Re: [R] test if all predictors in a glm object are factors
On Wed, 3 Sep 2008, Marc Schwartz wrote:
on 09/03/2008 04:56 PM Michael Friendly wrote:
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
Is an ordered factor a 'discrete factor'? I suspect it is, so this needs
to be
is.discrete.glm - function(model)
all(attr(terms(model), dataClasses)[-1] %in% c(factor, ordered))
(removing redundant braces).
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,
How about something like this:
is.discrete.glm - function(model) {
all(attr(terms(model), dataClasses)[-1] == factor)
}
Essentially, take the output of terms(model), check the 'dataClasses'
attribute, except for the first element, which is the DV.
is.discrete.glm(mod.glm)
[1] TRUE
HTH,
Marc Schwartz
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--
Brian D. Ripley, [EMAIL PROTECTED]
Professor of Applied Statistics, http://www.stats.ox.ac.uk/~ripley/
University of Oxford, Tel: +44 1865 272861 (self)
1 South Parks Road, +44 1865 272866 (PA)
Oxford OX1 3TG, UKFax: +44 1865 272595
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PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
[R] test if all predictors in a glm object are factors
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 Streethttp://www.math.yorku.ca/SCS/friendly.html
Toronto, ONT M3J 1P3 CANADA
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
Re: [R] test if all predictors in a glm object are factors
on 09/03/2008 04:56 PM Michael Friendly wrote:
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,
How about something like this:
is.discrete.glm - function(model) {
all(attr(terms(model), dataClasses)[-1] == factor)
}
Essentially, take the output of terms(model), check the 'dataClasses'
attribute, except for the first element, which is the DV.
is.discrete.glm(mod.glm)
[1] TRUE
HTH,
Marc Schwartz
__
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
