plotROC | R Documentation |
Modifier
and ModifierSet
objectsplotROC
streamlines labeling, prediction, performance and plotting
functions to test the peformance of a Modifier
object and the data
analyzed via the functionallity from the ROCR
package.
The data from x
will be labeled as positive using the coord
arguments. The other arguments will be passed on to the specific ROCR
functions.
By default the prediction.args
include three values:
measure = "tpr"
x.measure = "fpr"
score = mainScore(x)
The remaining arguments are not predefined.
plotROC(x, coord, ...)
## S4 method for signature 'Modifier'
plotROC(
x,
coord,
score = NULL,
prediction.args = list(),
performance.args = list(),
plot.args = list()
)
## S4 method for signature 'ModifierSet'
plotROC(
x,
coord,
score = NULL,
prediction.args = list(),
performance.args = list(),
plot.args = list()
)
x |
a |
coord |
coordinates of position to label as positive. Either a
|
... |
additional arguments |
score |
the score identifier to subset to, if multiple scores are available. |
prediction.args |
arguments which will be used for calling
|
performance.args |
arguments which will be used for calling
|
plot.args |
arguments which will be used for calling |
a plot send to the active graphic device
Tobias Sing, Oliver Sander, Niko Beerenwinkel, Thomas Lengauer (2005): "ROCR: visualizing classifier performance in R." Bioinformatics 21(20):3940-3941 DOI: 10.1093/bioinformatics/bti623
data(msi,package="RNAmodR")
# constructing a GRanges obejct to mark positive positions
mod <- modifications(msi)
coord <- unique(unlist(mod))
coord$score <- NULL
coord$sd <- NULL
# plotting a TPR vs. FPR plot per ModInosine object
plotROC(msi[[1]],coord)
# plotting a TPR vs. FPR plot per ModSetInosine object
plotROC(msi,coord)
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