% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/nmfkc.signed.R
\name{nmfkc.signed.cv}
\alias{nmfkc.signed.cv}
\title{Column-wise k-fold cross-validation for nmfkc.signed}
\usage{
nmfkc.signed.cv(Y, A, rank = 2, ...)
}
\arguments{
\item{Y}{Real-valued \eqn{Q_{\mathrm{obs}} \times N} response matrix
(signed entries allowed).}

\item{A}{Real-valued \eqn{D \times N} covariate matrix (signed).}

\item{rank}{Integer \eqn{Q}.}

\item{...}{Passed to \code{\link{nmfkc.signed}}; also accepts
\code{nfolds} (default 5; \code{div} alias), \code{seed}
(default 123), \code{shuffle} (default \code{TRUE}).}
}
\value{
A list with \code{objfunc} (mean squared prediction error),
\code{sigma} (RMSE), \code{objfunc.block} (per-fold MSE vector),
\code{block} (integer fold assignment of length \eqn{N}).  Field
names match \code{\link{nmfkc.cv}}.
}
\description{
Column-wise k-fold CV by held-out samples: for each
fold, the model is fit on the training columns and evaluated on the
held-out columns by solving for new-sample coefficients via a
weighted refit with \eqn{X} fixed.
}
\section{Lifecycle}{

This function is \strong{experimental}.
}

\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.
}
\seealso{
\code{\link{nmfkc.signed}}, \code{\link{nmfkc.signed.ecv}}
}
