Generates matrix of selection indicators from stability selection.

Usage

getSelMatrix(
  x,
  y,
  lambda,
  B,
  sampling_type,
  subsamps_object,
  num_cores,
  fitfun = cssLasso
)

Arguments

x: an n x p numeric matrix or a data.frame containing the predictors.
y: A response vector; can be any response that takes the form of a length n vector and is used (or not used) by fitfun. Typically (and for default fitfun = cssLasso), y should be an n-dimensional numeric vector containing the response.
lambda: A tuning parameter or set of tuning parameters that may be used by the feature selection method fitfun. In the default case when fitfun = cssLasso, lambda should be a numeric: the penalty to use for each lasso fit. (css() does not require lambda to be any particular object because for a user-specified feature selection method fitfun, lambda can be an arbitrary object. See the description of fitfun below.)
B: Integer or numeric; the number of subsamples. Note: For sampling_type=="MB" the total number of subsamples will be B; for sampling_type="SS" the number of subsamples will be 2*B. Default is 100 for sampling_type="MB" and 50 for sampling_type="SS".
sampling_type: A character vector; either "SS" or "MB". For "MB", all B subsamples are drawn randomly (as proposed by Meinshausen and Bühlmann 2010). For "SS", in addition to these B subsamples, the B complementary pair subsamples will be drawn as well (see Faletto and Bien 2022 or Shah and Samworth 2013 for details). Default is "SS", and "MB" is not supported yet.
subsamps_object: A list of length B (or 2*B if sampling_type="SS"), where each element is one of the following: subsampleAn integer vector of size n/2 containing the indices of the observations in the subsample. drop_var_inputA named list containing two elements: one named "subsample", matching the previous description, and a logical vector named "feats_to_keep" containing the indices of the features to be automatically selected. (The first object is the output of the function createSubsamples when the provided prop_feats_remove is 0, the default, and the second object is the output of createSubsamples when prop_feats_remove > 0.)
num_cores: Optional; an integer. If using parallel processing, the number of cores to use for parallel processing (num_cores will be supplied internally as the mc.cores argument of parallel::mclapply).
fitfun: A function; the feature selection function used on each subsample by cluster stability selection. This can be any feature selection method; the only requirement is that it accepts the arguments (and only the arguments) X, y, and lambda and returns an integer vector that is a subset of 1:p. For example, fitfun could be best subset selection or forward stepwise selection or LARS and lambda could be the desired model size; or fitfun could be the elastic net and lambda could be a length-two vector specifying lambda and alpha. Default is cssLasso, an implementation of lasso (relying on the R package glmnet), where lambda must be a positive numeric specifying the L1 penalty for the lasso.

Value

A binary integer matrix of dimension B x p (if sampling_type == "MB") or 2*B x p (if sampling_type == "SS"). resi, j = 1 if feature j was selected on subsample i and equals 0 otherwise. (That is, each row is a selected set.)

Author

Gregory Faletto, Jacob Bien