Extracts selected clusters and cluster prototypes from the glmnet lasso output
getClusterSelsFromGlmnet.RdExtracts selected clusters and cluster prototypes from the glmnet lasso output
Arguments
- lasso_sets
A list of integer vectors. Each vector represents a set of features selected by the lasso for a given value of the penalty parameter lambda.
- clusters
A named list where each entry is an integer vector of indices of features that are in a common cluster. (The length of list clusters is equal to the number of clusters.) All identified clusters must be non-overlapping. All features appear in exactly one cluster (any unclustered features must be in their own "cluster" of size 1).
- prototypes
An integer vector whose length must be equal to the number of clusters. Entry i should be the index of the feature belonging to cluster i that is most highly correlated with y (that is, the prototype for the cluster, as in the protolasso; see Reid and Tibshirani 2016).
- feat_names
Character vector; the names of the features in X. (If the X provided to protolasso or clusterRepLasso did not have feature names, feat_names will be NA.)
Value
A list containing the following items:
- selected_sets
A list of integer vectors. Entry k of this list contains a selected set of size k yielded by glmnet--each member of the set is the index of a single feature from a cluster selected by either the protolasso or the cluster representative lasso (the prototype from that cluster--the cluster member most highly correlated with y). (If no set of size k was selected, entry k will be NULL.)
- selected_clusts_list
A list of lists; entry k of this list is a list of length k of clusters (the clusters that were selected by the cluster representative lasso). Again, if no set of size k was selected, entry k will be NULL.
References
Reid, S., & Tibshirani, R. (2016). Sparse regression and marginal
testing using cluster prototypes. Biostatistics, 17(2), 364–376.
https://doi.org/10.1093/biostatistics/kxv049.
Bühlmann, P.,
Rütimann, P., van de Geer, S., & Zhang, C. H. (2013). Correlated variables in
regression: Clustering and sparse estimation.
Journal of Statistical Planning and Inference, 143(11), 1835–1858.
https://doi.org/10.1016/j.jspi.2013.05.019.