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

 `1` ```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

 ```1 2 3 4 5 6 7 8``` ```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

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 31, 2017, 2:26 a.m.