collinear: Collinearity test

In jeffreyevans/rfUtilities: Random Forests Model Selection and Performance Evaluation

Collinearity test

Description

Test for linear or nonlinear collinearity/correlation in data

Usage

collinear(x, p = 0.85, nonlinear = FALSE, p.value = 0.001)

Arguments

x	A data.frame or matrix containing continuous data
p	The correlation cutoff (default is 0.85)
nonlinear	A boolean flag for calculating nonlinear correlations (FALSE/TRUE)
p.value	If nonlinear is TRUE, the p value to accept as the significance of the correlation

Value

Messages and a vector of correlated variables

Note

Evaluation of the pairwise linear correlated variables to remove is accomplished through calculating the mean correlations of each variable and selecting the variable with higher mean.

Nonlinear correlations assume the model form: E(Y_i | X_i) = \alpha + f(X_i) + \varepsilon_i With the hypothesis: H_{0} : f(x) = 0, \ \forall x$$

Author(s)

Jeffrey S. Evans <jeffrey_evans<at>tnc.org>

Examples

data(cor.data)

# Evaluate linear correlations on linear data
head( dat <- cor.data[[4]] ) 
pairs(dat, pch=20)
  ( cor.vars <- collinear( dat ) )

# Remove identified variable(s)
head( dat[,-which(names(dat) %in% cor.vars)] )

# Evaluate linear correlations on nonlinear data
#   using nonlinear correlation function
plot(cor.data[[1]], pch=20) 
  collinear(cor.data[[1]], p=0.80, nonlinear = TRUE ) 		       

jeffreyevans/rfUtilities documentation built on Nov. 12, 2023, 6:52 p.m.