| Type: | Package |
| Title: | Multi-Collinearity Visualization |
| Version: | 1.0.8 |
| 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>. |
| Encoding: | UTF-8 |
| Imports: | assertthat, igraph, ggplot2, purrr, magrittr, reshape2, shiny, dplyr, psych, rlang |
| RoxygenNote: | 7.1.1.9001 |
| License: | GPL-3 |
| Suggests: | testthat (≥ 2.1.0), covr, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| NeedsCompilation: | no |
| Packaged: | 2021-07-30 02:56:36 UTC; kevinwang |
| Author: | Kevin Wang [aut, cre], Chen Lin [aut], Samuel Mueller [aut] |
| Maintainer: | Kevin Wang <kevin.wang09@gmail.com> |
| Repository: | CRAN |
| Date/Publication: | 2021-07-30 08:20:05 UTC |
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)
}