Package 'mcvis'

Title: Multi-Collinearity Visualization
Description: Visualize the relationship between linear regression variables and causes of multi-collinearity. Implements the method in Lin et. al. (2020) <doi:10.1080/10618600.2020.1779729>.
Authors: Kevin Wang [aut, cre], Chen Lin [aut], Samuel Mueller [aut]
Maintainer: Kevin Wang <[email protected]>
License: GPL-3
Version: 1.0.8
Built: 2025-02-06 04:42:29 UTC
Source: https://github.com/kevinwang09/mcvis

Help Index

Multi-collinearity Visualization plots

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")

Multi-collinearity Visualization

Description

Multi-collinearity Visualization

Usage

mcvis(
  X,
  sampling_method = "bootstrap",
  standardise_method = "studentise",
  times = 1000L,
  k = 10L
)

Arguments

X

A matrix of regressors (without intercept terms).
sampling_method

The resampling method for the data. Currently supports 'bootstrap' or 'cv' (cross-validation).
standardise_method

The standardisation method for the data. Currently supports 'euclidean' (default, centered by mean and divide by Euclidiean length) and 'studentise' (centred by mean and divide by standard deviation)
times

Number of resampling runs we perform. Default is set to 1000.
k

Number of partitions in averaging the MC-index. Default is set to 10.

Value

A list of outputs:

  • t_square:The t^2 statistics for the regression between the VIFs and the tau's.

  • MC:The MC-indices

  • col_names:Column names (export for plotting purposes)

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 = X)
mcvis_result

Shiny app for mcvis exploration

Description

Shiny app for mcvis exploration

Usage

shiny_mcvis(mcvis_result, X)

Arguments

mcvis_result

Output of the mcvis function
X

The original X matrix

Value

A shiny app allowing for interactive exploration of mcvis results

Author(s)

Chen Lin, Kevin Wang, Samuel Mueller

Examples

if(interactive()){
set.seed(1)
p = 10
n = 100
X = matrix(rnorm(n*p), ncol = p)
mcvis_result = mcvis(X)
shiny_mcvis(mcvis_result = mcvis_result, X = X)
}