Nothing
newY <- function(y, family) {
if (is.data.frame(y)) y <- as.matrix(y)
if (is.matrix(y)) {
d <- dim(y)
y <- t(y)
} else {
d <- c(length(y), 1)
}
# Convert fuzzy binomial data
if (family=="binomial" && typeof(y) != "logical") {
tab <- table(y)
if (length(tab) > 2) stop("Attemping to use family='binomial' with non-binary data", call.=FALSE)
if (!identical(names(tab), c("0", "1"))) {
message(paste0("Logistic regression modeling Pr(y=", names(tab)[2], ")"))
y <- as.double(as.character(y) == names(tab)[2])
if (d[2] > 1) attr(y, "dim") <- d
}
}
# Convert to double, if necessary
if (typeof(y) != "double") {
tryCatch(storage.mode(y) <- "double", warning=function(w) {stop("y must be numeric or able to be coerced to numeric", call.=FALSE)})
}
if (any(is.na(y))) stop("Missing data (NA's) detected in outcome y. You must eliminate missing data (e.g., by removing cases or imputation) before passing y to grpreg", call.=FALSE)
# Handle multi
if (is.matrix(y)) {
if (ncol(y) > 1) {
if (is.null(colnames(y))) paste("Y", 1:ncol(y), sep="")
}
attributes(y) <- NULL
}
if (family=="gaussian") {
meanY <- mean(y)
y <- y - meanY
attr(y, "mean") <- meanY
}
attr(y, "m") <- d[2]
y
}
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