WMLlogreg: Weighted likelihood estimator for the logistic model

In RobStatTM: Robust Statistics: Theory and Methods

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 documentation built on Nov. 28, 2023, 1:11 a.m.