plot.selection: Plots to visualize the post-screening selection

In SMLE: Joint Feature Screening via Sparse MLE

Plots to visualize the post-screening selection

Description

This function constructs a sparsity vs. selection criterion curve for a 'selection' object. When EBIC is used with voting, it also constructs a histogram showing the voting result.

Usage

## S3 method for class 'selection'
plot(x, ...)

Arguments

x	A 'selection' object as the output from smle_select().
...	Additional arguments to the plot() function.

Value

No return value.

Examples

set.seed(1)
Data <- Gen_Data(correlation = "MA", family = "gaussian")
fit <- SMLE(Y = Data$Y, X = Data$X, k = 20, family = "gaussian")
fit_s <- smle_select(fit, vote = TRUE)
plot(fit_s)

SMLE documentation built on Jan. 22, 2023, 1:55 a.m.