Age | Commit message (Collapse) | Author | Files | Lines |
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As we set the tolerance for ode() appropriately
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This increases the performance in the complete test suite
by about 20 secs from 120 to around 100 secs.
I tried improving merge speed by using data.table on another
branch, but this did not give a noticeable performance gain.
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Still in preparation for analytical solutions of coupled models
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Preparing for symbolic solutions for more than one compound
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The likelihood ratio test method is lrtest, in addition,
methods for update and residuals were added.
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The confint method can do profile likelihood based confidence intervals!
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in the hope that this makes plotting cross-platform also for this error
model
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mkinfit objects now include an ll() function to calculate the
log-likelihood. Part of the code was refactored, hopefully making it
easier to read and maintain. IRLS is currently the default algorithm for
the error model "obs", for no particular reason. This may be subject
to change when I get around to investigate.
Slow tests are now in a separate subdirectory and will probably
only be run by my own Makefile target.
Formatting of test logs is improved.
Roundtripping error model parameters works with a precision of 10% when
we use lots of replicates in the synthetic data (see slow tests). This
is not new in this commit, but as I think it is reasonable this
closes #7.
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Address winbuilder check problems, update check log, update of static docs
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generated with mkin < 0.9.49.5
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The new algorithm tries direct optimization of the likelihood, as well
as a three step procedure. In this way, we consistently get the
model with the highest likelihood for SFO, DFOP and HS for all 12
new test datasets.
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All of them are working now and allow for comparison
Based on SFO, DFOP and HS fits to twelve test datasets, only
the combination of direct and threestep is needed to find
the lowest AIC
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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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