Description Usage Arguments Value See Also Examples
The fortify
function converts an S3
object generated by
evalmod
to a data frame for ggplot2.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 | ## 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,
...)
|
model |
An
See the Value section of | ||||||||||||||||||||||||||||||
raw_curves |
A Boolean value to specify whether raw curves are
shown instead of the average curve. It is effective only
when | ||||||||||||||||||||||||||||||
reduce_points |
A Boolean value to decide whether the points should
be reduced. The points are reduced according to | ||||||||||||||||||||||||||||||
... |
Not used by this method. |
The fortify
function returns a data frame for
ggplot2.
evalmod
for generating S3
objects with
performance evaluation measures.
autoplot
for plotting with ggplot2.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95 96 97 98 99 100 101 102 103 104 105 106 107 108 109 110 111 112 113 114 115 116 117 118 119 120 121 122 123 124 125 126 127 128 129 130 131 132 133 134 135 136 137 138 139 140 141 142 143 144 145 146 147 148 149 150 151 152 153 154 155 156 157 158 159 160 161 162 163 164 165 166 167 168 169 170 171 172 173 174 175 176 177 178 179 180 181 182 183 184 185 186 187 188 189 190 191 192 | ## 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)
|
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