dlda.intcv: Diagonal Linear Discriminant Classifier

In yilinwu123/precision1: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

Diagonal Linear Discriminant Classifier

Description

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)

yilinwu123/precision1 documentation built on June 28, 2022, 2:53 a.m.