Variable selection

[rvosa]

Variable selection

[rvosa]

De gehanteerde method is nu hier geimplementeerd:

trait-geo-diverse-ungulates/script/MaxEnt_function.R

Lines 2 to 55 in f2df7fc

	removeCollinearity_adjusted <- function(raster.stack, multicollinearity.cutoff = .7,
	select.variables = FALSE, sample.points = FALSE,
	nb.points = 10000, plot = FALSE)
	{
	env.df <- getValues(raster.stack)
	env.df <- env.df[-unique(which(is.na(env.df), arr.ind = T)[, 1]), ] # Removing NAs
	# Correlation matrix creation
	cor.matrix <- matrix(data = 0,
	nrow = nlayers(raster.stack),
	ncol = nlayers(raster.stack),
	dimnames = list(names(raster.stack), names(raster.stack)))
	# Correlation based on Pearson
	cor.matrix<-1 - abs(stats::cor(env.df, method = "pearson" ))
	cor.matrix[is.na(cor.matrix)]<- 0
	# Transforming the correlation matrix into an ascendent hierarchical classification
	dist.matrix <- stats::as.dist(cor.matrix)
	ahc <- stats::hclust(dist.matrix, method = "complete")
	groups <- stats::cutree(ahc, h = 1 - multicollinearity.cutoff)
	if(length(groups) == max(groups))
	{
	message(paste(" - No multicollinearity detected in your data at threshold ", multicollinearity.cutoff, "\n", sep = ""))
	mc <- FALSE
	} else
	{ mc <- TRUE }
	# Random selection of variables
	if(select.variables)
	{
	sel.vars <- NULL
	for (i in 1:max(groups))
	{
	sel.vars <- c(sel.vars, sample(names(groups[groups == i]), 1))
	}
	} else
	{
	if(mc)
	{
	sel.vars <- list()
	for (i in groups)
	{
	sel.vars[[i]] <- names(groups)[groups == i]
	}
	} else
	{
	sel.vars <- names(raster.stack)
	}
	}
	return(sel.vars)
	}