Wrapper function to generate predictions from cluster stability selected model in one step
cssPredict.RdSelect clusters using cluster stability selection, form cluster representatives, fit a linear model, and generate predictions from a matrix of unlabeled data. This is a wrapper function for css and getCssPreds. Using cssPredict is simpler, but it has fewer options, and it executes the full (computationally expensive) subsampling procedured every time it is called. In contrast, css can be called just once, and then cssPredict can quickly return results for different matrices of new data or using different values of cutoff, max_num_clusts, etc. by using the calculations done in one call to css.
Usage
cssPredict(
X_train_selec,
y_train_selec,
X_test,
clusters = list(),
lambda = NA,
cutoff = NA,
max_num_clusts = NA,
train_inds = NA,
auto_select_size = TRUE
)Arguments
- X_train_selec
An n x p numeric matrix (preferably) or a data.frame (which will be coerced internally to a matrix by the function model.matrix) containing the p >= 2 features/predictors. The data from X_train_selec and y_train_selec will be split into two parts; half of the data will be used for feature selection by cluster stability selection, and half will be used for estimating a linear model on the selected cluster representatives.
- y_train_selec
A length-n numeric vector containing the responses;
y[i]is the response corresponding to observationX[i, ]. Unlke the more general setup of css, y_train_selec must be real-valued because predictions will be generated by ordinary least squares.- X_test
A numeric matrix (preferably) or a data.frame (which will be coerced internally to a matrix by the function model.matrix) containing the data that will be used to generate predictions. Must contain the same features (in the same number of columns) as X_train_selec, and if the columns of X_test are named, they must match the names of X_train_selec.
- clusters
Optional; either an integer vector of a list of integer vectors; each vector should contain the indices of a cluster of features (a subset of 1:p). (If there is only one cluster, clusters can either be a list of length 1 or an integer vector.) All of the provided clusters must be non-overlapping. Every feature not appearing in any cluster will be assumed to be unclustered (that is, they will be treated as if they are in a "cluster" containing only themselves). If clusters is a list of length 0 (or a list only containing clusters of length 1), then css() returns the same results as stability selection (so feat_sel_mat will be identical to clus_sel_mat). Names for the clusters will be needed later; any clusters that are not given names in the list clusters will be given names automatically by css. CAUTION: if the provided X is a data.frame that contains a categorical feature with more than two levels, then the resulting matrix made from model.matrix will have a different number of columns than the provided data.frame, some of the feature numbers will change, and the clusters argument will not work properly (in the current version of the package). To get correct results in this case, please use model.matrix to convert the data.frame to a numeric matrix on your own, then provide this matrix and cluster assignments with respect to this matrix.Default is list() (so no clusters are specified, and every feature is assumed to be in a "cluster" containing only itself).
- lambda
Optional; the tuning parameter to be used by the lasso for feature selection in each subsample. If lambda is not provided, cssPredict will choose one automatically by cross-validation. Default is NA.
- cutoff
Numeric; getCssPreds will make use only of those clusters with selection proportions equal to at least cutoff. Must be between 0 and 1. Default is 0 (in which case either all clusters are used, or max_num_clusts are used, if max_num_clusts is specified).
- max_num_clusts
Integer or numeric; the maximum number of clusters to use regardless of cutoff. (That is, if the chosen cutoff returns more than max_num_clusts clusters, the cutoff will be decreased until at most max_num_clusts clusters are selected.) Default is NA (in which case max_num_clusts is ignored).
- train_inds
Optional; an integer or numeric vector containing the indices of observations in X and y to set aside for model training after feature selection. If train_inds is not provided, half of the data will be used for feature selection and half for model estimation (chosen at random).
- auto_select_size
Logical; if TRUE, then max_num_clusts will be automatically estimated using the lasso with cross-validation. Default is TRUE, though his argument is ignored if either cutoff or max_num_clusts is provided. (If desired output is to generate predictions using all clusters, you should set auto_select_size to FALSE and do not provide cutoff or max_num_clusts.)