Nothing
msvytotal <-
function(y, design, k, type="rht", na.rm=FALSE, control=rht.control(...), ...){
# retrieve all parameters
acc <- control$acc
steps <- control$steps
asymmetric <- control$asymmetric
ddigits <- control$ddigits
quietly<- control$quietly
# methods; note that the type here is different from the type argument in msvymean
method <- switch(type,
rht = "robust Horvitz-Thompson total",
rwt = "robust weighted total")
if (is.null(method)) stop("Type must be either 'rht' or 'rwt' !\n")
# add the one-step estimator as an explicit estimator
add.method <- ifelse(steps == 1, " (One-step M-estimation)", " (M-estimation)")
method <- paste(method, add.method, sep="")
# remember the call
call <- match.call()
# call msvymean
type <- ifelse(type == "rwt", "rwm", "rht")
average <- msvymean(y, design, type=type, k, na.rm, acc=acc, steps=steps, quietly=TRUE, asymmetric=asymmetric, exact=FALSE, ddigits=ddigits)
# retrieve the robustness weights from estimating the M-estimator
u <- robweights(average)
# compute y_i * u_i and update the design
altered.design <- .updte(y, design, u)
# compute the robust total
tmp <- svytotal(y, altered.design)
total <- coef(tmp)
# add the attributes from average
attributes(total) <- attributes(average)
# and modifiy the relevant attributes
attr(total, "var") <- vcov(tmp)
attr(total, "call") <- call
attr(total, "statistic") <- "total"
attr(total, "method") <- method
#
if(!quietly){
summary(total)
invisible(total)
}else{
invisible(total)
}
}
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