The methods 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, ...) # S4 method for NMF
predict(object,
what = c("columns", "rows", "samples", "features"),
prob = FALSE, dmatrix = FALSE)
# S4 method for NMFfitX
predict(object,
what = c("columns", "rows", "samples", "features", "consensus", "chc"),
dmatrix = FALSE, ...)
an NMF model
a character string that indicates the type of cluster membership should be returned: ‘columns’ or ‘rows’ for clustering the colmuns or the rows of the target matrix respectively. The values ‘samples’ and ‘features’ are aliases for ‘colmuns’ and ‘rows’ respectively.
logical that indicates if the relative contributions of/to the dominant basis component should be computed and returned. See Details.
logical that indicates if a dissimiliarity matrix should be attached to the result. This is notably used internally when computing NMF clustering silhouettes.
additional arguments affecting the predictions produced.
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'returns the cluster
membership based on the hierarchical clustering of the
consensus matrix, as performed by
consensushc.
'consensus'same as '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.
The cluster membership is computed as the index of the
dominant basis component for each sample
(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):
Samples: $$p_j = x_{k_0} / \sum_k
x_k$$, for each sample
\(1\leq j \leq p\), where \(x_k\) is the
contribution of the \(k\)-th basis component to
\(j\)-th sample (i.e. H[k ,j]), and
\(x_{k_0}\) is the maximum of these
contributions.
Features: $$p_i = y_{k_0} / \sum_k y_k$$, for each feature \(1\leq i \leq
p\), where \(y_k\) is the contribution of the
\(k\)-th basis component to \(i\)-th feature (i.e.
W[i, k]), and \(y_{k_0}\) is the maximum
of these contributions.
Brunet J, Tamayo P, Golub TR and Mesirov JP (2004). "Metagenes and molecular pattern discovery using matrix factorization." _Proceedings of the National Academy of Sciences of the United States of America_, *101*(12), pp. 4164-9. ISSN 0027-8424, <URL: http://dx.doi.org/10.1073/pnas.0308531101>, <URL: http://www.ncbi.nlm.nih.gov/pubmed/15016911>.
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.
# roxygen generated flag
options(R_CHECK_RUNNING_EXAMPLES_=TRUE)
# 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)
Run the code above in your browser using DataLab