DAMisc (version 1.3)

glmChange: Maximal First Differences for Generalized Linear Models

Description

For objects of class glm, it calculates the change in predicted responses, for maximal discrete changes in all covariates holding all other variables constant at typical values.

Usage

glmChange(obj, data, typical.dat=NULL, diffchange=c("range", "sd", "unit"), 
    sim=FALSE, R=1000)

Arguments

obj

A model object of class glm.

data

Data frame used to fit object.

typical.dat

Data frame with a single row containing values at which to hold variables constant when calculating first differences. These values will be passed to predict, so factors must take on a single value, but have all possible levels as their leve

diffchange

A string indicating the difference in predictor values to calculate the discrete change. range gives the difference between the minimum and maximum, sd gives plus and minus one-half standard deviation change around the median an

sim

Logical indicating whether simulated confidence bounds on the difference should be calculated and presented.

R

Number of simulations to perform if sim is TRUE

Value

• A list with the following elements:
• diffs
  A matrix of calculated first differences
• minmax
  A matrix of values that were used to calculate the predicted changes

Details

The function calculates the changes in predicted responses for maximal discrete changes in the covariates, for objects of class glm. This function works with polynomials specified with the poly function. It also works with multiplicative interactions of the covariates by virtue of the fact that it holds all other variables at typical values. By default, typical values are the median for quantitative variables and the mode for factors. The way the function works with factors is a bit different. The function identifies the two most different levels of the factor and calculates the change in predictions for a change from the level with the smallest prediction to the level with the largest prediction.

Examples

data(france)
left.mod <- glm(voteleft ~ male + age + retnat + 
	poly(lrself, 2), data=france, family=binomial)
typical.france <- data.frame(
	retnat = factor(1, levels=1:3, labels=levels(france$retnat)), 
	age = 35
	)
glmChange(left.mod, data=france, typical.dat=typical.france)