mlogit2logit: Converting Multinomial Logit Data into Binary Logit Data

Description

Converts multinomial logit data into a combination of several binary logit data sets, in order to analyze it via the Begg & Gray approximation using a binary logistic regression.

Usage

mlogit2logit(f, data, choices = NULL, base.choice = 1, 
             varying = NULL, sep = ".")

Arguments

Formula as described in Details of mnl.spec.

data

Data frame containing the variables of the model.

choices

Vector of names of alternatives. If it is not given, it is determined from the response column of the data frame. Values of this vector should match or be a subset of those in the response column. If it is a subset, data is reduced to contain

base.choice

Index of the base alternative within the vector choices.

varying

Indices of variables within data that are alternative-specific.

sep

Separator of variable name and alternative name in the varying variables.

Value

List with components:
dataConverted data set.
formulaFormula to be used with the converted data set.
nobsNumber of observations in the original data set.
z.indexIndex of all $Z$ columns within data (see vignette for details), i.e. columns that correspond to alternative specific constants.
z.namesNames of the $Z$ columns.
zcolsList in which each element corresponds to any of the data columns that involve $Z$, which is either $Z$ itself or an interaction between a variable and $Z$, (see vignette). The value of such element is a vector with the components name: either $Z$ itself, or name of the corresponding $X$ or $U$ variable with which $Z$ interacts; choice: which alternative it belongs to; intercept: logical determining if it is an alternative specific constant.
choicesVector of names of the alternatives.
choice.main.interceptIndex of alternative within choices that corresponds to the main intercept of the binary logistic model.

Details

Details of the conversion algorithm are described in the vignette of this package, see vignette('conversion').

References

Begg, C.B., Gray, R. (1984) Calculation of polychotomous logistic regression parameters using individualized regressions. Biometrika 71, 11--18.

Yeung, K.Y., Bumgarner, R.E., Raftery, A.E. (2005) Bayesian model averaging: development of an improved multi-class, gene selection and classification tool for microarray data. Bioinformatics 21 (10), 2394--2402.

Examples

Run this code

data(heating)
bin.data <- mlogit2logit(depvar ~ ic + oc, heating, choices=1:5, 
                         varying=3:12, sep='')
bin.glm <- glm(bin.data$formula, 'binomial', data=bin.data$data)
summary(bin.glm)

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