Helper function run on each subsample

Runs provided feature selection method fitfun on each subsample for cluster stability selection (this function is called within mclapply).

Usage

cssLoop(input, x, y, lambda, fitfun)

Arguments

input

Could be one of two things: 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" and the same as 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.)

x

an n x p numeric matrix containing the predictors. (This should be the full design matrix provided to css.)

y

A response; 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. This should be the full response provided to css.

lambda

A tuning parameter or set of tuning parameters that may be used by the feature selection method. For example, in the default case when fitfun = cssLasso, lambda is a numeric: the penalty to use for each lasso fit.

fitfun

A function that takes in arguments X, y, and lambda and returns a vector of indices of the columns of X (selected features).

Value

An integer vector; the indices of the features selected by fitfun.

Author

Gregory Faletto, Jacob Bien