Description Usage Arguments Value See Also Examples
The plot
function creates a plot of performance evaluation measures.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ## S3 method for class 'sscurves'
plot(x, y = NULL, ...)
## S3 method for class 'mscurves'
plot(x, y = NULL, ...)
## S3 method for class 'smcurves'
plot(x, y = NULL, ...)
## S3 method for class 'mmcurves'
plot(x, y = NULL, ...)
## S3 method for class 'sspoints'
plot(x, y = NULL, ...)
## S3 method for class 'mspoints'
plot(x, y = NULL, ...)
## S3 method for class 'smpoints'
plot(x, y = NULL, ...)
## S3 method for class 'mmpoints'
plot(x, y = NULL, ...)
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x |
An
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y |
Equivalent with | ||||||||||||||||||||||||||||||
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All the following arguments can be specified.
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The plot
function shows a plot and returns NULL.
evalmod
for generating an S3
object.
autoplot
for plotting the equivalent curves
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 | ## Not run:
##################################################
### 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 measures
sspoints <- evalmod(mode = "basic", scores = P10N10$scores,
labels = P10N10$labels)
## Plot normalized ranks vs. basic evaluation measures
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 measures
mspoints <- evalmod(mdat, mode = "basic")
## Plot normalized ranks vs. basic evaluation measures
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 measures
smpoints <- evalmod(mdat, mode = "basic")
## Plot normalized ranks vs. average basic evaluation measures
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 measures
mmpoints <- evalmod(mdat, mode = "basic")
## Plot normalized ranks vs. average basic evaluation measures
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 measures
cvpoints <- evalmod(cvdat, mode = "basic")
## Normalized ranks vs. average basic evaluation measures
plot(cvpoints)
## End(Not run)
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