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#' Print summary of distance detection function model object
#'
#' Provides a brief summary of a distance sampling analysis. Including:
#' detection function parameters, model selection criterion, and optionally
#' abundance in the covered (sampled) region and its standard error.
#'
#' @aliases print.summary.dsmodel
#' @param x a summary of distance sampling analysis
#' @param \dots unspecified and unused arguments for S3 consistency
#' @return Nothing, just prints the summary.
#' @author David L. Miller and Jeff Laake
#' @seealso [`summary.ds`][summary.ds]
#' @keywords utility
#' @export
print.summary.dsmodel <- function (x,...){
# split up the object
dht.obj <- x$dht
model <- x$ddf
x <- x$ds
# routine from dht to print the tables...
# stolen from print.dht, removed vcmatrices and cor arguments
print.tables <- function(x,bysample){
cat("\nSummary statistics:\n")
print(x$summary)
if("N" %in% names(x))
{
cat("\nAbundance:\n")
print(x$N)
}
cat("\nDensity:\n")
print(x$D)
if(bysample)
{
cat("\nEstimates by sample:\n")
print(x$bysample)
}
}
cat("\nSummary for distance analysis \n")
cat("Number of observations : ", x$n, "\n")
cat("Distance range : ", x$left, " - ", x$width, "\n")
cat("\nModel :", model.description(model), "\n")
# Remind the user that monotonicity constraints were enforced
if(x$mono & x$mono.strict){
cat("\nStrict monotonicity constraints were enforced.\n")
}else if(x$mono){
cat("\nMonotonicity constraints were enforced.\n")
}
cat("AIC : ", x$aic, "\n")
cat("Optimisation: ", x$optimise, "\n")
# parameter summaries
cat("\nDetection function parameters\n")
cat("Scale coefficient(s): ", "\n")
print(x$coeff$key.scale)
if(x$key %in% c("gamma","hr")) {
cat("\nShape coefficient(s): ", "\n")
print(x$coeff$key.shape)
}
if(!is.null(x$coeff$adj.parm)) {
cat("\nAdjustment term coefficient(s): ", "\n")
print(x$coeff$adj.parm)
}
cat("\n")
if(!is.null(x$Nhat)){
parameters <- data.frame(Estimate=c(x$average.p,x$Nhat))
row.names(parameters) <- c("Average p", "N in covered region")
if(!is.null(x$average.p.se)){
parameters$SE <- c(x$average.p.se, x$Nhat.se)
parameters$CV <- parameters$SE/parameters$Estimate
}
}else{
parameters <- data.frame(Estimate=c(x$average.p))
row.names(parameters) <- c("Average p")
if(!is.null(x$average.p.se)){
parameters$SE <- c(x$average.p.se)
parameters$CV <- parameters$SE/parameters$Estimate
}
}
print(parameters)
## dht stuff
x <- dht.obj
if(!is.null(x)){
bysample <- FALSE # for now!
if(is.null(x$clusters)){
print.tables(x$individuals,bysample)
}else{
cat("\nSummary for clusters\n")
print.tables(x$clusters,bysample)
cat("\nSummary for individuals\n")
print.tables(x$individuals,bysample)
cat("\nExpected cluster size\n")
S <- x$Expected.S
if(!is.null(S$se.Expected.S)){
S$cv.Expected.S <- S$se.Expected.S/S$Expected.S
S$cv.Expected.S[S$Expected.S==0] <- 0
}
print(S)
}
}
invisible()
}
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