VIF.subset: Reduce the Climate Data based on Variance Inflation Factor...

In AlleleShift: Predict and Visualize Population-Level Changes in Allele Frequencies in Response to Climate Change

Reduce the Climate Data based on Variance Inflation Factor Analysis.

Description

Through Variance Inflation Factor (VIF) analysis, a subset of variables is indentified where all variables have VIF below a predefined threshold.

Usage

VIF.subset(data, VIF.max, keep=NULL, silent=FALSE, cor.plot=TRUE)

Arguments

data	(Bio)-Climatic or environmental descriptors of the populations
VIF.max	Maximum Variance Inflation Factor as in ensemble.VIF.dataframe that is used internally.
keep	Variables to keep as in as in ensemble.VIF.dataframe.
silent	Limit the verbose output as in ensemble.VIF.dataframe.
cor.plot	Generate a correlation matrix for the final subset via ggcorr.

Value

The function returns information on a subset of variables where VIF is below a pre-defined threshold.

Author(s)

Roeland Kindt (World Agroforestry, CIFOR-ICRAF)

Examples

data(Poptri.baseline.env)

# error as many variables are highly correlated
# VIF.subset(Poptri.baseline.env)

VIF.subset(Poptri.baseline.env, keep=c("MAT", "CMI"), cor.plot=TRUE)

AlleleShift documentation built on Nov. 2, 2023, 6:08 p.m.