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enetLTS (version 0.1.0)

Robust and Sparse Methods for High Dimensional Linear and Logistic Regression

Description

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) . The algorithm searches for outlier free subsets on which the classical elastic net estimators can be applied.

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Version

Install

install.packages('enetLTS')

Monthly Downloads

167

Version

0.1.0

License

GPL (>= 3)

Maintainer

Fatma Kurnaz

Last Published

January 22nd, 2018

Functions in enetLTS (0.1.0)

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