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,
...
)An object of class "loglmlist", each of which is a class "loglm" object
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
Michael Friendly
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, ...
loglmlist
Other loglinear models:
glmlist(),
joint()
data(Titanic, package="datasets")
# variables are in the order Class, Sex, Age, Survived
tt <- seq_loglm(Titanic)
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