plot.smle: Plots to visualize SMLE screening

In SMLE: Joint Feature Screening via Sparse MLE

Plots to visualize SMLE screening

Description

This function returns two plot windows. By default, the first shows 1) the solution path (estimated coefficient by iteration step) for the retained features. By default, the second plot contains 4 plots to assess convergence: 2) log-likelihood, 3) Euclidean distance between the current and the previous coefficient estimates, 4) the number of tries in u-search (see details of SMLE()), and 5) the number of features changed in the current active set.

Usage

## S3 method for class 'smle'
plot(x, num_path = NULL, label = TRUE, which_path = NULL, out_plot = 1, ...)

Arguments

x	A 'smle' object as the output from SMLE().
num_path	The number of top coefficients to be shown. Default is equal to the number of features retained in the model.
label	Logical flag for whether to label each curve with the feature index. Default is TRUE.
which_path	A vector to control which features are shown in addition to the paths for the most significant coefficients.
out_plot	A number from 1 to 5 indicating which plot is to be shown in the separate window; the default for solution path plot is "1". See Description for plot labels 2-5.
...	Additional arguments passed to the second plot.

Value

No return value.

Examples

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

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