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#' 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.fi} 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.fi
#' @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 defined 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 divisions number of divisions for averaging line values; default = 25
#' @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 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.fi <- 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, ...){
# Functions used: process.data, predict(predict.io.fi),
# plot.uncond, plot.cond
# Retrieve values from model object
model <- x
xmat <- model$data
xmat$offsetvalue <- 0
cond.det <- predict(model,newdata=xmat,integrate=FALSE)
fitted <- cond.det$fitted
p1 <- cond.det$p1
p2 <- cond.det$p2
width <- model$meta.data$width
left <- model$meta.data$left
# If number of classes for histogram intervals was not set compute
# a reasonable default
if(is.null(nc)){
nc <- round(sqrt(length(xmat$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, 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), 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$data, model$meta.data)$xmat
data$offsetvalue <- 0
if(is.element(3, which)){
gxvalues <- p1[xmat$detected[xmat$observer==2] == 1 &
xmat$distance[xmat$observer==2] >= left &
xmat$distance[xmat$observer==2] <= width]
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)
}
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