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This revealed that transforming rates is necessary for fitting
the analytical solution of the SFO-SFO model to the FOCUS D dataset.
Benchmarks show that fitting coupled models with deSolve got a bit
slower through the latest changes
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This increases performance up to a factor of five!
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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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This was done to address the test failure on
r-devel-linux-x86_64-debian-gcc on CRAN
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No idea why I had to do more assignments all of a sudden in test_nlme.R
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- Added a section with platform specific notes on getting compiled
models to work to the compiled models article
- Don't return empty SFORB parameter list from endpoints() if there is no
SFORB model
- Avoid warnings when using standardized = TRUE in plot.mmkin()
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- Switch an example dataset in the test setup to a dataset with
replicates, adapt tests
- Skip the test for lrtest with an update specification as it does not
only fail when pkgdown generates static help pages, but also in testthat
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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 cutoff now matches what is given by Venzon and Moolgavkar (1988).
Also, confidence intervals closely match intervals obtained with
stats4::confint in the test case where an stats4::mle object
is created from the likelihood function in one test case.
Static documentation rebuilt by pkgdown
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Static documentation rebuilt by pkgdown
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in the hope that this makes plotting cross-platform also for this error
model
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Especially on winbuilder (i386 and amd64)
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Because on winbuilder obviously gcc was not found, so the Eigenvalue
based solution method was used, leading to a test failure when
comparing the summary, as the solution method is listed
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Static documentation rebuilt by pkgdown
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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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generated with mkin < 0.9.49.5
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One of the tests exceeded the number of iterations when using the
d_3 error model algorithm, so only use "direct" in this case.
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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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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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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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- Improve control of the number of cores
- Reduce the precision of the correlation matrix in the test summary
output, as the exact results are platform dependent
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- Also make it possible to specify initial values for error model
parameters.
- Run tests
- Rebuild docs
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