R/dshm_spatial_correlation.R

In FilippoFranchini/dshm: Density surface modelling unsing Hurdle approach for zero inflation

#' Calculating spatial correlation for Hurdle models.
#'
#' \code{dshm_spatial_correlation} calculates spatial correlation for fitted Hurdle models.
#'
#' @param coord Centroids of each segment as defined in \code{\link[sp]{coordinates}}.
#' @param z Model residuals.
#' @param xlab Label for x-axis.
#' @param ylab Label for y-axis.
#' @param lims A vector of minimum and maximum correlation values.
#' @details For more information about fitting Hurdle models, plotting model predictions and much more you can download the \href{http://github.com/FilippoFranchini/dshm/blob/master/vignettes}{fitting_Hurldle.pdf} tutorial.
#' @return Plot for correlation coefficient vs. distance and percentage of data within \code{lims}.
#' @author Filippo Franchini \email{filippo.franchini@@outlook.com}
#' @export
#'
dshm_spatial_correlation<-function(coord,z,xlab,ylab,lims){
  data<-data.frame(x=coord[,1],y=coord[,2],z=z)
  surf<-spatial::surf.ls(6,stats::na.omit(data))
  correl<-spatial::correlogram(krig = surf,nint = length(data[,1]),plotit=FALSE)[]
  graphics::plot(correl$x/1000,correl$y,ylim=c(-1,1),type="b",xlab=xlab,ylab=ylab,pch=19,col=grDevices::rgb(0,0,0,0.5))
  graphics::abline(h=0)

  info<-length(subset(correl$y,correl$y>=lims[1] & correl$y<=lims[2]))/length(correl$y)*100 #proportion of correlation within -0.1 and 0.1
  cat(paste(round(info,1),"% of the data is within ",lims[1]," and ",lims[2],"."))
}