svm.intcv: Support Vector Machine Classifier

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

Support Vector Machine Classifier

Description

Build a support vactor machine classifier using internal cross validation to choose the turning parameter, with a 5-fold cross validation as default.

Usage

## S3 method for class 'intcv'
svm(kfold = 5, X, y, seed)

Arguments

kfold	number of folds. By default, kfold = 5.
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 of performing internal validation with SVM
model	a SVM classifier, resulted from cv.fit

References

https://cran.r-project.org/web/packages/e1071/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]

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

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