lasso.intcv: Least absolute shrinkage and selection operator through...

In LXQin/precision: PaiREd miCrorna sImulation on Study desIgn for mOlecular classificatioN

Description

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

Usage

lasso.intcv(kfold = 5, X, y, seed, alp = 1)

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.
alp	alpha, the penalty type. It can be any numeric value from 0 to 1. By default, alp = 1 which is for LASSO. alp = 0 is for ridge and any value in between is for elastic net.

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
cfs	estimated coefficients for the final classifier

References

Friedman, J., Hastie, T. and Tibshirani, R. (2008) Regularization Paths for Generalized Linear Mod- els via Coordinate Descent, http://www.stanford.edu/~hastie/Papers/glmnet.pdf Journal of Statistical Software, Vol. 33(1), 1-22 Feb 2010

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]

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

LXQin/precision documentation built on May 11, 2019, 6:24 p.m.