| EBelasticNet.BinomialCV {EBEN} | R Documentation |
Cross Validation (CV) Function to Determine Hyperparameter of the EB_Elastic Net Algorithm for Binomial Model with Normal-Gamma (NG) Prior Distribution
Description
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)
Usage
EBelasticNet.BinomialCV(BASIS, Target, nFolds,foldId, Epis = FALSE, verbose = 0)
Arguments
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 |
TRUE or FALSE for including two-way interactions |
foldId |
random assign samples to different folds |
verbose |
from 0 to 5; larger verbose displays more messages |
Details
If Epis=TRUE, the program adds two-way interaction K*(K-1)/2 more columns to BASIS
Value
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 |
Author(s)
Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL
References
Huang A, Xu S, Cai X: Empirical Bayesian LASSO-logistic regression for multiple binary trait locus mapping. BMC genetics 2013, 14(1):5.
Examples
## not run
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
## Not run:
CV = EBelasticNet.BinomialCV(BASIS, y, nFolds = 3,Epis = FALSE)
## End(Not run)