![]() If (! is.na (sensitivity(data02, ref03, "A" ))) stop( "error in sensitivity test3" ) If (!isTRUE(all.equal(sensitivity(data02, ref02), 0 ))) stop( "error in sensitivity test 2" ) 75 ))) stop( "error in sensitivity test 1" ) If (!isTRUE(all.equal(sensitivity(data01, ref01). NegPredValue(data, reference, negative = levels(reference)) Argumentsĭata01 <- factor( c ( "A", "B", "B", "B" ))ĭata02 <- factor( c ( "A", "B", "B", "B" )) PosPredValue(data, reference, positive = levels(reference)) ![]() Specificity(data, reference, negative = levels(reference)) ![]() Usage sensitivity(data, reference, positive = levels(reference)) Of negative positives that are actually negative. The positive predictive value is defined as the percent of predicted positives thatĪre actually positive while the negative predictive value is defined as the percent Statements are true for predictive values. Results, specificity is not defined and a value of NA is returned. Not defined and a value of NA is returned. ![]() When there are no positive results, sensitivity is The sensitivity is defined as the proportion of positive results out of the number of Must be thought of as a "positive" results. The measurement and "truth"ĭata must have the same two possible outcomes and one of the outcomes Reference results (the truth or a gold standard). Sensitivity: Calculate Sensitivity, Specificity and predictive values Description These functions calculate the sensitivity, specificity or predictive values of a measurement system compared to a ![]()
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