% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nmfkc.signed.R
\name{predict.nmfkc.signed}
\alias{predict.nmfkc.signed}
\title{Predict method for nmfkc.signed}
\usage{
\method{predict}{nmfkc.signed}(object, newA = NULL, type = c("response", "prob", "class"), ...)
}
\arguments{
\item{object}{A fitted \code{"nmfkc.signed"} object.}

\item{newA}{Real-valued \eqn{D \times N_{\mathrm{new}}} covariate matrix.}

\item{type}{Output: \code{"response"} (raw signed), \code{"prob"},
or \code{"class"}.}

\item{...}{Unused.}
}
\value{
A numeric matrix (\code{"response"} or \code{"prob"}) or a
character vector (\code{"class"}).
}
\description{
Computes \eqn{\widehat Y = X \, C \, A_{\mathrm{new}}}
(\eqn{= X (C_{+} - C_{-})(A_{+}^{\mathrm{new}} - A_{-}^{\mathrm{new}})}).
For \code{type = "response"} the raw prediction is returned
(possibly signed).  For \code{type = "prob"} and \code{"class"},
negative entries of \eqn{\widehat Y} are clipped to zero before
column normalization, since probabilities must be non-negative.
}
\section{Lifecycle}{

This function is \strong{experimental}. The interface may change in
future versions.
}

\references{
Ding, C. H. Q., Li, T., & Jordan, M. I. (2010). Convex and
semi-nonnegative matrix factorizations. \emph{IEEE Transactions on
Pattern Analysis and Machine Intelligence}, 32(1), 45--55.
}
