getPartialRUnconditional <-
function(indicator,
variable,
sampleData,
sampleDesign)
{ #++
# Estimates unconditional partial R-indicators.
stopifnot(variable %in% names(sampleData))
categories <- sampleData[[variable]]
RBiasFactor <- indicator$RBiasFactor
nPopulation <- sum(indicator$sampleDesign$weights)
propMean <- indicator$propMean
arg <- with(indicator,
data.frame(
n = sampleDesign$weights,
prop = sampleDesign$weights * prop))
byCategory <- within(
aggregate(arg, list(category = categories), sum), {
prop <- prop / n
propSign <- sign(n * (prop - propMean))
propVar <- n * (prop - propMean)^2 / nPopulation
PuUnadj <- propSign * sqrt(propVar) })
model <- within(indicator$model,
formula <- replaceRHSByVariable(formula, variable))
propVar <- sum(byCategory$propVar)
Pu <- sqrt(propVar * RBiasFactor)
PuUnadj <- sqrt(propVar)
PuSE <- 0.5 * getRSampleBased(model, sampleData, sampleDesign)$RSE
partialIndicator <- list(
type = 'Unconditional partial R-indicator, sample based',
variable = variable,
Pu = Pu,
PuUnadj = PuUnadj,
PuSE = PuSE,
byCategory = byCategory)
partialIndicator <- getVariancePartialRUnconditional(
partialIndicator, indicator, sampleData, sampleDesign)
partialIndicator$byCategory <-
partialIndicator$byCategory[c('category', 'PuUnadj', 'PuUnadjSE')]
return (partialIndicator)
}
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