I'm developing some functions to create symbolic specifications for
loglinear models of different types.
I don't really know how to 'compute' with model formulas, so I've done
this in the notation
for stats::loglin(), which is a list of high-order terms in the model.
What I'd like is a function to turn the results of these into a model
formula, suitable for
MASS::loglm. That's the reverse of what loglm does.
For example, the simplest versions of models for 3-way tables for joint,
conditional, and marginal independence can be computed as follows.
After each, I indicated
the WANTED model formula I'd like from the result
> joint(3)
$term1
[1] 1 2
$term2
[1] 3
WANTED: ~ 1:2 + 3
> condit(3)
$term1
[1] 1 3
$term2
[1] 2 3
WANTED: ~ 1:2 + 2:3
> mutual(3)
$term1
[1] 1
$term2
[1] 2
$term3
[1] 3
WANTED: ~ 1 + 2 + 3
In case anyone want to play with the code, here are the current, not too
elegant definitions
of the functions, and some further test cases,
# models of joint independence
joint <- function(nf, factors=1:nf, with=nf) {
if (nf == 1) return (list(term1=factors[1]))
if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
others <- setdiff(1:nf, with)
result <- list(term1=factors[others], term2=factors[with])
result
}
# conditional independence
condit <- function(nf, factors=1:nf, with=nf) {
if (nf == 1) return (list(term1=factors[1]))
if (nf == 2) return (list(term1=factors[1], term2=factors[2]))
main <- setdiff(1:nf, with)
others <- matrix(factors[with], length(with), length(main))
result <- rbind(factors[main], others)
result <- as.list(as.data.frame(result, stringsAsFactors=FALSE))
names(result) <- paste('term', 1:length(result), sep='')
result
}
# mutual independence
mutual <- function(nf, factors=1:nf) {
result <- sapply(factors[1:nf], list)
names(result) <- paste('term', 1:length(result), sep='')
result
}
### some comparisons
loglin(HairEyeColor, list(c(1, 2), c(1, 3), c(2, 3)))$lrt
loglm(~1:2 + 1:3 +2:3, HairEyeColor)
# use factor names
joint(3, factors=names(dimnames(HairEyeColor)))
condit(3, factors=names(dimnames(HairEyeColor)))
loglin(HairEyeColor, joint(3))$lrt
loglm(~1:2 + 3, HairEyeColor)
loglin(HairEyeColor, condit(3))$lrt
loglm(~1:3 + 2:3, HairEyeColor)
--
Michael Friendly Email: friendly AT yorku DOT ca
Professor, Psychology Dept. & Chair, Quantitative Methods
York University Voice: 416 736-2100 x66249 Fax: 416 736-5814
4700 Keele Street Web: http://www.datavis.ca
Toronto, ONT M3J 1P3 CANADA
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