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\name{silhouette.NMF}
\alias{silhouette.NMF}
\title{Silhouette of NMF Clustering}
\usage{
\method{silhouette}{NMF} (x, what = NULL, order = NULL,
...)
}
\arguments{
\item{x}{an NMF object, as returned by
\code{\link{nmf}}.}
\item{what}{defines the type of clustering the computed
silhouettes are meant to assess: \code{'samples'} for the
clustering of samples (i.e. the columns of the target
matrix), \code{'features'} for the clustering of features
(i.e. the rows of the target matrix), and \code{'chc'}
for the consensus clustering of samples as defined by
hierarchical clustering dendrogram, \code{'consensus'}
for the consensus clustering of samples, with clustered
ordered as in the \strong{default} hierarchical
clustering used by \code{\link{consensusmap}} when
plotting the heatmap of the consensus matrix (for
multi-run NMF fits). That is \code{dist = 1 -
consensus(x)}, average linkage and reordering based on
row means.}
\item{order}{integer indexing vector that can be used to
force the silhouette order.}
\item{...}{extra arguments not used.}
}
\description{
Silhouette of NMF Clustering
}
\examples{
\dontshow{# roxygen generated flag
options(R_CHECK_RUNNING_EXAMPLES_=TRUE)
}
x <- rmatrix(75, 15, dimnames = list(paste0('a', 1:75), letters[1:15]))
# NB: using low value for maxIter for the example purpose only
res <- nmf(x, 4, nrun = 3, maxIter = 20)
# sample clustering from best fit
plot(silhouette(res))
# average silhouette are computed in summary measures
summary(res)
# consensus silhouettes are ordered as on default consensusmap heatmap
\dontrun{ op <- par(mfrow = c(1,2)) }
consensusmap(res)
si <- silhouette(res, what = 'consensus')
plot(si)
\dontrun{ par(op) }
# if the order is based on some custom numeric weights
\dontrun{ op <- par(mfrow = c(1,2)) }
cm <- consensusmap(res, Rowv = runif(ncol(res)))
# NB: use reverse order because silhouettes are plotted top-down
si <- silhouette(res, what = 'consensus', order = rev(cm$rowInd))
plot(si)
\dontrun{ par(op) }
# do the reverse: order the heatmap as a set of silhouettes
si <- silhouette(res, what = 'features')
\dontrun{ op <- par(mfrow = c(1,2)) }
basismap(res, Rowv = si)
plot(si)
\dontrun{ par(op) }
}
\seealso{
\code{\link[NMF]{predict}}
}
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