plot.mcvis: Multi-collinearity Visualization plots

In mcvis: Multi-Collinearity Visualization

Description

Multi-collinearity Visualization plots

Multi-collinearity Visualization plots

Multi-collinearity Visualization plots

Usage

alt_mcvis(mcvis_result, eig_max = 1L, var_max = ncol(mcvis_result$MC))

ggplot_mcvis(
  mcvis_result,
  eig_max = 1L,
  var_max = ncol(mcvis_result$MC),
  label_dodge = FALSE
)

igraph_mcvis(mcvis_result, eig_max = 1L, var_max = ncol(mcvis_result$MC))

## S3 method for class 'mcvis'
plot(
  x,
  type = c("ggplot", "igraph", "alt"),
  eig_max = 1L,
  var_max = ncol(x$MC),
  label_dodge = FALSE,
  ...
)

Arguments

mcvis_result	Output of the mcvis function
eig_max	The maximum number of eigenvalues to be displayed on the plot.
var_max	The maximum number of variables to be displayed on the plot.
label_dodge	If variable names are too long, it might be helpful to dodge the labelling. Default to FALSE.
x	Output of the mcvis function
type	Plotting mcvis result using "igraph" or "ggplot". Default to "ggplot".
...	additional arguments (currently unused)

Value

A mcvis visualization plot

Author(s)

Chen Lin, Kevin Wang, Samuel Mueller

Examples

set.seed(1)
p = 10
n = 100
X = matrix(rnorm(n*p), ncol = p)
X[,1] = X[,2] + rnorm(n, 0, 0.1)
mcvis_result = mcvis(X)
plot(mcvis_result)
plot(mcvis_result, type = "igraph")
plot(mcvis_result, type = "alt")

mcvis documentation built on July 30, 2021, 9:09 a.m.