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#' Plot fit of detection functions and histograms of data from distance
#' sampling independent observer (\code{io}) 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.io.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.
#'
#' @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 secondary unconditional detection function \cr
#' 3 \tab Plot pooled unconditional detection function \cr
#' 4 \tab Plot duplicate unconditional detection function \cr
#' 5 \tab Plot primary conditional detection function\cr
#' 6 \tab Plot secondary conditional detection function \cr}
#' Note that the order of which is ignored and plots are produced in the above
#' order.
#' @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 TRUE plots predicted value for each
#' observation
#' @param showlines logical variable; if 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 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 Just plots
#' @author Jeff Laake, Jon Bishop, David Borchers, David L Miller
#' @keywords plot
#' @examples
#' \donttest{
#' library(mrds)
#' data(book.tee.data)
#' egdata <- book.tee.data$book.tee.dataframe
#' result.io <- ddf(dsmodel=~cds(key = "hn"), mrmodel=~glm(~distance),
#' data=egdata, method="io", meta.data=list(width=4))
#'
#' # just plot everything
#' plot(result.io)
#'
#' # Plot primary and secondary unconditional detection functions on one page
#' # and primary and secondary conditional detection functions on another
#' plot(result.io,which=c(1,2,5,6),pages=2)
#' }
plot.io <- function(x, which=1:6, breaks=NULL, nc=NULL, maintitle="",
showlines=TRUE, showpoints=TRUE, ylim=c(0, 1), angle=NULL,
density=NULL, col="lightgrey", jitter=NULL, divisions=25,
pages=0, xlab="Distance", ylab="Detection probability",
subtitle=TRUE, ...){
model <- x
# since we can't get the same ordering as which is, make sure it's the same
# every time, at least
which <- sort(which)
# Retrieve values from model object
xmat.p0 <- model$mr$mr$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$mr$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$p2
# list of values to pass as gxvalues for unconditional plots
# in order as below
# gxvalues are the points shown in the plots
gxlist <- list(p1/delta,
p2/delta,
(p1+p2-p1*p2)/delta,
p1*p2/delta)
# If number of classes for histogram intervals was not set compute
# a reasonable default
if(is.null(nc)){
nc<-round(sqrt(min(length(xmat$distance[xmat$observer==1&xmat$detected==1]),
length(xmat$distance[xmat$observer==2&xmat$detected==1]),
length(xmat$distance[xmat$observer==1 &
xmat$timesdetected==2]) )),0)
}
# 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))
# loop over the unconditional plots
# 1 - Plot primary unconditional detection function
# 2 - Plot secondary unconditional detection function
# 3 - Plot pooled unconditional detection function
# 4 - Plot duplicate unconditional detection function
for(wh in which[which<5]){
plot_uncond(model, wh, xmat, gxvalues=gxlist[[wh]], 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,...)
}
# 5 - Plot conditional detection function (1|2)
data <- model$mr$mr$data
data$offsetvalue <- 0
if(is.element(5,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,...)
}
# 6 - Plot secondary conditional detection function (2|1)
if(is.element(6,which)){
gxvalues <- p2[xmat$detected[xmat$observer==1]==1]
plot_cond(2,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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