Multi-collinearity Visualization

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

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
#>       X01 X02 X03 X04 X05 X06 X07 X08 X09 X10
#> tau10 0.5 0.5   0   0   0   0   0   0   0   0