plot.ssvs_mi: Plot SSVS-MI Estimates and Marginal Inclusion Probabilities...

In sabainter/SSVS: Functions for Stochastic Search Variable Selection (SSVS)

Plot SSVS-MI Estimates and Marginal Inclusion Probabilities (MIP)

Description

This function creates a plot of SSVS-MI estimates with minimum and maximum and a plot for marginal inclusion probabilities (MIP) optional thresholds for highlighting significant predictors..

Usage

## S3 method for class 'ssvs_mi'
plot(
  x,
  type = "both",
  threshold = 0.5,
  legend = TRUE,
  est_title = NULL,
  mip_title = NULL,
  color = TRUE,
  ...
)

Arguments

x	An ssvs result object obtained from ssvs_mi()
type	Defaults to "both", can change to "estimate" or "MIP".
threshold	A numeric value (between 0 and 1) specifying the MIP threshold to highlight significant predictors. Defaults to 0.5.
legend	Logical indicating whether to include a legend for the threshold. Defaults to TRUE.
est_title	A character string specifying the plot title. Defaults to "SSVS-MI estimates".
mip_title	A character string specifying the plot title. Defaults to "Multiple Inclusion Probability for SSVS-MI".
color	Logical indicating whether to use color to highlight thresholds. Defaults to TRUE.
...	Ignored

Value

Two ggplot2 objects representing the plot of SSVS estimates and the plot of MIP with thresholds.

Examples

data(imputed_mtcars)
outcome <- 'qsec'
predictors <- c('cyl', 'disp', 'hp', 'drat', 'wt', 'vs', 'am', 'gear', 'carb','mpg')
imputation <- '.imp'
results <- ssvs_mi(data = imputed_mtcars, y = outcome, x = predictors, imp = imputation)
plot(results)

sabainter/SSVS documentation built on April 17, 2025, 12:48 p.m.