precrec (version 0.14.4)

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

Description

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

Usage

# S3 method for sscurves
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for mscurves
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for smcurves
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for mmcurves
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for sspoints
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for mspoints
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for smpoints
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for mmpoints
as.data.frame(x, row.names = NULL, optional = FALSE, raw_curves = NULL, ...)
# S3 method for aucroc
as.data.frame(x, row.names = NULL, optional = FALSE, ...)

Value

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

Arguments

x

An S3 object generated by evalmod. The as.data.frame function takes one of the following S3 objects.

1. 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

2. Basic evaluation measures (mode = "basic")

   S3 object	# of models	# of test datasets
   sspoints	single	single
   mspoints	multiple	single
   smpoints	single	multiple
   mmpoints	multiple	multiple

3. 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.

See Also

evalmod for generating S3 objects with performance evaluation measures.

Examples

if (FALSE) {
##################################################
### 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))
}