R: Loglinear Model Utilities

loglin-utilities {vcdExtra} | R Documentation

## Loglinear Model Utilities

### Description

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

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

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

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

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

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

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

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

### Arguments

`nf` | number of factors for which to generate the model
`table` | a contingency table used only for factor names in the model, typically the output from `table` and possibly permuted with `aperm`
`factors` | names of factors used in the model formula when `table` is not specified
`with` | For `joint` and `conditional` models, `with` gives the indices of the factors against which all others are considered jointly or conditionally independent
`order` | For `markov`, this gives the order of the Markov chain model for the factors. An `order=1` Markov chain allows associations among sequential pairs of factors, e.g., `[A,B], [B,C], [C,D]` .... An `order=2` Markov chain allows associations among sequential triples.
`x` | For the `loglin2*` functions, a list of terms in a loglinear model, such as returned by `conditional`, `joint`, ...
`env` | For `loglin2formula`, environment in which to evaluate the formula
`brackets` | For `loglin2string`, characters to use to surround model terms. Either a single character string containing two characters (e.g., `'[]'` or a character vector of length two.
`sep` | For `loglin2string`, the separator character string used for factor names within a given model term
`collapse` | For `loglin2string`, the character string used between terms in the the model string
`abbrev` | For `loglin2string`, whether and how to abbreviate the terms in the string representation. This has not yet been implemented.

### 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]"`.

### Value

For the main model specification functions, `conditional`, `joint`, `markov`, ..., the result is a list of vectors (terms), where the elements in each vector are the names of the factors. The elements of the list are given names `term1, term2, ...`.

### Author(s)

Michael Friendly

### References

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

### See Also

`loglin`, `loglm`

### Examples

    joint(3, table=HairEyeColor)
    # as a formula or string
    loglin2formula(joint(3, table=HairEyeColor))
    loglin2string(joint(3, table=HairEyeColor))

    joint(2, HairEyeColor)  # marginal model for [Hair] [Eye]

    # other possibilities
    joint(4, factors=letters, with=1)
    joint(5, factors=LETTERS)
    joint(5, factors=LETTERS, with=4:5)

    conditional(4)
    conditional(4, with=3:4)

    # use in mosaic displays or other strucplots
    mosaic(HairEyeColor, expected=joint(3))
    mosaic(HairEyeColor, expected=conditional(3))

    # use with MASS::loglm
    cond3 <- loglin2formula(conditional(3, table=HairEyeColor))
    cond3 <- loglin2formula(conditional(3))  # same, with factors 1,2,3
    require(MASS)
    loglm(cond3, data=HairEyeColor)

    saturated(3, HairEyeColor)
    loglin2formula(saturated(3, HairEyeColor))
    loglin2string(saturated(3, HairEyeColor))
    loglin2string(saturated(3, HairEyeColor), brackets='{}', sep=', ')

[Package vcdExtra version 0.7-1 Index]