# predValues: Compute PPV and NPV. In MKmisc: Miscellaneous Functions from M. Kohl

## Description

The function computes the positive (PPV) and negative predictive value (NPV) given sensitivity, specificity and prevalence (pre-test probability).

## Usage

 `1` ```predValues(sens, spec, prev) ```

## Arguments

 `sens` numeric vector: sensitivities. `spec` numeric vector: specificities. `prev` numeric vector: prevalence.

## Details

The function computes the positive (PPV) and negative predictive value (NPV) given sensitivity, specificity and prevalence (pre-test probability).

It's a simple application of the Bayes formula.

One can also specify vectors of length larger than 1 for sensitivity and specificity.

## Value

Vector or matrix with PPV and NPV.

## Author(s)

Matthias Kohl [email protected]

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14``` ```## Example: HIV test ## 1. ELISA screening test (4th generation) predValues(sens = 0.999, spec = 0.998, prev = 0.001) ## 2. Western-Plot confirmation test predValues(sens = 0.998, spec = 0.999996, prev = 1/3) ## Example: connection between sensitivity, specificity and PPV sens <- seq(0.6, 0.99, by = 0.01) spec <- seq(0.6, 0.99, by = 0.01) ppv <- function(sens, spec, pre) predValues(sens, spec, pre)[,1] res <- outer(sens, spec, ppv, pre = 0.1) image(sens, spec, res, col = terrain.colors(256), main = "PPV for prevalence = 10%", xlim = c(0.59, 1), ylim = c(0.59, 1)) contour(sens, spec, res, add = TRUE) ```

MKmisc documentation built on May 29, 2017, 2:10 p.m.