roc: Build a ROC Curve

In Fuzzy-Logix/AdapteR: Converts R syntax to SQL to provide fast analytics using in-database C++ library DB Lytix.

Description

roc is a diagnostic test which plots the true positive rate against the false positive rate for a given test set.

Usage

roc(object, limit = 1000, ...)

Arguments

response	a FLvector of responses, typically encoded with 0 (controls) and 1 (cases). Only two classes can be used in a ROC curve.
predictor	a FLvector of the same length than response, containing the predicted value of each observation.

Details

The wrapper overloads roc from pROC package and implicitly calls DB-Lytix roc function.

Value

roc returns a FLROC class object which replicates equivalent R output from roc in pROC package.

See Also

roc for corresponding R function reference.

Examples

tbl <- FLTable(getTestTableName("tblROCCurve"),
              "ObsID")
mod <- roc(tbl$ActualVal, tbl$ProbVal)
print(mod)
plot(mod)
auc(mod)
mod$levels

roctbl <- FLTable(getTestTableName("tblROCcurve"), obs_id_colname = "ObsID")
rocmod <- roc.FLTable(ActualVal~ProbVal, data = roctbl)

Fuzzy-Logix/AdapteR documentation built on May 6, 2019, 5:07 p.m.