Age | Commit message (Collapse) | Author | Files | Lines |
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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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by plotting squared residuals against predicted values, and
showing the variance function used in the fitted error model.
Rebuild docs
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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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Also reduce the digits in the representative half-live given by nafta()
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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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In order to be able to test cross-platform
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Static documentation rebuilt by pkgdown
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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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which was accidentally overwritten by pkgdown -> roxygen
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Static documentation rebuilt by pkgdown
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Static documentation rebuilt by pkgdown
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Further relax two tests to pass build on Travis
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Check in work from the beginning of the week
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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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Static documentation except articles rebuilt by pkgdown
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... when using plot_sep() or plot.mkinfit(..., sep_obs = TRUE)
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... in the case of separate plots for each observed variable as obtained
with plot_sep() or plot.mkinfit(..., sep_obs = TRUE)
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