# R/sensitivity.R In caret: Classification and Regression Training

```sensitivity <-
function(data, ...){
UseMethod("sensitivity")
}

"sensitivity.default" <-
function(data, reference, positive = levels(reference)[1], na.rm = TRUE, ...)
{
if(!is.factor(reference) | !is.factor(data))
stop("inputs must be factors")

## todo: relax the =2 constraint and let ngative length be > 2
if(length(unique(c(levels(reference), levels(data)))) != 2)
stop("input data must have the same two levels")
if(na.rm)
{
cc <- complete.cases(data) & complete.cases(reference)
if(any(!cc))
{
data <- data[cc]
reference <- reference[cc]
}
}
numer <- sum(data %in% positive & reference %in% positive)
denom <- sum(reference %in% positive)
sens <- ifelse(denom > 0, numer / denom, NA)
sens
}

"sensitivity.table" <-
function(data, positive = rownames(data)[1], ...)
{
## "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)
}

numer <- sum(data[positive, positive])
denom <- sum(data[, positive])
sens <- ifelse(denom > 0, numer / denom, NA)
sens
}

"sensitivity.matrix" <-
function(data, positive = rownames(data)[1], ...)
{
data <- as.table(data)
sensitivity.table(data)
}
```

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caret documentation built on May 2, 2019, 5:47 p.m.