as.data.frame: Convert a curves and points object to a data frame

In guillermozbta/precrec: Calculate Accurate Precision-Recall and ROC (Receiver Operator Characteristics) Curves

Description

The as.data.frame function converts an S3 object generated by evalmod to a data frame.

Usage

## S3 method for class 'sscurves'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'mscurves'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'smcurves'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'mmcurves'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'sspoints'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'mspoints'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'smpoints'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'mmpoints'
as.data.frame(x, row.names = NULL, optional = FALSE,
  raw_curves = NULL, ...)

## S3 method for class 'aucroc'
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Arguments

x	An S3 object generated by evalmod. The as.data.frame function takes one of the following S3 objects. ROC and Precision-Recall curves (mode = "rocprc") S3 object # of models # of test datasets sscurves single single mscurves multiple single smcurves single multiple mmcurves multiple multiple Basic evaluation measures (mode = "basic") S3 object # of models # of test datasets sspoints single single mspoints multiple single smpoints single multiple mmpoints multiple multiple Fast AUC (ROC) calculation with the U statistic (mode = "aucroc") S3 object # of models # of test datasets aucroc - - See the Value section of evalmod for more details.
row.names	Not used by this method.
optional	Not used by this method.
raw_curves	A Boolean value to specify whether raw curves are shown instead of the average curve. It is effective only when raw_curves is set to TRUE of the evalmod function.
...	Not used by this method.

Value

The as.data.frame function returns a data frame.

See Also

evalmod for generating S3 objects with performance evaluation measures.

Examples

## Not run: 
##################################################
### 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)

## Convert sscurves to a data frame
sscurves.df <- as.data.frame(sscurves)

## Show data frame
head(sscurves.df)

## Generate an sspoints object that contains basic evaluation measures
sspoints <- evalmod(mode = "basic", scores = P10N10$scores,
                    labels = P10N10$labels)
## Convert sspoints to a data frame
sspoints.df <- as.data.frame(sspoints)

## Show data frame
head(sspoints.df)


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

## Convert mscurves to a data frame
mscurves.df <- as.data.frame(mscurves)

## Show data frame
head(mscurves.df)

## Generate an mspoints object that contains basic evaluation measures
mspoints <- evalmod(mdat, mode = "basic")

## Convert mspoints to a data frame
mspoints.df <- as.data.frame(mspoints)

## Show data frame
head(mspoints.df)


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

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(10, 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)

## Convert smcurves to a data frame
smcurves.df <- as.data.frame(smcurves)

## Show data frame
head(smcurves.df)

## Generate an smpoints object that contains basic evaluation measures
smpoints <- evalmod(mdat, mode = "basic")

## Convert smpoints to a data frame
smpoints.df <- as.data.frame(smpoints)

## Show data frame
head(smpoints.df)


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

## Create sample datasets with 100 positives and 100 negatives
samps <- create_sim_samples(10, 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)

## Convert mmcurves to a data frame
mmcurves.df <- as.data.frame(mmcurves)

## Show data frame
head(mmcurves.df)

## Generate an mmpoints object that contains basic evaluation measures
mmpoints <- evalmod(mdat, mode = "basic")

## Convert mmpoints to a data frame
mmpoints.df <- as.data.frame(mmpoints)

## Show data frame
head(mmpoints.df)


##################################################
### N-fold cross validation datasets
###

## Load test data
data(M2N50F5)

## Speficy nessesary columns to create mdat
cvdat <- mmdata(nfold_df = M2N50F5, score_cols = c(1, 2),
                lab_col = 3, fold_col = 4,
                modnames = c("m1", "m2"), dsids = 1:5)

## Generate an mmcurve object that contains ROC and Precision-Recall curves
cvcurves <- evalmod(cvdat)

## Convert mmcurves to a data frame
cvcurves.df <- as.data.frame(cvcurves)

## Show data frame
head(cvcurves.df)

## Generate an mmpoints object that contains basic evaluation measures
cvpoints <- evalmod(cvdat, mode = "basic")

## Convert mmpoints to a data frame
cvpoints.df <- as.data.frame(cvpoints)

## Show data frame
head(cvpoints.df)


##################################################
### AUC with the U statistic
###

## mode = "aucroc"
data(P10N10)
uauc1 <- evalmod(scores = P10N10$scores, labels = P10N10$labels,
                 mode="aucroc")

# as.data.frame 'aucroc'
as.data.frame(uauc1)

## mode = "aucroc"
samps <- create_sim_samples(10, 100, 100, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
               modnames = samps[["modnames"]],
               dsids = samps[["dsids"]])
uauc2 <- evalmod(mdat, mode="aucroc")

# as.data.frame 'aucroc'
head(as.data.frame(uauc2))

## End(Not run)

guillermozbta/precrec documentation built on May 11, 2019, 7:22 p.m.