# collinear

0th

Percentile

##### Collinearity test

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

##### 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.

##### Value

Messages and a vector of correlated variables

• collinear
##### 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 )

# }

Documentation reproduced from package spatialEco, version 1.3-2, License: GPL-3

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