I'm trying to develop some graphic methods for glm objects, but they
only apply for models
where all predictors are discrete factors. How can I test for this in a
function, given the
glm model object?
That is, I want something that will serve as an equivalent of
is.discrete.glm() in the following
context:
myplot.glm <-
function(model, ...) {
if (!inherits(model,"glm")) stop("requires a glm object")
if (!is.discrete.glm(model)) stop("only factors are allowed")
...
}
A small example, for count data, a poisson glm:
GSS <- data.frame(
expand.grid(sex=c("female", "male"), party=c("dem", "indep", "rep")),
count=c(279,165,73,47,225,191))
mod.glm <- glm(count ~ sex + party, family = poisson, data = GSS)
So, the model terms are sex and party, both factors. Peeking inside
mod.glm, I
can find
> mod.glm$xlevels
$sex
[1] "female" "male"
$party
[1] "dem" "indep" "rep"
and, in str(mod.glm$model) I see
> str(mod.glm$model)
'data.frame': 6 obs. of 3 variables:
$ count: num 279 165 73 47 225 191
$ sex : Factor w/ 2 levels "female","male": 1 2 1 2 1 2
$ party: Factor w/ 3 levels "dem","indep",..: 1 1 2 2 3 3
- attr(*, "terms")=Classes 'terms', 'formula' length 3 count ~ sex + party
....
so this is a keeper. Can someone help me improve on the following
is.discrete.glm() function.
It works for mod.glm, but isn't very general ;-)
is.discrete.glm <- function(model) {
TRUE
}
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept.
York University Voice: 416 736-5115 x66249 Fax: 416 736-5814
4700 Keele Street http://www.math.yorku.ca/SCS/friendly.html
Toronto, ONT M3J 1P3 CANADA
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