R: Retrieve a data frame of pAUC scores

pauc {precrec} | R Documentation

## Retrieve a data frame of pAUC scores

### Description

The `auc` function takes an `S3` object generated by `part` and `evalmod` and retrieves a data frame with the partial AUC scores of ROC and Precision-Recall curves.

### Usage

    pauc(curves)

    ## S3 method for class 'aucs'
    pauc(curves)

### Arguments

`curves` | An `S3` object generated by `part` and `evalmod`. The `pauc` function accepts the following S3 objects.
`S3` object | # of models | # of test datasets
sscurves | single | single
mscurves | multiple | single
smcurves | single | multiple
mmcurves | multiple | multiple
See the Value section of `evalmod` for more details.

### Value

The `auc` function returns a data frame with pAUC scores.

### See Also

`evalmod` for generating `S3` objects with performance evaluation measures. `part` for calculation of pAUCs. `auc` for retrieving a dataset of AUCs.

### Examples

    ##################################################
    ### Single model & single test dataset
    ###

    ## Load a dataset with 10 positives and 10 negatives
    data(P10N10)

    ## Generate an sscurve object that contains ROC and Precision-Recall curves
    sscurves <- evalmod(scores = P10N10$scores, labels = P10N10$labels)

    ## Calculate partial AUCs
    sscurves.part <- part(sscurves, xlim = c(0.25, 0.75))

    ## Shows pAUCs
    pauc(sscurves.part)

    ##################################################
    ### Multiple models & single test dataset
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(1, 100, 100, "all")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]])

    ## Generate an mscurve object that contains ROC and Precision-Recall curves
    mscurves <- evalmod(mdat)

    ## Calculate partial AUCs
    mscurves.part <- part(mscurves, xlim = c(0, 0.75), ylim = c(0.25, 0.75))

    ## Shows pAUCs
    pauc(mscurves.part)

    ##################################################
    ### Single model & multiple test datasets
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(4, 100, 100, "good_er")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]],
                   dsids = samps[["dsids"]])

    ## Generate an smcurve object that contains ROC and Precision-Recall curves
    smcurves <- evalmod(mdat, raw_curves = TRUE)

    ## Calculate partial AUCs
    smcurves.part <- part(smcurves, xlim = c(0.25, 0.75))

    ## Shows pAUCs
    pauc(smcurves.part)

    ##################################################
    ### Multiple models & multiple test datasets
    ###

    ## Create sample datasets with 100 positives and 100 negatives
    samps <- create_sim_samples(4, 100, 100, "all")
    mdat <- mmdata(samps[["scores"]], samps[["labels"]],
                   modnames = samps[["modnames"]],
                   dsids = samps[["dsids"]])

    ## Generate an mscurve object that contains ROC and Precision-Recall curves
    mmcurves <- evalmod(mdat, raw_curves = TRUE)

    ## Calculate partial AUCs
    mmcurves.part <- part(mmcurves, xlim = c(0, 0.25))

    ## Shows pAUCs
    pauc(mmcurves.part)

[Package precrec version 0.9.1 Index]