cjvanlissa / metaforest

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#' are thus included in the same cross-validation sample. Then, two random
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#' forests are grown on these cross-validation samples, and for each random
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#' forest, the other sample is used to calculate prediction error and variable
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#' importance (see \href{http://doi.org/10.1007/s11634-016-0276-4}{Janitza,
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#' importance (see \href{https://doi.org/10.1007/s11634-016-0276-4}{Janitza,
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#' Celik, & Boulesteix, 2016}).
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#' @import stats
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#' @import ranger

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Files Coverage
MF.R 93.33%
MF_cluster.R 77.08%
MetaForest.R 90.91%
ModelInfo_mf.R 98.51%
ModelInfo_rma.R 0.00%
PartialDependence.R 47.01%
SimulateSMD.R 0.00%
VarImpPlot.R 84.62%
WeightedScatter.R 0.00%
coef_test.R 0.00%
extract_proximity.MetaForest.R 0.00%
helpers.R 11.11%
marginal_predictions.R 46.43%
merge_forests.R 96.88%
plot.MetaForest.R 30.43%
predict.MetaForest.R 71.43%
preselect.R 66.67%
print.MetaForest.R 0.00%
print.summary.MetaForest.R 0.00%
summary.MetaForest.R 90.91%
Folder Totals (20 files) 51.43%
Project Totals (20 files) 51.43%
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