mlogit2logit {mlogitBMA} | R Documentation |
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
f |
Formula as described in Details of |
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, |
base.choice |
Index of the base alternative within the vector |
varying |
Indices of variables within |
sep |
Separator of variable name and alternative name in the ‘varying’ variables. |
Details
Details of the conversion algorithm are described in the vignette of this package, see vignette('conversion')
.
Value
List with components:
data |
Converted data set. |
formula |
Formula to be used with the converted data set. |
nobs |
Number of observations in the original data set. |
z.index |
Index of all |
z.names |
Names of the |
zcols |
List in which each element corresponds to any of the |
choices |
Vector of names of the alternatives. |
choice.main.intercept |
Index of alternative within |
Note
This function is called from within the bic.mlogit
and thus usually will not need to be called explicitly.
Author(s)
Hana Sevcikova
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.
See Also
Examples
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)