enetLTS v0.1.0
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Robust and Sparse Methods for High Dimensional Linear and Logistic Regression
Fully robust versions of the elastic net estimator are introduced for linear and logistic regression, in particular high dimensional data by Kurnaz, Hoffmann and Filzmoser (2017) <DOI:10.1016/j.chemolab.2017.11.017>. The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.
Functions in enetLTS
| Name | Description | |
| plot.enetLTS | plots from the "enetLTS" object | |
| plotCoef.enetLTS | coefficients plots from the "enetLTS" object | |
| coef.enetLTS | coefficients from the enetLTS object | |
| cv.enetLTS | Cross-validation for the enetLTS object | |
| enetLTS | Robust and sparse estimation for linear and logistic regression | |
| fitted.enetLTS | the fitted values from the "enetLTS" object. | |
| lambda00 | Upper limit of the penalty parameter for family="binomial" | |
| nonzeroCoef.enetLTS | nonzero coefficients indices from the "enetLTS" object | |
| plotDiagnostic.enetLTS | diagnostics plots from the "enetLTS" object | |
| plotResid.enetLTS | residuals plots from the "enetLTS" object | |
| predict.enetLTS | make predictions from the "enetLTS" object. | |
| print.enetLTS | print from the "enetLTS" object | |
| residuals.enetLTS | the residuals from the "enetLTS" object | |
| weights.enetLTS | binary weights from the "enetLTS" object | |
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Details
| Type | Package |
| Date | 2018-01-18 |
| License | GPL (>= 3) |
| NeedsCompilation | no |
| Packaged | 2018-01-19 20:21:04 UTC; fatmasevinckurnaz |
| Repository | CRAN |
| Date/Publication | 2018-01-22 09:31:45 UTC |
| imports | cvTools , ggplot2 , glmnet , grid , parallel , reshape , robustHD , stats |
| Contributors | Peter Filzmoser, Irene Hoffmann, Fatma KURNAZ |
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[](http://www.rdocumentation.org/packages/enetLTS)