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#' Summarize a ds.mixture object
#'
#' Summarize a ds.mixture object. The function provides information on parameter
#' estimates, estimates of the abundance in the covered area and the average
#' detectability and their respective standard errors and coefficients of
#' variation.
#'
#' @aliases summary.ds.mixture
#' @method summary ds.mixture
#' @S3method summary ds.mixture
#' @export
#'
#' @param object A fitted mixture model detection function object.
#' @param ... Anything, but it will be ignored.
#' @return a summary of a \code{\link{ds.mixture}} object.
#'
#' @references
#' Miller, D.L. and L. Thomas (in prep.). Mixture model distance sampling detection functions.
#'
#' @author David L. Miller
#' @examples
#' library(mmds)
#' set.seed(0)
#' ## simulate some line transect data from a 2 point mixture
#' sim.dat<-sim.mix(c(-0.223,-1.897,inv.reparam.pi(0.3)),2,100,1)
#' ## fit the model
#' fit.sim.dat<-fitmix(sim.dat,1,2)
#' ## what happened?
#' summary(fit.sim.dat)
#'
summary.ds.mixture<-function(object,...){
# See print.summary.ds for how this gets printed
model<-object
ans <- list()
# Number of observations
ans$n <- length(model$distance)
# number of mixture components
ans$mix.terms<-model$mix.terms
ans$coeff<-list()
# Parameter estimates and their standard errors
ans$coeff$pars<-data.frame(estimate=model$pars,se=model$pars.se)
# separate mixture proportions
gp<-getpars(model$pars,model$mix.terms,model$zdim,model$z)
ans$coeff$mix.prop<-gp$mix.prop
names(ans$coeff$mix.prop)<-paste("pi_",1:model$mix.terms,sep="")
# AIC
ans$aic <- model$aic
# Truncation distance
ans$width <- model$width
# point or line transects?
if(model$pt){
ans$ttype<-"point"
}else{
ans$ttype<-"line"
}
# average p
ans$average.p<-model$pa
ans$average.p.se<-model$pa.se
ans$average.p.cv<-model$pa.se/model$pa
# abundance
ans$Nhat <- model$N
ans$Nhat.se <- model$N.se
ans$Nhat.cv <- model$N.se/model$N
# set the class and return
class(ans) <- "summary.ds.mixture"
return(ans)
}
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