getPartialRs:

In BERENZ/RISQ: What the package does (short line)

Usage

getPartialRs(indicator, sampleData, sampleDesign, otherVariables = character())

Arguments

indicator
sampleData
sampleDesign
otherVariables

Examples

##---- Should be DIRECTLY executable !! ----
##-- ==>  Define data, use random,
##--	or do  help(data=index)  for the standard data sets.

## The function is currently defined as
function (indicator, sampleData, sampleDesign, otherVariables = character()) 
{
    modelVariables <- getVariables(indicator$model$formula, FALSE)
    variables <- unique(c(modelVariables, otherVariables))
    byVariables <- NULL
    byCategories <- list()
    for (variable in variables) {
        pConditional <- getPartialRConditional(indicator, variable, 
            sampleData, sampleDesign)
        pUnconditional <- getPartialRUnconditional(indicator, 
            variable, sampleData, sampleDesign)
        byVariable <- data.frame(variable = variable, Pu = pUnconditional$Pu, 
            PuUnadj = pUnconditional$PuUnadj, PuSE = pUnconditional$PuSE, 
            Pc = pConditional$Pc, PcUnadj = pConditional$PcUnadj, 
            PcSEApprox = pUnconditional$PuSE)
        byVariables <- rbind(byVariables, byVariable)
        byCategory <- merge(pUnconditional$byCategory, pConditional$byCategory)
        byCategories <- c(byCategories, list(byCategory))
    }
    names(byCategories) <- byVariables$variable
    partialRs <- list(byVariables = byVariables, byCategories = byCategories)
    return(partialRs)
  }

BERENZ/RISQ documentation built on May 5, 2019, 10:25 a.m.