This function takes an n-way contingency table and fits a series of sequential models to the 1-, 2-, ... n-way marginal tables, corresponding to a variety of types of loglinear models.
seq_loglm(x,
type = c("joint", "conditional", "mutual", "markov", "saturated"),
marginals = 1:nf, vorder = 1:nf,
k = NULL, prefix = "model", fitted = TRUE, ...)a contingency table in array form, with optional category labels specified in the
dimnames(x) attribute,
or else a data.frame in frequency form, with the frequency variable named "Freq".
type of sequential model to fit, a character string.
One of "joint", "conditional", "mutual", "markov",
or "saturated".
which marginal sub-tables to fit? A vector of a (sub)set of the integers, 1:nf
where nf is the number of factors in the full n-way table.
order of variables, a permutation of the integers 1:nf,
used to reorder the variables in the original table for the purpose
of fitting sequential marginal models.
conditioning variable(s) for type = "joint", "conditional"
or Markov chain order for type = "markov"
prefix used to give names to the sequential models
argument passed to loglm to store the fitted values in the
model objects
other arguments, passed down
An object of class "loglmlist", each of which is a class "loglm" object
Sequential marginal models for an n-way tables begin with the
model of equal-probability for the one-way margin
(equivalent to a chisq.test) and add
successive variables one at a time in the order specified by
vorder.
All model types give the same result for the two-way margin, namely the test of independence for the first two factors.
Sequential models of joint independence (type="joint")
have a particularly simple interpretation, because they
decompose the likelihood ratio test for the model of
mutual independence in the full n-way table, and hence
account for "total" association in terms of portions attributable
to the conditional probabilities of each new variable,
given all prior variables.
These functions were inspired by the original SAS implementation of mosaic displays, described in the User's Guide, http://www.datavis.ca/mosaics/mosaics.pdf
loglin-utilities for descriptions of sequential models,
conditional,
joint,
mutual, …
# NOT RUN {
data(Titanic, package="datasets")
# variables are in the order Class, Sex, Age, Survived
tt <- seq_loglm(Titanic)
# }
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