xgboost.intcv: XGBoost Classifier

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

XGBoost Classifier

Description

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

Usage

xgboost.intcv(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 LASSO
model	a LASSO 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]

ranfor.int <- ranfor.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.