EBelasticNet.BinomialCV {EBEN} | R Documentation |
Hyperparameter controls degree of shrinkage, and is obtained via Cross Validation (CV). This program calculates the maximum lambda that allows one non-zero basis; and performs a search down to 0.001*lambda_max at even steps. (20 steps)
EBelasticNet.BinomialCV(BASIS, Target, nFolds, Epis = "no",foldId)
BASIS |
sample matrix; rows correspond to samples, columns correspond to features |
Target |
Class label of each individual, TAKES VALUES OF 0 OR 1 |
nFolds |
number of n-fold cv |
Epis |
"yes" or "no" for including two-way interactions |
foldId |
random assign samples to different folds |
If Epis="yes", the program adds two-way interaction K*(K-1)/2 more columns to BASIS
CrossValidation |
col1: hyperparameter; col2: loglikelihood mean; standard ERROR of nfold mean log likelihood |
Lmabda_optimal |
the optimal hyperparameter as computed |
Alpha_optimal |
the optimal hyperparameter as computed |
Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL
Huang A, Xu S, Cai X: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics 2013, 14(1):5.
library(EBEN) data(BASISbinomial) data(yBinomial) #reduce sample size to speed up the running time n = 50; k = 100; N = length(yBinomial); set.seed(1) set = sample(N,n); BASIS = BASISbinomial[set,1:k]; y = yBinomial[set]; nFolds = 3 CV = EBelasticNet.BinomialCV(BASIS, y, nFolds = 3,Epis = "no")