Nothing
bic.mlogit <- function (f, data, choices=NULL, base.choice=1,
varying = NULL, sep='.', approx=TRUE,
include.intercepts=TRUE,
verbose=FALSE, ...
) {
cl <- match.call()
bin.logit <- mlogit2logit(f, data=data, choices=choices, base.choice=base.choice,
varying=varying, sep=sep)
mnl <- list()
mnl[['specification']] <- mnl.spec(f, data=data, choices=choices, base.choice=base.choice,
varying=varying, sep=sep)
mnl[['zcols']] <- bin.logit$zcols
mnl[['choice.main.intercept']] <- bin.logit$choice.main.intercept
which.y <- which.max(mnl$specification$response == colnames(data))
mnl[['x']] <- data[-which.y]
mnl[['y']] <- data[,which.y]
varying.tmp <- rep(FALSE, ncol(data))
varying.tmp[varying] <- TRUE
varying.tmp <- varying.tmp[-which.y]
mnl[['varying']] <- cumsum(varying.tmp)
bma.res <- bic.glm.bg(f=bin.logit$formula, data=bin.logit$data, mnl=mnl,
glm.family='binomial', approx=approx,
include.intercepts=include.intercepts,
verbose=verbose, ...
)
bma.res$bic.glm$call <- cl
invisible(structure(list(bic.glm=bma.res$bic.glm, bin.logit=bin.logit, spec=mnl[['specification']],
bma.specifications=bma.res$specifications, approx=approx),
class='bic.mlogit'))
}
summary.bic.mlogit <- function(object, ...) {
summary(object$bic.glm, ...)
cat('\nMNL specification:\n')
cat('==================')
summary(object$spec, ...)
}
imageplot.mlogit <- function(x, ...) imageplot.bma(x$bic.glm, ...)
plot.bic.mlogit <- function(x, ...) plot(x$bic.glm, ...)
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