qpPRscoreThreshold {qpgraph} | R Documentation

## Calculation of scores thresholds attaining nominal precision or recall levels

### Description

Calculates the score threshold at a given precision or recall level from a given precision-recall curve.

### Usage

    qpPRscoreThreshold(preRecFun, level, recall.level=TRUE, max.score=9999999)

### Arguments

`preRecFun` | precision-recall function (output from `qpPrecisionRecall`).
`level` | recall or precision level.
`recall.level` | logical; if TRUE then it is assumed that the value given in the level parameter corresponds to a desired level of recall; if FALSE then it is assumed a desired level of precision.
`max.score` | maximum score given by the method that produced the precision-recall function to an association.

### Value

The score threshold at which a given level of precision or recall is attained by the given precision-recall function. For levels that do not form part of the given function their score is calculated by linear interpolation and for this reason is important to carefully specify a proper value for the `max.score` parameter.

### Author(s)

R. Castelo and A. Roverato

### References

Fawcett, T. An introduction to ROC analysis. Pattern Recogn. Lett., 27:861-874, 2006.

### See Also

`qpPrecisionRecall` `qpGraph`

### Examples

    require(mvtnorm)

    nVar <- 50  ## number of variables
    maxCon <- 5 ## maximum connectivity per variable
    nObs <- 30  ## number of observations to simulate

    set.seed(123)

    A <- qpRndGraph(p=nVar, d=maxCon)
    Sigma <- qpG2Sigma(A, rho=0.5)
    X <- rmvnorm(nObs, sigma=as.matrix(Sigma))

    nrr.estimates <- qpNrr(X, q=1, verbose=FALSE)

    nrr.prerec <- qpPrecisionRecall(nrr.estimates, A, decreasing=FALSE,
                                    recallSteps=seq(0, 1, by=0.1))

    qpPRscoreThreshold(nrr.prerec, level=0.5, recall.level=TRUE, max.score=0)

    qpPRscoreThreshold(nrr.prerec, level=0.5, recall.level=FALSE, max.score=0)

[Package qpgraph version 2.20.0 Index]