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

Description Usage Arguments Details Value Author(s) References Examples

View source: R/EBelasticNet.GaussianCV.R

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 0.0001*lambda_max at even steps. (20 steps)

Usage

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EBelasticNet.GaussianCV(BASIS, Target, nFolds, Epis = "no",foldId)

Arguments

BASIS

sample matrix; rows correspond to samples, columns correspond to features

Target

Response each individual

nFolds

number of n-fold cv

Epis

"yes" or "no" for including two-way interactions

foldId

random assign samples to different folds

Details

If Epis="yes", 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., and Cai, X. (2013). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. submitted.

Examples

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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];
CV = EBelasticNet.GaussianCV(BASIS, y, nFolds = 3,Epis = "no")

EBEN documentation built on May 29, 2017, 7:28 p.m.