glmChange2: Maximal First Differences for Generalized Linear Models

In DAMisc: Dave Armstrong's Miscellaneous Functions

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

glmChange2(obj, varname, data, change=c("unit", "sd"), R=1500)

Arguments

obj	A model object of class glm.
varname	Character string giving the variable name for which average effects are to be calculated.
data	Data frame used to fit object.
change	A string indicating the difference in predictor values to calculate the discrete change. sd gives plus and minus one-half standard deviation change around the median and unit gives a plus and minus one-half unit change around the median.
R	Number of simulations to perform.

Details

The function calculates the average effect discrete changes in the covariates, for objects of class glm. This function works with polynomials specified with the poly function.

Value

A vector of values giving the average and 95 percent confidence bounds

Author(s)

Dave Armstrong (UW-Milwaukee, Department of Political Science)

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
	)
glmChange2(left.mod, "age", data=france, "sd")

Example output

Loading required package: car
Loading required package: effects
Loading required package: carData

Attaching package: 'carData'

The following objects are masked from 'package:car':

    Guyer, UN, Vocab

lattice theme set by effectsTheme()
See ?effectsTheme for details.
          mean       lower       upper
age -0.0426603 -0.06744547 -0.01781606

DAMisc documentation built on May 2, 2019, 4:52 p.m.