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#' @rdname sensitivity
#' @export
posPredValue <-
function(data, ...){
UseMethod("posPredValue")
}
#' @rdname sensitivity
#' @export
"posPredValue.default" <-
function(data, reference, positive = levels(reference)[1], prevalence = NULL, ...)
{
if(!is.factor(reference) | !is.factor(data))
stop("inputs must be factors")
if(length(unique(c(levels(reference), levels(data)))) != 2)
stop("input data must have the same two levels")
lvls <- levels(data)
if(is.null(prevalence)) prevalence <- mean(reference == positive)
sens <- sensitivity(data, reference, positive)
spec <- specificity(data, reference, lvls[lvls != positive])
(sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence)))
}
#' @rdname sensitivity
#' @export
"posPredValue.table" <-
function(data, positive = rownames(data)[1], prevalence = NULL, ...)
{
## "truth" in columns, predictions in rows
if(!all.equal(nrow(data), ncol(data))) stop("the table must have nrow = ncol")
if(!all.equal(rownames(data), colnames(data))) stop("the table must the same groups in the same order")
if(nrow(data) > 2)
{
tmp <- data
data <- matrix(NA, 2, 2)
colnames(data) <- rownames(data) <- c("pos", "neg")
posCol <- which(colnames(tmp) %in% positive)
negCol <- which(!(colnames(tmp) %in% positive))
data[1, 1] <- sum(tmp[posCol, posCol])
data[1, 2] <- sum(tmp[posCol, negCol])
data[2, 1] <- sum(tmp[negCol, posCol])
data[2, 2] <- sum(tmp[negCol, negCol])
data <- as.table(data)
positive <- "pos"
rm(tmp)
}
negative <- colnames(data)[colnames(data) != positive]
if(is.null(prevalence)) prevalence <- sum(data[, positive])/sum(data)
sens <- sensitivity(data, positive)
spec <- specificity(data, negative)
(sens * prevalence)/((sens*prevalence) + ((1-spec)*(1-prevalence)))
}
#' @rdname sensitivity
#' @export
"posPredValue.matrix" <-
function(data, positive = rownames(data)[1], prevalence = NULL, ...)
{
data <- as.table(data)
posPredValue.table(data, prevalence = prevalence)
}
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