logregperm-package {logregperm}R Documentation

Inference in Logistic Regression

Description

A permutation test is used for inference in logistic regression. The procedure is useful when parameter estimates in ordinary logistic regression fail to converge or are unreliable due to small sample size, or when the conditioning in exact conditional logistic regression restricts the sample space too severely (as when independent variables are continuous).

Details

Package: logregperm
Type: Package
Version: 1.0
Date: 2008-03-18
License: GPL-3

The function prr.test requires that the user identify one independent variable for which a test of the null hypothesis (that the regression coefficient is zero) is to be performed. This variable and the dependent binary variable are supplied as vector arguments; the remaining independent variables are supplied as a matrix.

Author(s)

Douglas M. Potter

Maintainer: Douglas M Potter <potter@upci.pitt.edu>

References

Potter D.M. (2005) A permutation test for inference in logistic regression with small- and moderate-sized datasets. Statistics in Medicine, 24:693-708.

Examples


##40 observations, 3 independent variables

nobs<-40

x1<-rnorm(nobs)
x2<-rnorm(nobs)
xx<-cbind(x1,x2)

x0<-rnorm(nobs)+x1+x2

y<-x0+x1+x2+2*rnorm(nobs)
y<-ifelse(y>0,1,0)

prr.test(y,x0,xx)

##compare prr.test with ordinary logistic regression 
##using a likelihood ratio test

t1<-glm(y~x0+x1+x2,family=binomial)
t2<-glm(y~x1+x2,family=binomial)
1-pchisq(abs(anova(t1,t2)$Deviance[2]),1)


[Package logregperm version 1.0 Index]