Modifier
and ModifierSet
objectsR/AllGenerics.R
, R/Modifier-roc.R
plotROC.Rd
plotROC
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.
a Modifier
or a ModifierSet
object
coordinates of position to label as positive. Either a
GRanges
or a GRangesList
object. For both types the Parent
column is expected to match the gene or transcript name.
additional arguments
the score identifier to subset to, if multiple scores are available.
arguments which will be used for calling
prediction
form the ROCR
package
arguments which will be used for calling
performance
form the ROCR
package
arguments which will be used for calling plot
on the
performance object of the ROCR
package. If multiple scores are plotted
(for example if the score argument is not explicitly set) add = FALSE
will be set.
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)