View source: R/plot.precision.multiclass.R
plot.precision.multiclass | R Documentation |
Plot the average external error rate of different classifications from the output of precision.simulate.multiclass
## S3 method for class 'precision.multiclass' plot( data, mytitle = "Average external error rates", class.order = NULL, ylim = c(0, 0.5), save.name = NULL )
data |
output variable of |
mytitle |
plot title |
class.order |
the order of classifications |
ylim |
the length of y axis in the plot |
save.name |
plot name to be saved if not NULL |
## Not run: set.seed(101) biological.effect <- estimate.biological.effect(uhdata = uhdata.pl) handling.effect <- estimate.handling.effect(uhdata = uhdata.pl, nuhdata = nuhdata.pl) ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))] biological.effect.nc <- biological.effect[!rownames(biological.effect) %in% ctrl.genes, ] handling.effect.nc <- handling.effect[!rownames(handling.effect) %in% ctrl.genes, ] group.id <- substr(colnames(biological.effect.nc), 7, 7) # randomly split biological effect data into training and test set with # equal number of endometrial and ovarian samples biological.effect.train.ind <- colnames(biological.effect.nc)[c(sample(which(group.id == "E"), size = 64), sample(which(group.id == "V"), size = 64))] biological.effect.test.ind <- colnames(biological.effect.nc)[!colnames(biological.effect.nc) %in% biological.effect.train.ind] biological.effect.train.test.split = list("tr" = biological.effect.train.ind, "te" = biological.effect.test.ind) # non-randomly split handling effect data into training and test set handling.effect.train.test.split = list("tr" = c(1:64, 129:192), "te" = 65:128) biological.effect.nc.tr <- biological.effect.nc[, biological.effect.train.ind] biological.effect.nc.te <- biological.effect.nc[, biological.effect.test.ind] handling.effect.nc.tr <- handling.effect.nc[, c(1:64, 129:192)] handling.effect.nc.te <- handling.effect.nc[, 65:128] # Simulation precision.multiclass.results = precision.simulate.multiclass(seed = 0, N = 3, biological.effect.tr = biological.effect.nc.tr, biological.effect.te = biological.effect.nc.te, handling.effect.tr = handling.effect.nc.tr, handling.effect.te = handling.effect.nc.te, group.id.tr = substr(colnames(biological.effect.nc.tr), 7, 7), group.id.te = substr(colnames(biological.effect.nc.te), 7, 7), train.design.met = "BLK", test.design.met = "STR", train.norm.met = "MN", test.norm.met = "fMN", class.list = c("SVM", "kNN", "LASSO"), train.batch.id = list(1:40, 41:64, (129:152)-64, (153:192)-64), test.batch.id = list((65:80)-64,(81:114)-64,(115:128)-64)) # Plot plot.precision.multiclass(data = precision.multiclass.results, mytitle = "Average external error rates", class.order = c("SVM", "kNN", "ClaNC"), ylim = c(0,0.5), save.name = "myimage") ## End(Not run)
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