Loglinear Model Utilities

These functions generate lists of terms to specify a loglinear model in a form compatible with loglin and also provide for conversion to an equivalent loglm specification or a shorthand character string representation.

They allow for a more conceptual way to specify such models by a function for their type, as opposed to just an uninterpreted list of model terms and also allow easy specification of marginal models for a given contingency table. They are intended to be used as tools in higher-level modeling and graphics functions, but can also be used directly.

Usage

joint(nf, table = NULL, factors = 1:nf, with = nf)

conditional(nf, table = NULL, factors = 1:nf, with = nf)

mutual(nf, table = NULL, factors = 1:nf)

saturated(nf, table = NULL, factors = 1:nf)

markov(nf, factors = 1:nf, order = 1)

loglin2formula(x, env = parent.frame())

loglin2string(x, brackets = c("[", "]"), sep = ",", collapse = " ", abbrev)

Source

Code from Henrique Dallazuanna, wwwhsd@gmail.com, R-help 7-4-2013

Arguments

nf: number of factors for which to generate model
table: a contingency table used for factor names, typically the output from table
factors: names of factors used in the model when table is not specified
with: indices of the factors against which others are considered conditionally independent
order: order of the markov chain
x: a list of terms in a loglinear model, such as returned by joint, conditional, ...
env: environment in which to evaluate the formula
brackets: characters to use to surround model terms. Either a single character string containing two characters or a character vector of length two.
sep: characters used to separate factor names within a term
collapse: characters used to separate terms
abbrev: Unused as yet

Details

The main model specification functions, conditional, joint, markov, ..., saturated, return a list of vectors indicating the marginal totals to be fit, via the margin argument to loglin. Each element of this list corresponds to a high-order term in a hierarchical loglinear model, where, e.g., a term like c("A", "B") is equivalent to the loglm term "A:B" and hence automatically includes all low-order terms.

Note that these can be used to supply the expected argument for the default mosaic function, when the data is supplied as a contingency table.

The table below shows some typical results in terms of the standard shorthand notation for loglinear models, with factors A, B, C, ..., where brackets are used to delimit the high-order terms in the loglinear model.

function	3-way	4-way	5-way
`mutual`	[A] [B] [C]	[A] [B] [C] [D]	[A] [B] [C] [D] [E]
`joint`	[AB] [C]	[ABC] [D]	[ABCE] [E]
`joint (with=1)`	[A] [BC]	[A] [BCD]	[A] [BCDE]
`conditional`	[AC] [BC]	[AD] [BD] [CD]	[AE] [BE] [CE] [DE]
`condit (with=1)`	[AB] [AC]	[AB] [AC] [AD]	[AB] [AC] [AD] [AE]
`markov (order=1)`	[AB] [BC]	[AB] [BC] [CD]	[AB] [BC] [CD] [DE]
`markov (order=2)`	[A] [B] [C]	[ABC] [BCD]	[ABC] [BCD] [CDE]
`saturated`	[ABC]	[ABCD]	[ABCDE]

loglin2formula converts the output of one of these to a model formula suitable as the formula for of loglm.

loglin2string converts the output of one of these to a string describing the loglinear model in the shorthand bracket notation, e.g., "[A,B] [A,C]".

Usage

Source

Arguments

Details

See also