chooseB.intern: Tune the number of iterations for variable selection using...

In clusterMI: Cluster Analysis with Missing Values by Multiple Imputation

Tune the number of iterations for variable selection using varselbest

Description

Tune the number of iterations for variable selection using varselbest

Usage

chooseB.intern(
  res.varselbest,
  linewidth = 1,
  linetype = "dotdash",
  xlab = "B",
  ylab = "Proportion",
  graph = TRUE,
  title = NULL
)

Arguments

res.varselbest	an output from the varselbest function
linewidth	a numerical value setting the widths of lines
linetype	what type of plot should be drawn
xlab	a title for the x axis
ylab	a title for the y axis
graph	a boolean. If FALSE, no graphics are ploted. Default value is TRUE
title	the main title

References

Bar-Hen, A. and Audigier, V. An ensemble learning method for variable selection: application to high dimensional data and missing values. ArXiv e-prints <arXiv:1808.06952>

clusterMI documentation built on April 4, 2025, 12:55 a.m.