Nothing
`predict.mlds` <-
function(object, newdata = NULL,
type = "link", ...) {
#object, obj of class mlds
# don't need type "terms"
miss <- missing(newdata)
if (object$method == "glm") {
if (miss) {
ans <- predict(object$obj, type = type, ...) } else
{
if (length(newdata) == 5) newdata <- make.ix.mat(newdata)
ans <- predict(object$obj, newdata = newdata,
type = type, ...)
}
} else
{
fam <- binomial(link = object$link)
psc <- object$pscale
s <- object$sigma
if (miss) d <- object$data else
d <- newdata
del <- matrix(psc[unlist(d[, -d$resp])],
# matrix(psc[unlist(subset(d, select = -resp))],
ncol = 4) %*% c(1, -1, -1, 1)
z <- del/s
if (type == "link") ans <- z else
ans <- fam$linkinv(z)
}
as.vector(ans)
}
`predict.mlbs` <- function (object, newdata = NULL, type = "link", ...) {
miss <- missing(newdata)
if (miss) {
ans <- predict(object$obj, type = type, ...)
}
else {
mx <- max(newdata[, -1])
d <- within(newdata, {S1 <- factor(S1, levels = seq_len(mx))
S2 <- factor(S2, levels = seq_len(mx))
S3 <- factor(S3, levels = seq_len(mx))
})
m.lst <- lapply(names(d[, -1]), function(nm) {
f <- as.formula(paste("~", nm))
m <- model.matrix(f, d)
if (nm == "S2") m <- -2 * m
m
})
m <- Reduce("+", m.lst)
dsInc.df <- data.frame(newdata[, 1], m[, -1])
names(dsInc.df) <- c("resp", paste("S", 2:mx, sep = ""))
ans <- predict(object$obj, newdata = dsInc.df, type = type,
...)
}
as.vector(ans)
}
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