Identifies covariates that are selected by the Bolasso algorithm at the user-defined threshold. There are two variable selection criterion to choose between; Variable Inclusion Probability ("vip") introduced in the original Bolasso paper (Bach, 2008) and further developed by Bunea et al. (2011), and the Quantile ("qnt") approach proposed by Abram et al. (2016). The desired threshold value is 1 - alpha, where alpha is some (typically small) significance level.
Usage
selected_variables(
object,
threshold = 0.95,
method = c("vip", "qnt"),
var_names_only = FALSE,
...
)Arguments
- object
An object of class bolasso.
- threshold
A numeric between 0 and 1, specifying the variable selection threshold to use.
- method
The variable selection method to use. The two valid options are
c("vip", "qnt"). The default"vip"and is the method described in the original Bach (2008) and complementary Bunea et al. (2011) works. The"qnt"method is the method proposed by Abram et al. (2016).- var_names_only
A boolean value. When
var_names_only = FALSE(the default value) this function will return a tibble::tibble of selected covariates and their corresponding coefficients across all bootstrap replicates. Whenvar_names_only == TRUE, it will return a vector containing all selected covariate names.- ...
Additional arguments to pass to
coefon objects with class bolasso orbolass_fast.
Value
A tibble with each selected variable and its respective coefficient for each bootstrap replicate OR a vector of the names of all selected variables.
Details
This function returns either a tibble::tibble of selected covariates and their corresponding coefficients across all bootstrap replicates, or a vector of selected covariate names.
See also
glmnet::coef.glmnet() and gamlr:::coef.gamlr for details
on additional arguments to pass to ....