spatialEco (version 1.3-5)

collinear: Collinearity test

Description

Test for linear or nonlinear collinearity/correlation in data

Test for linear or nonlinear collinearity/correlation in data

Usage

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

Messages and a vector of correlated variables

Details

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.

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. If nonlinear = TRUE, pairwise nonlinear correlations are evaluated by fitting y as a semi-parametrically estimated function of x using a generalized additive model and testing whether or not that functional estimate is constant, which would indicate no relationship between y and x thus, avoiding potentially arbitrary decisions regarding the order in a polynomial regression.

Examples

# NOT RUN {
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 ) 		       

data(cor.data)

# Evaluate linear correlations on linear dataCollinearity between
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 ) 		       

# }