ENMTools_removecol.r

removeCollinearity.terra <- function (raster.stack, multicollinearity.cutoff = 0.7, select.variables = FALSE, 
          sample.points = FALSE, nb.points = 10000, plot = FALSE, method = "pearson") 
{
  if (sample.points) {
    if (!is.numeric(nb.points)) {
      stop("nb.points must be a numeric value corresponding to the number of pixels to sample from raster.stack")
    }
    env.df <- sampleRandom(raster.stack, size = nb.points, 
                           na.rm = TRUE)
  }
  else {
    env.df <- readValues(raster.stack)
    if (any(is.na(env.df))) {
      env.df <- env.df[-unique(which(is.na(env.df), arr.ind = T)[, 
                                                                 1]), ]
    }
  }
  if (!is.numeric(multicollinearity.cutoff)) {
    stop("You must provide a numeric cutoff between 0 and 1 in multicollinearity.cutoff")
  }
  else if (multicollinearity.cutoff > 1 | multicollinearity.cutoff < 
           0) {
    stop("You must provide a numeric cutoff between 0 and 1 in multicollinearity.cutoff")
  }
  cor.matrix <- matrix(data = 0, nrow = nlayers(raster.stack), 
                       ncol = nlayers(raster.stack), dimnames = list(names(raster.stack), 
                                                                     names(raster.stack)))
  cor.matrix <- 1 - abs(stats::cor(env.df, method = method))
  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
  }
}
if (plot) {
  op <- par(no.readonly = TRUE)
  graphics::par(mar = c(5.1, 5.1, 4.1, 3.1))
  plot(ahc, hang = -1, xlab = "", ylab = "Distance (1 - Pearson's r)", 
       main = "", las = 1, sub = "", axes = F)
  graphics::axis(2, at = seq(0, 1, length = 6), las = 1)
  if (mc) {
    graphics::title(paste("Groups of intercorrelated variables at cutoff", 
                          multicollinearity.cutoff))
    par(xpd = T)
    rect.hclust(ahc, h = 1 - multicollinearity.cutoff)
  }
  else {
    graphics::title(paste("No intercorrelation among variables at cutoff", 
                          multicollinearity.cutoff))
  }
  par(op)
}
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)
  }