predict
for NMF models return the
cluster membership of each sample or each feature.
Currently the classification/prediction of new data is
not implemented.
predict(object, ...)
"predict"(object, what = c("columns", "rows", "samples", "features"), prob = FALSE, dmatrix = FALSE)
"predict"(object, what = c("columns", "rows", "samples", "features", "consensus", "chc"), dmatrix = FALSE, ...)
signature(object = "NMF")
: Default
method for NMF models signature(object = "NMFfitX")
:
Returns the cluster membership index from an NMF model
fitted with multiple runs. Besides the type of clustering available for any NMF
models ('columns', 'rows', 'samples', 'features'
),
this method can return the cluster membership index based
on the consensus matrix, computed from the multiple NMF
runs. Argument what
accepts the following extra types:
'chc'
consensushc
.'consensus'
'chc'
but the levels of the membership
index are re-labeled to match the order of the clusters
as they would be displayed on the associated dendrogram,
as re-ordered on the default annotation track in
consensus heatmap produced by
consensusmap
.what='samples' or 'columns'
) or each feature
(what='features' or 'rows'
), based on their
corresponding entries in the coefficient matrix or basis
matrix respectively. For example, if what='samples'
, then the dominant
basis component is computed for each column of the
coefficient matrix as the row index of the maximum within
the column.
If argument prob=FALSE
(default), the result is a
factor
. Otherwise a list with two elements is
returned: element predict
contains the cluster
membership index (as a factor
) and element
prob
contains the relative contribution of the
dominant component to each sample (resp. the relative
contribution of each feature to the dominant basis
component):
H[k ,j]
), and
$x(k0)$ is the maximum of these
contributions.
W[i, k]
), and $y(k0)$ is the maximum
of these contributions.
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D and Pascual-marqui RD (2006). "Nonsmooth nonnegative matrix factorization (nsNMF)." _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, pp. 403-415.
# random target matrix
v <- rmatrix(20, 10)
# fit an NMF model
x <- nmf(v, 5)
# predicted column and row clusters
predict(x)
predict(x, 'rows')
# with relative contributions of each basis component
predict(x, prob=TRUE)
predict(x, 'rows', prob=TRUE)
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