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

Source: R/etc_utils_fortify.R, R/g_fortify.R
fortify.Rd

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

Usage

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

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

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

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

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

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

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

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

# S3 method for class 'ssxycurves'
fortify(model, data, ...)

# S3 method for class 'msxycurves'
fortify(model, data, ...)

# S3 method for class 'smxycurves'
fortify(model, data, ...)

# S3 method for class 'mmxycurves'
fortify(model, data, ...)

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
    sscurvessinglesingle
    mscurvesmultiplesingle
    smcurvessinglemultiple
    mmcurvesmultiplemultiple

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

    S3 object# of models# of test datasets
    sspointssinglesingle
    mspointsmultiplesingle
    smpointssinglemultiple
    mmpointsmultiplemultiple

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

data

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. A single test dataset has no average to contrast the raw curves with, so sscurves and sspoints, and likewise mscurves and mspoints, always show the raw curves.

reduce_points

A Boolean value to decide whether the points should be reduced. The number kept is x_bins of the evalmod() function, per curve for mode = "rocprc" and per metric for mode = "basic". The default value is FALSE. Reduction changes only which points are returned, never how they were calculated, and nothing is dropped from an object that already holds x_bins points or fewer.

...

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 metrics. autoplot() for plotting with ggplot2.

Examples

if (FALSE) { # \dontrun{

## 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 metrics
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 metrics
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 metrics
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 metrics
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
} # }