glmChange2: Maximal First Differences for Generalized Linear Models

In davidaarmstrong/damisc_nodep: Dave Armstrong's Miscellaneous Functions

Description

For objects of class glm, it calculates the change in predicted responses, for discrete changes in a covariate holding all other variables at their observed 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 change in predicted probabiliy for a discrete change in a single covariate with all other variables at their observed values, for objects of class glm. This function works with polynomials specified with the poly function.

Value

res	A vector of values giving the average and 95 percent confidence bounds
ames	The average change in predicted probability (across all N observations) for each of the R simulations.
avesamp	The average change in predicted probability for each of the N observation (across all of the R simulations).

Author(s)

Dave Armstrong

Examples

data(france)
left.mod <- glm(voteleft ~ male + age + retnat + 
	poly(lrself, 2), data=france, family=binomial)
glmChange2(left.mod, "age", data=france, "sd")

davidaarmstrong/damisc_nodep documentation built on May 15, 2019, 6:25 p.m.