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 discrete changes in a covariate holding all other variables at their observed values.

Usage

glmChange2(
  obj,
  varname,
  data,
  V = NULL,
  diffchange = c("unit", "sd"),
  outcome = c("diff", "maxdiff"),
  baseline = c("obs", "median"),
  catdiff = c("biggest", "all"),
  n = 1,
  R = 1500,
  adjust = c("none", "shift", "trim"),
  ...
)

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.
V	An optional variance-covariance matrix for the coefficients, if NULL, will be obtained through a call to vcov.
diffchange	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.
outcome	For quantitative variables, should the difference over the range of chosen values be calculated (the default) or should the maximum probability difference over the range be calculated. These will be the same for single-term quantitative variables, but could be different for multi-term variables, like splines and polynomials.
baseline	Character string representing the baseline to use for the change. It can be one of "obs", in which case each observations value is used as the baseline or "median", in which case the median is used as a common baseline for all observations.
catdiff	String identifying how differences in factor variables is handled. Options are "all" in which case all pairwise differences are returned, or "biggest" in which case the biggest difference is returned.
n	Number of diffchange to move.
R	Number of simulations to perform.
adjust	String identifying how range should be changed if it goes out of the bounds of the observed data. Trimming will simply truncate the size of the change to make it fit in bounds. Shifting will shift the interval so both ends are in bounds. If the shifted interval is wider than the range of the data, the change will be truncated to the range of the data.
...	Allows user to specify legacy argument change

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, 
diffchange="sd")

DAMisc documentation built on Jan. 12, 2022, 1:07 a.m.