logistic.regression.R

logistic.regression.or.ci <- function(regress.out, level=0.95)
{
################################################################
# #
# This function takes the output from a glm #
# (logistic model) command in R and provides not #
# only the usual output from the summary command, but #
# adds confidence intervals for all coefficients and OR’s. #
# #
# This version accommodates multiple regression parameters #
# #
################################################################
usual.output <- summary(regress.out)
z.quantile <- qnorm(1-(1-level)/2)
number.vars <- length(regress.out$coefficients)
OR <- exp(regress.out$coefficients[-1])
temp.store.result <- matrix(rep(NA, number.vars*2), nrow=number.vars)
for(i in 1:number.vars)
{
temp.store.result[i,] <- summary(regress.out)$coefficients[i] +
c(-1, 1) * z.quantile * summary(regress.out)$coefficients[i+number.vars]
}
intercept.ci <- temp.store.result[1,]
slopes.ci <- temp.store.result[-1,]
13
OR.ci <- exp(slopes.ci)
output <- list(regression.table = usual.output, intercept.ci = intercept.ci,
slopes.ci = slopes.ci, OR=OR, OR.ci = OR.ci)
return(output)
}