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path: root/tests/testthat/test_error_models.R
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2020-05-29Warn if standardized residuals are unlikely normalJohannes Ranke1-8/+9
This revealed a bug in the data returned in mkinfit$data in the case of the d_3 algorithm, which also affected the residual plot - the data from the direct fitting was not returned even if this was the better method.
2020-04-22Remove GPL header from test filesJohannes Ranke1-18/+0
2019-11-01Update link, increase tolerance of a test for Travisv0.9.49.7Johannes Ranke1-1/+1
2019-11-01Fix bug in yesterdays release, add methods for BICJohannes Ranke1-1/+7
2019-10-22Improved visual testingJohannes Ranke1-6/+0
2019-10-21Skip an offensive test on TravisJohannes Ranke1-0/+1
2019-10-21Improve some plotting routines, more testsJohannes Ranke1-0/+3
Static documentation rebuilt by pkgdown
2019-10-21Improve tests, remove geometric_meanJohannes Ranke1-31/+0
2019-10-21Refactor mkinfit, infrastructure workJohannes Ranke1-128/+10
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.
2019-07-04Address failures of CRAN checks, improve NEWSv0.9.49.5Johannes Ranke1-5/+8
Static documentation rebuilt by pkgdown
2019-07-03Skip a test on CRAN/winbuilderJohannes Ranke1-0/+1
The test uses multiple cores in order to complete within a reasonable time
2019-06-05Adapt tests to new algorithms and outputJohannes Ranke1-1/+1
One of the tests exceeded the number of iterations when using the d_3 error model algorithm, so only use "direct" in this case.
2019-06-04Fix a bug introduced in the last commitJohannes Ranke1-1/+1
2019-06-04Additional algorithm "d_c", more tests, docsJohannes Ranke1-75/+40
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.
2019-06-04Algorithms direct, two-, three-, fourstep, IRLSJohannes Ranke1-5/+45
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
2019-06-03Status von Samstag morgen - untestedJohannes Ranke1-0/+42
2019-05-02Prepare for CRAN releaseJohannes Ranke1-0/+1
- Skip long running tests on CRAN as well to avoid timeout on winbuilder - Don't install benchmark results in the package, they are only needed in the git repository - Don't run example in man/add_err.Rd as it takes > 10 s on winbuilder - Rebuild docs
2019-05-02Improve testsJohannes Ranke1-6/+13
- 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
2019-04-10Adapt tests, vignettes and examplesJohannes Ranke1-12/+5
- Write the NEWS - Static documentation rebuilt by pkgdown - Adapt mkinerrmin - Fix (hopefully all) remaining problems in mkinfit
2019-04-04Direct error model fitting worksJohannes Ranke1-0/+178
- 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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