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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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

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