Prediction with Diagonal Linear Discriminant classifier

Predict from a Diagonal Linear Discriminant classifier fit.

Usage

dlda.predict(dlda.intcv.model, pred.obj, pred.obj.group.id)

Arguments

dlda.intcv.model: a DLDA classifier built with dlda.intcv.
pred.obj: dataset to have its sample group predicted. The dataset must have rows as probes and columns as samples. It must have an equal number of probes as the dataset being trained.
pred.obj.group.id: a vector of sample-group labels for each sample of the dataset to be predicted. It must have an equal length to the number of samples as pred.obj.

Value

a list of 3 elements:

pred: predicted sample group for each sample
mc: a predicted misclassification error rate (external validation)
prob: predicted probability for each sample

References

https://cran.r-project.org/web/packages/HiDimDA/index.html

Examples

set.seed(101)
biological.effect <- estimate.biological.effect(uhdata = uhdata.pl)
ctrl.genes <- unique(rownames(uhdata.pl))[grep("NC", unique(rownames(uhdata.pl)))]
biological.effect.nc <- biological.effect[!rownames(biological.effect) %in% ctrl.genes, ]
group.id <- substr(colnames(biological.effect.nc), 7, 7)

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.nc.tr <- biological.effect.nc[, biological.effect.train.ind]
biological.effect.nc.te <- biological.effect.nc[, biological.effect.test.ind]

dlda.int <- dlda.intcv(X = biological.effect.nc.tr,
                     y = substr(colnames(biological.effect.nc.tr), 7, 7),
                     kfold = 5, seed = 1)

dlda.pred <- dlda.predict(dlda.intcv.model = dlda.int,
                        pred.obj = biological.effect.nc.te,
                        pred.obj.group.id = substr(colnames(biological.effect.nc.te), 7, 7))
dlda.int$mc
#> [1] 0.1171875
dlda.pred$mc
#> [1] 0.140625