R/plot.rem.R

In DistanceDevelopment/mrds: Mark-Recapture Distance Sampling

#' Plot fit of detection functions and histograms of data from removal distance
#' sampling model
#'
#' Plots the fitted detection functions for a distance sampling model and
#' histograms of the distances (for unconditional detection functions) or
#' proportion of observations detected within distance intervals (for
#' conditional detection functions) to compare visually the fitted model and
#' data.
#'
#' The structure of the histogram can be controlled by the user-defined
#' arguments \code{nc} or \code{breaks}.  The observation specific detection
#' probabilities along with the line representing the fitted average detection
#' probability.
#'
#' It is not intended for the user to call \code{plot.rem} but its arguments
#' are documented here. Instead the generic \code{plot} command should be used
#' and it will call the appropriate function based on the class of the
#' \code{ddf} object.
#'
#' @aliases plot.rem
#' @export
#' @param x fitted model from \code{ddf}
#' @param which index to specify which plots should be produced.
#'  \tabular{ll}{1 \tab Plot primary unconditional detection function \cr
#'               2 \tab Plot pooled unconditional detection function \cr
#'               3 \tab Plot conditional (1|2) detection function \cr}
#' @param breaks user define breakpoints
#' @param nc number of equal-width bins for histogram
#' @param maintitle main title line for each plot
#' @param showpoints logical variable; if \code{TRUE} plots predicted value for
#' each observation
#' @param showlines logical variable; if \code{TRUE} a line representing the
#' average detection probability is plotted
#' @param ylim range of vertical axis; defaults to (0,1)
#' @param angle shading angle for histogram bars.
#' @param density shading density for histogram bars.
#' @param col colour for histogram bars.
#' @param jitter scaling option for plotting points.  Jitter is applied to
#'   points by multiplying the fitted value by a random draw from a normal
#'   distribution with mean 1 and sd jitter.
#' @param pages the number of pages over which to spread the plots. For
#'  example, if \code{pages=1} then all plots will be displayed on one page.
#'  Default is 0, which prompts the user for the next plot to be displayed.
#' @param divisions number of divisions for averaging line values; default = 25
#' @param xlab label for x-axis
#' @param ylab label for y-axis
#' @param subtitle if \code{TRUE}, shows plot type as sub-title
#' @param \dots other graphical parameters, passed to the plotting functions
#'   (\code{plot}, \code{hist}, \code{lines}, \code{points}, etc)
#' @return NULL
#' @author Jeff Laake, Jon Bishop, David Borchers, David L Miller
#' @keywords plot
plot.rem <- function(x, which=1:3, breaks=NULL, nc=NULL, maintitle="",
                     showlines=TRUE, showpoints=TRUE, ylim=c(0, 1), angle=NULL,
                     density=NULL, col="lightgrey", jitter=NULL, divisions=25,
                     pages=0,
                     xlab="Distance", ylab="Detection probability",
                     subtitle=TRUE, ...){

  model <- x

  # Retrieve values from model object
  xmat.p0 <- model$data
  xmat.p0$offsetvalue <- 0
  xmat.p0$distance <- 0
  ddfobj <- model$ds$ds$aux$ddfobj
  if(ddfobj$type=="gamma"){
    xmat.p0$distance <- rep(apex.gamma(ddfobj), 2)
  }
  p0 <- predict(model$mr, newdata=xmat.p0, integrate=FALSE)$fitted
  xmat <- model$mr$data
  cond.det <- predict(model$mr, newdata=xmat, integrate=FALSE)
  width <- model$meta.data$width
  left <- model$meta.data$left
  detfct.pooled.values <- detfct(xmat$distance[xmat$observer==1], ddfobj,
                                 width=width-left)
  delta <- cond.det$fitted/(p0*detfct.pooled.values)
  p1 <- cond.det$p1
  p2 <- cond.det$p2

  # If number of classes for histogram intervals was not set compute
  # a reasonable default
  if(is.null(nc)){
    nc <- round(sqrt(length(xmat$distance[xmat$observer==2&xmat$detected==1])))
  }

  # Set up default break points unless specified
  if(model$meta.data$binned){
    breaks <- model$meta.data$breaks
    nc <- length(breaks)-1
  }else{
    if(is.null(breaks)){
      breaks <- left + ((width-left)/nc)*(0:nc)
    }else{
      nc <- length(breaks)-1
    }
  }

  # do the plotting layout
  oask <- plot_layout(which,pages)
  on.exit(devAskNewPage(oask))

  # Plot primary unconditional detection function
  if(is.element(1, which)){
    plot_uncond(model, 1, xmat, gxvalues=p1/delta, nc,
                finebr=(width/divisions)*(0:divisions),
                breaks, showpoints, showlines, maintitle, ylim,
                angle=angle, density=density,
                col=col, jitter=jitter, xlab=xlab, ylab=ylab, subtitle=subtitle,
                ...)
  }

  # Plot pooled unconditional detection function
  if(is.element(2, which)){
    plot_uncond(model, 3, xmat, gxvalues=(p1+p2*(1-p1))/delta, nc,
                finebr=(width/divisions)*(0:divisions), breaks, showpoints,
                showlines, maintitle, ylim,
                angle=angle, density=density, col=col, jitter=jitter,
                xlab=xlab, ylab=ylab, subtitle=subtitle, ...)
  }

  # Plot conditional detection function
  data <- process.data(model$mr$data,model$meta.data)$xmat
  data$offsetvalue <- 0
  if(is.element(3, which)){
    gxvalues <- p1[xmat$detected[xmat$observer==2]==1]
    plot_cond(1, data, gxvalues, model, nc, breaks,
              finebr=(width/divisions)*(0:divisions), showpoints, showlines,
              maintitle, ylim, angle=angle, density=density, col=col,
              jitter=jitter, xlab=xlab, ylab=ylab, subtitle=subtitle, ...)
  }
  invisible(NULL)
}