selection_thresholds {bolasso} | R Documentation |
Calculate each covariate's smallest variable selection threshold
Description
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 |
grid |
A vector of numbers between 0 and 1 (inclusive) specifying
the grid of threshold values to calculate variable inclusion criterion
at. Defaults to |
... |
Additional parameters to pass to |
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.
Value
A tibble with dimension (2*p)x5 where p is the number of covariates.