elasticNetSML: The Elastic Net penalty for SEM

Description Usage Arguments Details Value Note Author(s) References Examples

View source: R/elasticNetSML.R

Description

For each alpha from 0.95 to 0.05 at a step of 0.05, the function perform 5 fold CV for lambda_max to lambda_min in 20 step to determine the optimal alpha and lambda for the data.

Usage

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elasticNetSML(Y, X, Missing, B, Verbose = 0)

Arguments

Y

gene expression M by N matrix

X

cis_eQTL M by N matrix

Missing

missing data in Y

B

true network topology if available

Verbose

describe the information output from 0 - 10, larger number means more output

Details

the function perform CV and parameter inference, calculate power and FDR

Value

Bout

the matrix B from sparseSEM

fout

f: the weight for matrix X

stat

compute the power and FDR statistics if the ture topology is provided

simTime

computational time

Note

Difference in three functions:
1) elasticNetSML: Default alpha = 0.95: -0.05: 0.05; default 20 lambdas
2) elasticNetSMLcv: user supplied alphas (one or more), lambdas; compute the optimal parameters and network parameters
3) elasticNetSMLpoint: user supplied one alpha and one lambda, compute the network parameters

Author(s)

Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL

References

1. Cai, X., Bazerque, J.A., and Giannakis, G.B. (2013). Inference of Gene Regulatory Networks with Sparse Structural Equation Models Exploiting Genetic Perturbations. PLoS Comput Biol 9, e1003068.
2. Huang A., Bazerque J. A., Giannakis G. B., DUroso G., Myers C. L., Cai X., Elastic Net algorithm for inferring gene regulatory networks based on structural equation models, to be submitted.

Examples

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	library(sparseSEM)
	data(B);
	data(Y);
	data(X);
	data(Missing);
	#Example is commented to satisfy CRAN's CPU time limit requirement.
	#OUT <- elasticNetSML(Y, X, Missing, B, Verbose = 1); 

sparseSEM documentation built on May 29, 2017, 8:29 p.m.