auc_ci | R Documentation |
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")
curves |
An
See the Value section of | |||||||||
alpha |
A numeric value of the significant level (default: 0.05) | |||||||||
dtype |
A string to specify the distribution used for CI calculation.
|
The auc_ci
function returns a dataframe of AUC CIs.
evalmod
for generating S3
objects with
performance evaluation measures. auc
for retrieving a dataset
of AUCs.
##################################################
### 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
##################################################
### 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
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