roc {GRridge} | R Documentation

## Produces ROC curve for probabilistic classifiers (e.g. logistic regression)

### Description

Computes an ROC-curve for probabilistic classifiers.

### Usage

    roc(probs, true, cutoffs)

### Arguments

`probs` | Numeric vector, with values between 0 and 1
`true` | Binary vector.
`cutoffs` | Numeric vector, with DECREASING values between 1 and 0.

### Details

The vector probs contains predicted probabilities for the response to equal 1, as produced by a probabilistic classifier like logistic regression. The cutoffs can simply represent a grid of values between 0 and 1.

### Value

A matrix with two rows which contain corresponding False Positive and True Positive Rates for all cutoffs.

### Author(s)

Mark A. van de Wiel

### See Also

For area-under-the ROC-curve: `auc`. Examples: `grridge`.

### Examples

    # Load data objects
    data(dataFarkas)

    firstPartition <- CreatePartition(CpGannFarkas)

    sdsF <- apply(datcenFarkas,1,sd)
    secondPartition <- CreatePartition(sdsF,decreasing=FALSE, uniform=TRUE, grsize=5000)

    # Concatenate two partitions
    partitionsFarkas <- list(cpg=firstPartition, sds=secondPartition)

    # A list of monotone functions from the corresponding partition
    monotoneFarkas <- c(FALSE,TRUE)

    #grFarkas <- grridge(datcenFarkas,respFarkas,optl=5.680087,partitionsFarkas,monotone=monotoneFarkas)
    #grFarkascv <- grridgeCV(grFarkas,datcenFarkas,respFarkas,outerfold=10)

    #cutoffs <- rev(seq(0,1,by=0.01))
    #rocgrridgeF <- roc(probs=grFarkascv[,3],true=grFarkascv[,1],cutoffs=cutoffs)
    #rocridgeF <- roc(probs=grFarkascv[,2],true=grFarkascv[,1],cutoffs=cutoffs)
    #plot(rocridgeF[1,],rocridgeF[2,],type="l",lty=1,ann=FALSE,col="grey")
    #points(rocgrridgeF[1,],rocgrridgeF[2,],type="l",lty=1,col="black")
    #legend(0.75,0.1, legend=c("ridge","GRridge"),
    #       lty=c(1,1), lwd=c(1,1),col=c("grey","black"))

[Package GRridge version 1.4.0 Index]