NCovered.io <- function(par,model,data,group=TRUE,...){
# see docs in NCovered
#
# computes abundance in covered region for io model object
#
# par - parameter values (used when computing derivatives wrt
# parameter uncertainty)
# model - ddf model object
# group - if TRUE computes group abundance and if FALSE individual abundance
#
# result - abundance estimate
#
# Functions Used: predict (predict.io), compute.Nht
# set pars if we are differentiating
# then extract fitted values
if(!is.null(par)){
model$mr$mr$coefficients <- par[seq_along(model$mr$mr$coefficients)]
model$ds$par <- par[(length(model$mr$mr$coefficients)+1):length(par)]
model$ds$ds$aux$ddfobj <- assign.par(model$ds$ds$aux$ddfobj,model$ds$par)
fitted <- predict(model, compute=TRUE)$fitted
}else{
fitted <- model$fitted
}
if(!group){
size <- model$data$size[model$data$observer==1&model$data$object %in%
as.numeric(names(model$fitted))]
Nhat <- sum(compute.Nht(fitted,FALSE,size))
}else{
Nhat <- sum(compute.Nht(fitted,TRUE,size=NULL))
}
return(Nhat)
}
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