sensitivity
Calculate Sensitivity, Specificity and predictive values
These functions calculate the sensitivity, specificity or predictive values of a measurement system compared to a
reference results (the truth or a gold standard). The measurement and "truth"
data must have the same two possible outcomes and one of the outcomes
must be thought of as a "positive" results.
The sensitivity is defined as the proportion of positive results out of the number of
samples which were actually positive. When there are no positive results, sensitivity is
not defined and a value of NA is returned. Similarly, when there are no negative
results, specificity is not defined and a value of NA is returned. Similar
statements are true for predictive values.
The positive predictive value is defined as the percent of predicted positives that
are actually positive while the negative predictive value is defined as the percent
of negative positives that are actually negative.
- Keywords
- manip
Usage
sensitivity(data, reference, positive = levels(reference)[1])
specificity(data, reference, negative = levels(reference)[2])
posPredValue(data, reference, positive = levels(reference)[1])
negPredValue(data, reference, negative = levels(reference)[2])Arguments
- data
- a factor containing the discrete measurements
- reference
- a factor containing the reference values
- positive
- a character string that defines the factor level corresponding to the "positive" results
- negative
- a character string that defines the factor level corresponding to the "negative" results
Value
- A number between 0 and 1 (or NA).
Examples
data01 <- factor(c("A", "B", "B", "B"))
data02 <- factor(c("A", "B", "B", "B"))
ref01 <- factor(c("B", "B", "B", "B"))
ref02 <- factor(c("B", "A", "B", "B"))
table(data01, ref01)
sensitivity(data01, ref01)
posPredValue(data01, ref01)
table(data02, ref02)
sensitivity(data02, ref02)
posPredValue(data02, ref02)
data03 <- factor(c("A", "B", "B", "B"))
data04 <- factor(c("B", "B", "B", "B"))
ref03 <- factor(c("B", "B", "B", "B"), levels = c("A", "B"))
ref04 <- factor(c("B", "A", "B", "B"))
table(data03, ref03)
specificity(data03, ref03)
negPredValue(data03, ref03)
table(data04, ref04)
specificity(data04, ref04)
negPredValue(data04, ref04)
if(!isTRUE(all.equal(sensitivity(data01, ref01), .75))) stop("error in sensitivity test 1")
if(!isTRUE(all.equal(sensitivity(data02, ref02), 0))) stop("error in sensitivity test 2")
ref03 <- factor(c("B", "B", "B", "B"))
if(!is.na(sensitivity(data02, ref03, "A"))) stop("error in sensitivity test3")
options(show.error.messages = FALSE)
test1 <-try(sensitivity(data02, as.character(ref03)))
if(grep("Error", test1) != 1)
stop("error in sensitivity calculation - allowed non-factors")
options(show.error.messages = TRUE)
ref03 <- factor(c("B", "B", "B", "B"), levels = c("A", "B"))
if(!isTRUE(all.equal(specificity(data03, ref03), .75))) stop("error in specificity test 1")
if(!isTRUE(all.equal(specificity(data04, ref04), 1.00))) stop("error in specificity test 2")
if(!is.na(specificity(data01, ref01, "A"))) stop("error in specificity test3")
options(show.error.messages = FALSE)
test1 <-try(specificity(data04, as.character(ref03)))
if(grep("Error", test1) != 1)
stop("error in specificity calculation - allowed non-factors")
options(show.error.messages = TRUE)