Nothing
doe.gconsensus <- function(x) {
mss <- rep(TRUE, length(x$ilab$data$value))
subset <- (x$ilab$data$included[mss] == 1)
mm <- sum(subset)
if (x$config$expansion.factor.type == "naive") {
coverageFactor <- 2
} else if (x$config$expansion.factor.type == "large sample") {
coverageFactor <- qnorm(1 - x$config$alpha/2)
} else {
coverageFactor <- qt(1 - x$config$alpha/2, mm - 1)
}
doe <- (x$ilab$data$value[mss] - x$fit$value)
# if reliable estimates
# for results not included in calculation of RV
expandedUnc.doe <- sqrt(x$ilab$data$expandedUnc[mss]^2 + x$fit$expandedUnc^2)
# for results included in calculation of RV
expandedUnc.doe[subset] <- sqrt(1 - 2/mm)*
sqrt(x$ilab$data$expandedUnc[mss][subset]^2 + x$fit$expandedUnc^2)
# correction for results included in calculation of RV
if (x$config$unreliable.uncertainties) {
# if unreliable uncertainty estimates then
expandedUnc.doe[subset] <- sqrt(1 + 2*(mm - 2)/pi)*x$fit$expandedUnc
}
labs <- x$ilab$data$participant[mss]
codes <- x$ilab$data$code[mss]
standardUnc.doe <- sqrt(x$ilab$data$expandedUnc[mss]^2 + x$fit$expandedUnc^2)/x$fit$coverageFactor
standardUnc.doe[subset] <- sqrt(1 - 2/mm)*standardUnc.doe[subset]
if (x$config$unreliable.uncertainties) {
# if unreliable uncertainty estimates then
standardUnc.doe[subset] <- sqrt(1 + 2*(mm - 2)/pi)*x$fit$expandedUnc/x$fit$coverageFactor
}
coverageFactor <- expandedUnc.doe/standardUnc.doe
res <- list(fit = data.frame(code = codes, lab = labs, value = doe,
expandedUnc = expandedUnc.doe,
unit = rep(x$fit$unit, length(doe)),
coverageFactor = coverageFactor,
coverageProbability = rep(1 - x$config$alpha, length(doe)),
En = doe/expandedUnc.doe),
gconsensus = x)
class(res) <- "doe"
return(res)
}
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