it possible to specify initial values for error model
parameters.
- Run tests
- Rebuild docs
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Remove skipped tests as I do not intend to reactivate them
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- Write the NEWS
- Static documentation rebuilt by pkgdown
- Adapt mkinerrmin
- Fix (hopefully all) remaining problems in mkinfit
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Zero observations at time zero made fitting the two-component error
model fail. A concentration of exactly zero does not make sense anyways,
as we generally have a limit of detection
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If not quiet = TRUE
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- No IRLS required
- Removed optimization algorithms other than Port
- Removed the dependency on FME
- Fitting the error model 'obs' is much faster for the FOCUS_2006_D
dataset and the FOMC_SFO model (1 second versus 3.4 seconds)
- Vignettes build slower. Compiled models needs 3 minutes instead of 1.5
- For other vignettes, the trend is less clear. Some fits are faster,
even for error_model = "const". FOCUS_Z is faster (34.9 s versus
44.1 s)
- Standard errors and confidence intervals are slightly smaller
- Removed code for plotting during the fit, as I hardly ever used it
- Merged the two cost functions (using transformed and untransformed
parameters) into one log-likelihood function
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Also:
- Change rounding in print.nafta
- Add dots argument to nafta()
- Use cores=1 in examples
- Restrict N in IORE model to values > 0
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- Make mmkin compatible
- Return DT50 values corresponding to k0 and kmax
- Turn incompatible parameter names in parms.ini from an error to a
warning, in order to make it possible to use this argument in calls to
mmkin
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Further relax two tests to pass build on Travis
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with respect to accuracy and robustness.
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as the absolute value is a biased estimator for the standard deviation
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Clean up the code a bit
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Static documentation rebuilt by pkgdown
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Also improve the irls fitting of the error model and add a test
for FOCUS_2006_C where the second component of the error model is zero
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Rename 'sigma_rl' to 'sigma_twocomp' as the Rocke and Lorenzato model assumes lognormal distribution for large y.
Rebuild static documentation.
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that prevented the convergence message to be returned in the case of non-convergence.
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For fits generated with previous version, the version numbers used for
fitting were not stored in the fit object. Therefore, the versions
used for fitting can only be shown for fits generated with mkin
containing this commit.
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Static documentation except articles rebuilt by pkgdown
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Also ignore test.R in the top level directory, as it is not meant to
be public
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mkinfit: Do not error out in cases where the fit converges, but the
Jacobian for the untransformed model cost can not be estimated. Give a
warning instead and return NA for the t-test results.
summary.mkinfit: Give a warning message when the covariance matrix can
not be obtained.
A test has been added to containing such an edge case to check that
the warnings are correctly issued and the fit does not terminate.
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- Now it is easier to do decline kinetics from the maximum occurrence
- Static documentation rebuilt by staticdocs::build_site()
- Fixed an URL in the README, thanks to the win-builder check with
R-devel
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As it is unclear if and when ccSolve will be published on CRAN,
the generation, compilation and use of the C version of the
system of differential equations was developed for mkin, inspired and
guided by the code from the ccSolve package. Many thanks again to
Karline Soetaert for all of her work on this and other R packages.
Now all model types, including the Hockey-Stick model for the parent
compund and the IORE model for parent and/or metabolites can be compiled.
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Tests all pass (Linux)
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This was broken for using method.modFit = "SANN"
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Thanks to Michael Brauer for the hint
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See NEWS.md: When the parent was not the (only) variable with the
highest value out of all variables in the observed data, it could
happen that the initial value (state.ini) was not initialised.
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