Nothing
predict.opticut <-
function (object, gnew=NULL, xnew=NULL, ...)
{
if (is.null(xnew) && is.null(gnew)) {
fit <- fitted(object)
} else {
if (is.na(object$comb) || object$comb != "rank")
stop("predict method for new data available only for comb=rank")
if (is.null(gnew))
stop("gnew must be provided")
if (is.null(xnew)) {
xnew <- matrix(1, length(gnew), 1L)
}
bp <- summary(object)$bestpart
ff <- formula(object)
ff[[2]] <- NULL
X <- if (is.data.frame(xnew))
model.matrix(ff, xnew) else data.matrix(xnew)
Dist <- strsplit(object$dist, ":", fixed=TRUE)[[1L]][1L]
Link <- strsplit(object$dist, ":", fixed=TRUE)[[1L]][2L]
linkinv <- .get_linkinv(object)
g <- factor(gnew, levels(object$strata))
if (any(is.na(X)) || any(is.na(g)))
stop("new data must not have any NA")
if (nrow(X) != length(g))
stop("length of gnew must equal nrow(xnew)")
cf <- sapply(names(object$species), function(z)
getMLE(object, z, vcov=FALSE)$coef)
fit <- matrix(0, nrow(X), ncol(cf))
dimnames(fit) <- list(rownames(X), colnames(cf))
for (i in seq_len(ncol(cf))) {
gg <- ifelse(g %in% colnames(bp)[bp[i,] == 1], 1, 0)
fit[,i] <- .predict_dist(cf[,i],
dist=Dist, link=Link, X=X, Z=gg, linkinv=linkinv)
# XX <- cbind(X[,1L,drop=FALSE], gg, X[,-1L,drop=FALSE])
# fit[,i] <- linkinv(drop(XX %*% cf[,i]))
}
}
fit
}
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