Calculate each covariate's smallest variable selection threshold

There are two methods of variable selection for covariates. The first is the Variable Inclusion Probability (VIP) introduced by Bach (2008) and generalized by Bunea et al (2011). The second is the Quantile confidence interval (QNT) proposed by Abram et al (2016). For a given level of significance alpha, each method selects covariates for the given threshold = 1 - alpha. The higher the threshold (lower alpha), the more stringent the variable selection criterion.

Usage

selection_thresholds(object, grid = seq(0, 1, by = 0.01), ...)

Arguments

object: An object of class bolasso or bolasso_fast.
grid: A vector of numbers between 0 and 1 (inclusive) specifying the grid of threshold values to calculate variable inclusion criterion at. Defaults to seq(0, 1, by = 0.01).
...: Additional parameters to pass to coef on objects of class bolasso and bolasso_fast.

Value

A tibble with dimension (2*p)x5 where p is the number of covariates.

Details

This function returns a tibble that, for each covariate, returns the largest threshold (equivalently smallest alpha) at which it would be selected for both the VIP and the QNT methods. Consequently the number of rows in the returned tibble is 2*p where p is the number of covariates included in the model.