RobStatTM: WMLlogreg – R documentation

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WMLlogreg

Weighted likelihood estimator for the logistic model

Description

This function computes a weighted likelihood estimator for the logistic model, where the weights penalize high leverage observations. In this version the weights are zero or one.

Usage

logregWML(x0, y, intercept = 1)

Arguments

`x0`	p x n matrix of explanatory variables, p is the number of explanatory variables, n is the number of observations
`y`	response vector
`intercept`	1 or 0 indicating if an intercept is included or or not

Value

A list with the following components:

`coefficients`	vector of regression coefficients
`standard.deviation`	standard deviations of the regression coefficient estimators
`fitted.values`	vector with the probabilities of success
`residual.deviances`	residual deviances
`cov`	covariance matrix of the regression estimates
`objective`	value of the objective function at the minimum
`xweights`	vector of zeros and ones used to compute the weighted maimum likelihood estimator

Author(s)

Victor Yohai

References

http://www.wiley.com/go/maronna/robust

Examples

data(skin)
Xskin <- as.matrix( skin[, 1:2] )
yskin <- skin$vasoconst
skinWML <- logregWML(Xskin, yskin, intercept=1)
skinWML$coeff
skinWML$standard.deviation

RobStatTM

Robust Statistics: Theory and Methods

v1.0.2

GPL (>= 3)

Authors

Matias Salibian-Barrera [cre], Victor Yohai [aut], Ricardo Maronna [aut], Doug Martin [aut], Gregory Brownson [aut] (ShinyUI), Kjell Konis [aut], Kjell Konis [cph] (erfi), Christophe Croux [ctb] (WBYlogreg, BYlogreg), Gentiane Haesbroeck [ctb] (WBYlogreg, BYlogreg), Martin Maechler [cph] (lmrob.fit, lmrob..M..fit, lmrob.S), Manuel Koller [cph] (lmrob.fit, .vcov.avar1, lmrob.S, lmrob.lar), Matias Salibian-Barrera [aut]

Initial release

2020-03-02