logregWML {RobStatTM}

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