R/ellipsoidfit2.R

In luismurao/ntbox: From Getting Biodiversity Data to Evaluating Species Distribution Models in a Friendly GUI Environment

#' ellipsoidfit2: function to fit an ellipsoid model
#' @description Function to fit an ellipsoid model using the shape matrix (covariance matrix)
#' of the niche variables. This is an optimized version of the \code{\link[ntbox]{ellipsoidfit}} function; the diffrence is that it does not give the table of distances to niche centroid.
#' @param envlayers A RasterStack or RasterBrick object of the niche variables.
#' @param centroid A vector with the values of the centers of the ellipsoid (see \code{\link[ntbox]{cov_center}}).
#' @param covar The shape matrix (covariance) of the ellipoid (see \code{\link[ntbox]{cov_center}}).
#' @param level The proportion of points  to be included inside the ellipsoid
#' @param plot Logical If True a plot of the niche will be shown.
#' @param size The size of the points of the niche plot.
#' @param xlab1 For x label for 2-dimensional histogram
#' @param ylab1 For y label for 2-dimensional histogram
#' @param zlab1 For z label for 2-dimensional histogram
#' @param ... Arguments passed to \code{\link[rgl]{plot3d}} function from rgl
#' @return Returns a list containing a data.frame with the suitability values; a suitability raster;
#' a data.frame with the mahalanobis and euclidean distances to the centroid.
#' @export
#' @examples
#' ## Load niche data
#' \dontrun{
#' d_cardon <-  read.csv(system.file("extdata", "cardon_virtual.csv", package = "ntbox"))
#' ## Compute the centroid and shape (covariance matrix) of the ellipsoid model.
#' covar_centroid <- cov_center(d_cardon,mve=TRUE,level=0.99,vars=c(3,4,5))
#' ## RasterStack with the niche variables
#' nicheStack <- raster::stack(list.files(system.file("extdata",
#'                                        package = "ntbox"),
#'                                        pattern = ".asc$",
#'                                        full.names = TRUE))
#' # Fitting the ellipsoid model
#'  ellipsoidMod <- ellipsoidfit2(envlayers = nicheStack,
#'                                centroid = covar_centroid$centroid,
#'                                covar = covar_centroid$covariance,
#'                                level=0.99,
#'                                plot=TRUE,
#'                                size=3)
#'  raster::plot(ellipsoidMod)
#' }


ellipsoidfit2 <- function(envlayers,centroid,covar,level=0.95,
                         plot=T,size,
                         xlab1="niche var 1",ylab1= "niche var 2",zlab1="S",...){

  if(methods::is(envlayers, "RasterStack") || methods::is(envlayers, "RasterBrick")){
    ncells <- raster::ncell(envlayers[[1]])
    nonaids <- which(!is.na(envlayers[[1]][]))

    env_vars <- 1: raster::nlayers(envlayers) %>% purrr::map_dfc(function(x){
      capa_val <- envlayers[[x]]
      nombre_capa <- names(capa_val)
      capa_val <- capa_val[]
      capa_val <- capa_val[nonaids]
      df <- data.frame(capa_val)
      names(df) <- nombre_capa
      return(df)
    })
    env_vars <- as.matrix(env_vars)
    suitRaster <- envlayers[[1]]
    names(suitRaster) <- "Suitability"
    suitVals <- rep(NA,raster::ncell(suitRaster))

  }
  else{
    stop("envlayers should be of class 'RasterStack' or 'RasterBrick'")
  }
  # Calculating distance to the centroid
  mahalanobisD <- stats::mahalanobis(env_vars,
                                     center = centroid,
                                     cov = covar)

  suit <- function( mahalanobisD){
    a <- 1
    expo <- exp(-0.5* mahalanobisD)
    return(a*expo)
  }
  # Computing the suitabilities
  suits <- suit( mahalanobisD)
  suitVals[nonaids] <- suits
  suitRaster[] <- suitVals

  if(dim(env_vars)[2]==2 && plot==TRUE){

    x <- seq(from = centroid[1]/2,to =centroid[1]*2 ,length=100)
    x <- sort(x)
    y <- seq(from = centroid[2]/2,to =centroid[2]*2 ,length=100)
    y <- sort(y)

    suit1 <- function(x,y) {
      maha1 <- stats::mahalanobis(cbind(x,y),
                                  center = centroid,
                                  cov = covar)
      expo <- exp(-0.5* maha1)
      return(expo)
    }
    #z <- x %o% y
    z <- outer(x,y,FUN = suit1)

    p1 <- graphics::persp(x,y,z, box=T,xlab=xlab1,
                          ylab=ylab1,zlab=zlab1, col="blue",
                          theta = 55, phi = 30,r = 40,
                          d = 0.1, expand = 0.5,
                          ticktype = "detailed", nticks=5,
                          cex.lab=1.5, cex.axis=1.3,
                          cex.main=1.5, cex.sub=1.5)


    ranges <- t(sapply(list(x,y,z),range))
    means <- rowMeans(ranges)

    ## label offset distance, as a fraction of the plot width
    labelspace <- 0.12  ## tweak this until you like the result

    xpos <- min(x)-(diff(range(x)))*labelspace
    ypos <- min(y)-(diff(range(y)))*labelspace
    labelbot3d <- c(xpos,ypos,min(z))
    labeltop3d <- c(xpos,ypos,max(z))
    labelmid3d <- c(xpos,ypos,mean(range(z)))

    trans3dfun <- function(v) { grDevices::trans3d(v[1],v[2],v[3],p1) }
    labelbot2d <- trans3dfun(labelbot3d)
    labelmid2d <- trans3dfun(labelmid3d)
    labeltop2d <- trans3dfun(labeltop3d)
    labelang <- 180/pi*atan2(labeltop2d$y-labelbot2d$y,labeltop2d$x-labelbot2d$x)
    graphics::par(xpd=NA,srt=labelang)  ## disable clipping and set string rotation
    graphics::text(labelmid2d[1]$x,labelmid2d$y,zlab1,cex=1.5)


  }

  if(dim(env_vars)[2]==3 && plot==TRUE){

    data1 <- env_vars
    dfd <- dim(data1)[1] - 1
    dfn <- dim(data1)[2] - 1
    # Ellipsoid radius
    #ell.radius_E <- sqrt(dfn * qf(level, dfn, dfd))
    suits2 <- suits

    ellips_E  <- rgl::ellipse3d(covar,centre = centroid,level = 0.99)


    if(dfd > 50000)
      np <- 50000
    else
      np <- dim(data1)[1]

    toSam <- sample(1:length(data1[,1]),np)
    data1 <- data1[toSam,]

    rgl::plot3d(data1,size = size,col=grDevices::hsv(suits2[toSam]*.71,.95,.9),...)
    rgl::wire3d(ellips_E, col=4, lit=FALSE,alpha=.1)
  }

  return(suitRaster)


}