precrec (version 0.24.0)

plot: Plot performance evaluation metrics

Description

The plot function creates a plot of performance evaluation metrics.

Usage

# S3 method for sscurves
plot(x, y = NULL, ...)
# S3 method for mscurves
plot(x, y = NULL, ...)
# S3 method for smcurves
plot(x, y = NULL, ...)
# S3 method for mmcurves
plot(x, y = NULL, ...)
# S3 method for sspoints
plot(x, y = NULL, ...)
# S3 method for mspoints
plot(x, y = NULL, ...)
# S3 method for smpoints
plot(x, y = NULL, ...)
# S3 method for mmpoints
plot(x, y = NULL, ...)
# S3 method for ssxycurves
plot(x, y = NULL, ...)
# S3 method for msxycurves
plot(x, y = NULL, ...)
# S3 method for smxycurves
plot(x, y = NULL, ...)
# S3 method for mmxycurves
plot(x, y = NULL, ...)

Value

The plot function shows a plot and returns NULL.

Arguments

x

An S3 object generated by evalmod(). The plot function accepts 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 metrics (mode = "basic")

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

See the Value section of evalmod() for more details.

y

Equivalent with curvetype.

...

All the following arguments can be specified.

curvetype

1. ROC and Precision-Recall curves (mode = "rocprc")

   curvetype	description
   ROC	ROC curve
   PRC	Precision-Recall curve

Multiple curvetype can be combined, such as c("ROC", "PRC").

Basic evaluation metrics (mode = "basic")

curvetype	description
error	Normalized ranks vs. error rate
accuracy	Normalized ranks vs. accuracy
specificity	Normalized ranks vs. specificity
sensitivity	Normalized ranks vs. sensitivity
precision	Normalized ranks vs. precision
mcc	Normalized ranks vs. Matthews correlation coefficient
fscore	Normalized ranks vs. F-score
balanced_accuracy	Normalized ranks vs. balanced accuracy
npv	Normalized ranks vs. negative predictive value
informedness	Normalized ranks vs. informedness (Youden's J)
markedness	Normalized ranks vs. markedness
kappa	Normalized ranks vs. Cohen's kappa

Multiple curvetype can be combined, such as c("precision", "sensitivity").

type

A character to specify the line type as follows.

"l"

lines

"p"

points

"b"

both lines and points

show_cb

A Boolean value to specify whether point-wise confidence bounds are drawn. It is effective only when calc_avg of the evalmod() function is set to TRUE.

raw_curves

A Boolean value to specify whether raw curves are shown instead of the average curve. It is effective only when raw_curves of the evalmod() function is set to TRUE.

show_legend

A Boolean value to specify whether the legend is shown.

See Also

evalmod() for generating an S3 object. autoplot() for plotting the equivalent curves with ggplot2.

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)

## Plot both ROC and Precision-Recall curves
plot(sscurves)

## Plot a ROC curve
plot(sscurves, curvetype = "ROC")

## Plot a Precision-Recall curve
plot(sscurves, curvetype = "PRC")

## Generate an sspoints object that contains basic evaluation metrics
sspoints <- evalmod(
  mode = "basic", scores = P10N10$scores,
  labels = P10N10$labels
)

## Plot normalized ranks vs. basic evaluation metrics
plot(sspoints)

## Plot normalized ranks vs. precision
plot(sspoints, curvetype = "precision")


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

## Plot both ROC and Precision-Recall curves
plot(mscurves)

## Hide the legend
plot(mscurves, show_legend = FALSE)

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

## Plot normalized ranks vs. basic evaluation metrics
plot(mspoints)

## Hide the legend
plot(mspoints, show_legend = FALSE)


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

## Plot average ROC and Precision-Recall curves
plot(smcurves, raw_curves = FALSE)

## Hide confidence bounds
plot(smcurves, raw_curves = FALSE, show_cb = FALSE)

## Plot raw ROC and Precision-Recall curves
plot(smcurves, raw_curves = TRUE, show_cb = FALSE)

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

## Plot normalized ranks vs. average basic evaluation metrics
plot(smpoints)


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

## Plot average ROC and Precision-Recall curves
plot(mmcurves, raw_curves = FALSE)

## Show confidence bounds
plot(mmcurves, raw_curves = FALSE, show_cb = TRUE)

## Plot raw ROC and Precision-Recall curves
plot(mmcurves, raw_curves = TRUE)

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

## Plot normalized ranks vs. average basic evaluation metrics
plot(mmpoints)


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

## Average ROC and Precision-Recall curves
plot(cvcurves)

## Show confidence bounds
plot(cvcurves, show_cb = TRUE)

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

## Normalized ranks vs. average basic evaluation metrics
plot(cvpoints)
}