| EBelasticNet.GaussianCV {EBEN} | R Documentation |
Cross Validation (CV) Function to Determine Hyperparameters of the EBEN Algorithm for Gaussian Model
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
EBelasticNet.GaussianCV(BASIS, Target, nFolds,foldId, Epis = FALSE, verbose = 0)
Arguments
BASIS |
sample matrix; rows correspond to samples, columns correspond to features |
Target |
Response each individual |
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., and Cai, X. (2013). Empirical Bayesian elastic net for multiple quantitative trait locus mapping. submitted.
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 = EBelasticNet.GaussianCV(BASIS, y, nFolds = 3,Epis = FALSE)
## End(Not run)