EBlassoNEG.GaussianCV: Cross Validation (CV) Function to Determine Hyperparameters...

In EBEN: Empirical Bayesian Elastic Net

Cross Validation (CV) Function to Determine Hyperparameters of the EBlasso Algorithm for Gaussian Model with Normal-Exponential-Gamma (NEG) Prior Distribution

Description

Hyperparameters control degree of shrinkage, and are obtained via Cross Validation. This program performs three steps of CV.
1st: a = b = 0.001, 0.01, 0.1, 1;
2nd: fix b= b1; a=[-0.5, -0.4, -0.3, -0.2, -0.1, -0.01, 0.01, 0.05, 0.1, 0.5, 1];
3rd: fix a = a2; b= 0.01 to 10 with a step size of one for b > 1 and a step size of one on the logarithmic scale for b < 1
In the 2nd step, a can take value from -1 and values in [-1, -0.5] can be added to the set in line 13 of this function (The smaller a is, the less shrinkage.)

Usage

EBlassoNEG.GaussianCV(BASIS, Target, nFolds, foldId, Epis,verbose, group)

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
foldId	random assign samples to different folds
Epis	TRUE or FALSE for including two-way interactions
verbose	from 0 to 5; larger verbose displays more messages
group	TRUE or FALSE; FALSE: No group effect; TRUE two-way interaction grouped. Only valid when Epis = TRUE

Details

If Epis= TRUE, the program adds two-way interaction K*(K-1)/2 more columns to BASIS
Note: Given the fact that degree of shrinkage is a monotonic function of (a,b),
The function implemented a 3-step search as described in Huang, A. 2014, for full
grid search, user needs to modify the function accordingly.

Value

CrossValidation	col1: hyperparameters; col2: loglikelihood mean; standard ERROR of nfold mean log likelihood
a_optimal	the optimal hyperparameter as computed
b_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.
Huang, A., S. Xu, et al. Whole-genome quantitative trait locus mapping reveals major role of epistasis on yield of rice. PLoS ONE 2014, 9(1): e87330.

Examples

library(EBEN)
data(BASIS)
data(y)
#reduce sample size to speed up the running time
n = 50;
k = 100;
BASIS = BASIS[1:n,1:k];
y  = y[1:n];
## Not run: 
CV = EBlassoNEG.GaussianCV(BASIS, y, nFolds = 3,Epis = FALSE)

## End(Not run)

EBEN documentation built on May 31, 2023, 8:43 p.m.