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#####################################################
## summation.R
##
## the summation method for estimating respondents'
## degrees
#####################################################
##' summation.estimator
##'
##' compute an estimate of the respondents' degrees using
##' the summation method
##'
##' TODO -- cite summation method ref
##'
##' Note that the summation degree estimator for the
##' case where there is missing data is not yet implemented.
##' (In fact, I don't think that there is a known
##' estimator for this case.)
##'
##' @param survey.data the dataframe with the survey results
##' @param sum.q if not NULL, a vector whose entries are the variable
##' names in the dataset corresponding to each summation
##' question. if NULL, then assume that
##' the survey.data dataframe has an attribute called 'sum.qs'
##' containing this vector.
##' @param missing if "ignore", then proceed with the analysis without
##' doing anything about missing values. other options
##' are not yet implemented.
##' @return a vector with an estimate of the degree for each row
##' in survey.data. if na.rm=TRUE, then the degree for rows that have
##' missingness in the summation questions will be set
##' to NA
##' @export
summation.estimator <- function(survey.data,
sum.q=NULL,
missing="ignore")
{
if (is.null(sum.q)) {
sum.q <- attr(survey.data, "sum.q")
}
if (is.null(sum.q)) {
stop("no sum.q provided for computing degrees via the summation method.\n")
}
if (missing != "ignore") {
## TODO -- FILL THIS IN
stop("this is not yet implemented.\n")
} else {
res <- rowSums(subset(survey.data,
select=sum.q))
}
return(res)
}
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