Calculate CIs of ROC and precision-recall AUCs

The auc_ci function takes an S3 object generated by evalmod and calculates CIs of AUCs when multiple data sets are specified.

auc_ci(curves, alpha = NULL, dtype = NULL)

# S3 method for class 'aucs'
auc_ci(curves, alpha = 0.05, dtype = "normal")

Arguments

curves

An S3 object generated by evalmod. The auc_ci function accepts the following S3 objects.

S3 object	# of models	# of test datasets
smcurves	single	multiple
mmcurves	multiple	multiple

See the Value section of evalmod for more details.

alpha

A numeric value of the significant level (default: 0.05)

dtype

A string to specify the distribution used for CI calculation.

dtype	distribution
normal (default)	Normal distribution
z	Normal distribution
t	t-distribution

Value

The auc_ci function returns a dataframe of AUC CIs.

See also

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

Examples

##################################################
### 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)

## Calculate CI of AUCs
sm_auc_cis <- auc_ci(smcurves)

## Shows the result
sm_auc_cis
#>   modnames curvetypes     mean      error lower_bound upper_bound n
#> 1  good_er        ROC 0.789975 0.02019134   0.7697837   0.8101663 4
#> 2  good_er        PRC 0.834768 0.02176032   0.8130076   0.8565283 4

##################################################
### 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)

## Calculate CI of AUCs
mm_auc_ci <- auc_ci(mmcurves)

## Shows the result
mm_auc_ci
#>    modnames curvetypes      mean       error lower_bound upper_bound n
#> 1    random        ROC 0.4785750 0.025405848   0.4531692   0.5039808 4
#> 2    random        PRC 0.4927332 0.022360158   0.4703731   0.5150934 4
#> 3   poor_er        ROC 0.7893750 0.043471334   0.7459037   0.8328463 4
#> 4   poor_er        PRC 0.7458060 0.037821227   0.7079848   0.7836272 4
#> 5   good_er        ROC 0.7734750 0.041057727   0.7324173   0.8145327 4
#> 6   good_er        PRC 0.8099094 0.033761476   0.7761479   0.8436708 4
#> 7     excel        ROC 0.9810500 0.006008337   0.9750417   0.9870583 4
#> 8     excel        PRC 0.9813123 0.004354320   0.9769579   0.9856666 4
#> 9      perf        ROC 1.0000000 0.000000000   1.0000000   1.0000000 4
#> 10     perf        PRC 1.0000000 0.000000000   1.0000000   1.0000000 4