Diagonal Linear Discriminant Classifier — dlda.intcv • PRECISION.array

Build a Diagonal Linear Discriminant classifier.

Usage

dlda.intcv(kfold = NULL, X, y, seed)

Arguments

kfold: placeholder with no meaning, default as NULL.
X: dataset to be trained. This dataset must have rows as probes and columns as samples.
y: a vector of sample group of each sample for the dataset to be trained. It must have an equal length to the number of samples in X.
seed: an integer used to initialize a pseudorandom number generator.

Value

a list of 4 elements:

mc: an internal misclassification error rate
time: the processing time
model: a DLDA classifier

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

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