Clustering and Prediction
predict for NMF models return the
cluster membership of each sample or each feature.
Currently the classification/prediction of new data is
1 2 3 4 5 6 7 8 9 10 11
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.
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
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
prob=FALSE (default), the result is a
factor. Otherwise a list with two elements is
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
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.
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.
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.
whataccepts the following extra types:
returns the cluster membership based on the hierarchical clustering of the consensus matrix, as performed by
'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
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.
1 2 3 4 5 6 7 8 9 10 11 12
Want to suggest features or report bugs for rdrr.io? Use the GitHub issue tracker.