fortify: Convert a curves and points object to a data frame for...

fortifyR Documentation

Convert a curves and points object to a data frame for ggplot2

Description

The fortify function converts an S3 object generated by evalmod to a data frame for ggplot2.

Usage

## S3 method for class 'sscurves'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'mscurves'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'smcurves'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'mmcurves'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'sspoints'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'mspoints'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'smpoints'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

## S3 method for class 'mmpoints'
fortify(model, raw_curves = NULL, reduce_points = FALSE, ...)

Arguments

model

An S3 object generated by evalmod. The fortify 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

See the Value section of evalmod for more details.

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.

reduce_points

A Boolean value to decide whether the points should be reduced. The points are reduced according to x_bins of the evalmod function. The default values is FALSE.

...

Not used by this method.

Value

The fortify function returns a data frame for ggplot2.

See Also

evalmod for generating S3 objects with performance evaluation measures. autoplot for plotting with ggplot2.

Examples

## Not run: 

## Load library
library(ggplot2)

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

## Let ggplot internally call fortify
p_rocprc <- ggplot(sscurves, aes(x = x, y = y))
p_rocprc <- p_rocprc + geom_line()
p_rocprc <- p_rocprc + facet_wrap(~curvetype)
p_rocprc

## Explicitly fortify sscurves
ssdf <- fortify(sscurves)

## Plot a ROC curve
p_roc <- ggplot(subset(ssdf, curvetype == "ROC"), aes(x = x, y = y))
p_roc <- p_roc + geom_line()
p_roc

## Plot a Precision-Recall curve
p_prc <- ggplot(subset(ssdf, curvetype == "PRC"), aes(x = x, y = y))
p_prc <- p_prc + geom_line()
p_prc

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

## Plot normalized ranks vs. precision
p_prec <- ggplot(subset(ssdf, curvetype == "precision"), aes(x = x, y = y))
p_prec <- p_prec + geom_point()
p_prec


##################################################
### Multiple models & single test dataset
###

## Create sample datasets with 10 positives and 10 negatives
samps <- create_sim_samples(1, 10, 10, "all")
mdat <- mmdata(samps[["scores"]], samps[["labels"]],
               modnames = samps[["modnames"]])

## Generate an mscurve object that contains ROC and Precision-Recall curves
mscurves <- evalmod(mdat)

## Let ggplot internally call fortify
p_rocprc <- ggplot(mscurves, aes(x = x, y = y, color = modname))
p_rocprc <- p_rocprc + geom_line()
p_rocprc <- p_rocprc + facet_wrap(~curvetype)
p_rocprc

## Explicitly fortify mscurves
msdf <- fortify(mscurves)

## Plot ROC curve
df_roc <- subset(msdf, curvetype == "ROC")
p_roc <- ggplot(df_roc, aes(x = x, y = y, color = modname))
p_roc <- p_roc + geom_line()
p_roc

## Fortified data frame can be used for plotting a Precision-Recall curve
df_prc <- subset(msdf, curvetype == "PRC")
p_prc <- ggplot(df_prc, aes(x = x, y = y, color = modname))
p_prc <- p_prc + geom_line()
p_prc

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

## Fortify mspoints
msdf <- fortify(mspoints)

## Plot normalized ranks vs. precision
df_prec <- subset(msdf, curvetype == "precision")
p_prec <- ggplot(df_prec, aes(x = x, y = y, color = modname))
p_prec <- p_prec + geom_point()
p_prec


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

## Create sample datasets with 10 positives and 10 negatives
samps <- create_sim_samples(5, 10, 10, "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)

## Let ggplot internally call fortify
p_rocprc <- ggplot(smcurves, aes(x = x, y = y, group = dsid))
p_rocprc <- p_rocprc + geom_smooth(stat = "identity")
p_rocprc <- p_rocprc + facet_wrap(~curvetype)
p_rocprc

## Explicitly fortify smcurves
smdf <- fortify(smcurves, raw_curves = FALSE)

## Plot average ROC curve
df_roc <- subset(smdf, curvetype == "ROC")
p_roc <- ggplot(df_roc, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_roc <- p_roc + geom_smooth(stat = "identity")
p_roc

## Plot average Precision-Recall curve
df_prc <- subset(smdf, curvetype == "PRC")
p_prc <- ggplot(df_prc, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_prc <- p_prc + geom_smooth(stat = "identity")
p_prc

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

## Fortify smpoints
smdf <- fortify(smpoints)

## Plot normalized ranks vs. precision
df_prec <- subset(smdf, curvetype == "precision")
p_prec <- ggplot(df_prec, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_prec <- p_prec + geom_ribbon(aes(min = ymin, ymax = ymax),
                               stat = "identity", alpha = 0.25,
                               fill = "grey25")
p_prec <- p_prec + geom_point(aes(x = x, y = y))
p_prec


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

## Create sample datasets with 10 positives and 10 negatives
samps <- create_sim_samples(5, 10, 10, "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)

## Let ggplot internally call fortify
p_rocprc <- ggplot(mmcurves, aes(x = x, y = y, group = dsid))
p_rocprc <- p_rocprc + geom_smooth(aes(color = modname), stat = "identity")
p_rocprc <- p_rocprc + facet_wrap(~curvetype)
p_rocprc

## Explicitly fortify mmcurves
mmdf <- fortify(mmcurves, raw_curves = FALSE)

## Plot average ROC curve
df_roc <- subset(mmdf, curvetype == "ROC")
p_roc <- ggplot(df_roc, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_roc <- p_roc + geom_smooth(aes(color = modname), stat = "identity")
p_roc

## Plot average Precision-Recall curve
df_prc <- subset(mmdf, curvetype == "PRC")
p_prc <- ggplot(df_prc, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_prc <- p_prc + geom_smooth(aes(color = modname), stat = "identity")
p_prc

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

## Fortify mmpoints
mmdf <- fortify(mmpoints)

## Plot normalized ranks vs. precision
df_prec <- subset(mmdf, curvetype == "precision")
p_prec <- ggplot(df_prec, aes(x = x, y = y, ymin = ymin, ymax = ymax))
p_prec <- p_prec + geom_ribbon(aes(min = ymin, ymax = ymax, group = modname),
                               stat = "identity", alpha = 0.25,
                               fill = "grey25")
p_prec <- p_prec + geom_point(aes(x = x, y = y, color = modname))
p_prec

## End(Not run)


precrec documentation built on March 18, 2022, 6:14 p.m.