#' Mark-Recapture Distance Sampling (MRDS) Removal - PI
#'
#' Mark-Recapture Distance Sampling (MRDS) Analysis of Removal Observer
#' Configuration and Point Independence
#'
#' MRDS analysis based on point independence involves two separate and
#' independent analyses of the mark-recapture data and the distance sampling
#' data. For the removal observer configuration, the mark-recapture data are
#' analysed with a call to \code{\link{ddf.rem.fi}} (see Laake and Borchers
#' 2004) to fit conditional distance sampling detection functions to estimate
#' p(0), detection probability at distance zero for the primary observer based
#' on independence at zero (eq 6.22 in Laake and Borchers 2004). Independently,
#' the distance data, the observations from the primary observer, are used to
#' fit a conventional distance sampling (CDS) (likelihood eq 6.6) or
#' multi-covariate distance sampling (MCDS) (likelihood eq 6.14) model for the
#' detection function, g(y), such that g(0)=1. The detection function for the
#' primary observer is then created as p(y)=p(0)*g(y) (eq 6.28 of Laake and
#' Borchers 2004) from which predictions are made. \code{ddf.rem} is not called
#' directly by the user and is called from \code{\link{ddf}} with
#' \code{method="rem"}.
#'
#' For a complete description of each of the calling arguments, see
#' \code{\link{ddf}}. The argument \code{data} is the dataframe specified by
#' the argument \code{data} in \code{ddf}. The arguments \code{dsmodel},
#' \code{mrmodel}, \code{control} and \code{meta.data} are defined the same as
#' in \code{ddf}.
#'
#' @export
#' @method ddf rem
#' @param dsmodel distance sampling model specification; model list with key
#' function and scale formula if any
#' @param mrmodel mark-recapture model specification; model list with formula
#' and link
#' @param data analysis dataframe
#' @param method not used
#' @param meta.data list containing settings controlling data structure
#' @param control list containing settings controlling model fitting
#' @param call original function call used to call \code{ddf}
#' @return result: an rem model object which is composed of rem.fi and ds model
#' objects
#' @author Jeff Laake
#' @seealso \code{\link{ddf.rem.fi}}, \code{\link{ddf.ds}}
#' @references Laake, J.L. and D.L. Borchers. 2004. Methods for incomplete
#' detection at distance zero. In: Advanced Distance Sampling, eds. S.T.
#' Buckland, D.R.Anderson, K.P. Burnham, J.L. Laake, D.L. Borchers, and L.
#' Thomas. Oxford University Press.
#' @keywords Statistical Models
ddf.rem<-function(dsmodel,mrmodel,data,method = NULL,
meta.data=list(),control=list(),call=""){
# Test to make sure that observer not used in mrmodel
if(length(grep("observer",mrmodel))!=0){
stop("observer cannot be included in models for removal configurations\n")
}
# Save current user options and then set design contrasts to treatment style
save.options<-options()
options(contrasts=c("contr.treatment","contr.poly"))
# Set up meta data values
meta.data <- assign.default.values(meta.data, left=0, width=NA, binned=FALSE,
int.range=NA, point=FALSE)
# Set up control values
control <- assign.default.values(control, showit=0,
estimate=TRUE, refit=TRUE, nrefits=25,
initial=NA, lowerbounds=NA, upperbounds=NA,
mono.points=20)
# Process data
data.list <- process.data(data,meta.data)
meta.data <- data.list$meta.data
xmat <- data.list$xmat
# Create result list
result <- list(call=call, data=data, mrmodel=mrmodel, dsmodel=dsmodel,
meta.data=meta.data, control=control, method="rem")
class(result) <- c("rem","ddf")
# Fit the conditional detection functions using ddf.rem.fi
result$mr <- ddf.rem.fi(mrmodel=mrmodel, data=data,
meta.data=meta.data, control=control,
call=call,method="rem")
# Fit the unconditional detection functions using ddf.ds
# 5/24/05 - jll add call to process.data for unique.data because it
# didn't handle truncation correctly - error reported by Sharon Hedley
unique.data <- data[data$observer==1&data$detected==1,]
obs2 <- data[data$observer==2,]
obs1 <- data[data$observer==1,]
#missed <- data$detected[data$observer==1]==0
unique.data <- rbind(unique.data,obs2[obs1$detected==0,])
unique.data$observer <- 1
unique.data <- process.data(unique.data, meta.data,check=FALSE)$xmat
result$ds <- ddf.ds(dsmodel=dsmodel,data=unique.data,
meta.data=meta.data,control=control,
call=call)
# stop if ds model didn't converge
if(is.null(result$ds$Nhat)){
stop("ds model did not converge; no further results possible")
}
# Combine parameter vectors and hessian matrices
npar.uncond <- length(result$ds$par)
npar <- npar.uncond+length(result$mr$par)
hessian1 <- result$mr$hessian
if(npar.uncond==0){
result$hessian <- hessian1
}else{
hessian1 <- cbind(hessian1,matrix(0,ncol=npar.uncond,nrow=npar-npar.uncond))
hessian2 <- cbind(matrix(0,ncol=npar-npar.uncond,nrow=npar.uncond),
result$ds$hessian)
result$hessian <- rbind(hessian1,hessian2)
}
result$par <- coef(result)
row.names(result$hessian) <- row.names(result$par)
colnames(result$hessian) <- row.names(result$par)
result$par <- result$par$estimate
names(result$par) <- row.names(result$hessian)
# Compute total likelihood and AIC
result$lnl <- result$ds$lnl + result$mr$lnl
result$criterion<- -2*result$lnl + 2*npar
# Get fitted values and predict abundance and its variance in covered region
result$fitted <- predict(result,newdata=xmat,compute=TRUE)$fitted
result$Nhat <- NCovered(result)
# Restore user options
options(save.options)
# Return result
return(result)
}
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