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

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

`sens` |
numeric vector: sensitivities. |

`spec` |
numeric vector: specificities. |

`prev` |
numeric vector: prevalence. |

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.

Vector or matrix with PPV and NPV.

Matthias Kohl Matthias.Kohl@stamats.de

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)
``` |

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