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Consider a possibly nonlinear nonparametric regression with p regressors. We provide evaluations by 13 methods to rank regressors by their practical significance or importance using various methods, including machine learning tools. Comprehensive methods are as follows. m6=Generalized partial correlation coefficient or GPCC by Vinod (2021)<doi:10.1007/s10614-021-10190-x> and Vinod (2022)<https://www.mdpi.com/1911-8074/15/1/32>. m7= a generalization of psychologists' effect size incorporating nonlinearity and many variables. m8= local linear partial (dy/dxi) using the 'np' package for kernel regressions. m9= partial (dy/dxi) using the 'NNS' package. m10= importance measure using the 'NNS' boost function. m11= Shapley Value measure of importance (cooperative game theory). m12 and m13= two versions of the random forest algorithm. Taraldsen's exact density for sampling distribution of correlations added.
| Version: | 0.1.2 |
| Depends: | R (≥ 4.3.0), np (≥ 0.60), generalCorr (≥ 1.2) |
| Imports: | xtable (≥ 1.8.4), ShapleyValue (≥ 0.2.0), NNS (≥ 0.9), randomForest (≥ 4.7), hypergeo (≥ 1.2.13) |
| Suggests: | R.rsp |
| Published: | 2023-12-01 |
| Author: | Hrishikesh Vinod [aut, cre] |
| Maintainer: | Hrishikesh Vinod <vinod at fordham.edu> |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| NeedsCompilation: | no |
| Materials: | NEWS |
| CRAN checks: | practicalSigni results |
| Reference manual: | practicalSigni.pdf |
| Vignettes: |
practicalSigni-vignette practicalSigni-vignette2 |
| Package source: | practicalSigni_0.1.2.tar.gz |
| Windows binaries: | r-devel: practicalSigni_0.1.2.zip, r-release: practicalSigni_0.1.2.zip, r-oldrel: practicalSigni_0.1.2.zip |
| macOS binaries: | r-release (arm64): practicalSigni_0.1.2.tgz, r-oldrel (arm64): practicalSigni_0.1.2.tgz, r-release (x86_64): practicalSigni_0.1.2.tgz, r-oldrel (x86_64): practicalSigni_0.1.2.tgz |
| Old sources: | practicalSigni archive |
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