Learn R Programming

⚠️There's a newer version (2.0.3) of this package.Take me there.

rmargint (version 2.0.2)

Robust Marginal Integration Procedures

Description

Three robust marginal integration procedures for additive models based on local polynomial kernel smoothers. As a preliminary estimator of the multivariate function for the marginal integration procedure, a first approach uses local constant M-estimators, a second one uses local polynomials of order 1 over all the components of covariates, and the third one uses M-estimators based on local polynomials but only in the direction of interest. For this last approach, estimators of the derivatives of the additive functions can be obtained. All three procedures can compute predictions for points outside the training set if desired. See Boente and Martinez (2017) for details.

Copy Link

Version

Install

install.packages('rmargint')

Monthly Downloads

247

Version

2.0.2

License

GPL (>= 3.0)

Maintainer

Alejandra Martinez

Last Published

August 4th, 2020

Functions in rmargint (2.0.2)

k.epan

Epanechnikov kernel
deviance.margint

Deviance for objects of class margint
kernel4

Order 4 kernel
kernel6

Order 6 kernel
fitted.values.margint

Fitted values for objects of class margint
formula.margint

Additive model formula
kernel8

Order 8 kernel
kernel10

Order 10 kernel
margint.cl

Classic marginal integration procedures for additive models
psi.huber

Derivative of Huber's loss function.
margint.rob

Robust marginal integration procedures for additive models
summary.margint

Summary for additive models fits using a marginal integration procedure
print.margint

Print a Marginal Integration procedure
predict.margint

Fitted values for objects of class margint
psi.tukey

Derivative of Tukey's bi-square loss function.
rmargint-package

Robust marginal integration estimators for additive models.
residuals.margint

Residuals for objects of class margint
my.norm.2

Euclidean norm of a vector
plot.margint

Diagnostic plots for objects of class margint