R/etc_utils_fortify.R, R/g_fortify.R
fortify.RdThe fortify function converts an S3 object generated by
evalmod to a data frame for ggplot2.
# S3 method for sscurves
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for mscurves
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for smcurves
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for mmcurves
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for sspoints
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for mspoints
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for smpoints
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)
# S3 method for mmpoints
fortify(model, data, raw_curves = NULL, reduce_points = FALSE, ...)An S3 object generated by evalmod.
The fortify 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 |
See the Value section of evalmod for more details.
Not used by this method.
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 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.
The fortify function returns a data frame for
ggplot2.
if (FALSE) {
## 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
}