Description Usage Arguments Details Methods (by generic) References Examples
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | predict(object, ...)
## S4 method for signature 'NMF'
predict(
object,
what = c("columns", "rows", "samples", "features"),
prob = FALSE,
dmatrix = FALSE
)
## S4 method for signature 'NMFfitX'
predict(
object,
what = c("columns", "rows", "samples", "features", "consensus", "chc"),
dmatrix = FALSE,
...
)
|
object |
an NMF model |
... |
other arguments passed to suitable methods, like predict,NMF-method. |
what |
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. |
prob |
logical that indicates if the relative contributions of/to the dominant basis component should be computed and returned. See Details. |
dmatrix |
logical that indicates if a dissimiliarity matrix should be attached to the result. This is notably used internally when computing NMF clustering silhouettes. |
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(k0) / sum_k x(k)
,
for each sample 1≤q j ≤q p, where x(k) is the contribution
of the k-th basis component to j-th sample (i.e. H[k ,j]
), and
x(k0) is the maximum of these contributions.
Features:
p(i) = y(k0) / sum_k y(k)
,
for each feature 1≤q i ≤q p, where y(k) is the contribution
of the k-th basis component to i-th feature (i.e. W[i, k]
), and
y(k0) is the maximum of these contributions.
predict(object = NMF)
: Default method for NMF models
predict(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
.
When what
takes values other that those described above, then all extra arguments are passed to
the predict,NMF-method.
Brunet J, Tamayo P, Golub TR, 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), 4164-9. ISSN 0027-8424, doi: 10.1073/pnas.0308531101 (URL: https://doi.org/10.1073/pnas.0308531101).
Pascual-Montano A, Carazo JM, Kochi K, Lehmann D, Pascual-marqui RD (2006). “Nonsmooth nonnegative matrix factorization (nsNMF).” _IEEE Trans. Pattern Anal. Mach. Intell_, *28*, 403-415.
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