elasticNetSMLpoint: The Elastic Net penalty for SEM

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

View source: R/elasticNetSMLpoint.R

Description

This function can be used after elasticNetSMLcv determines the optimal parameters. For user supplied one alpha in range of (0,1) and one lambda, the function perform selection path from lambda_max to lambda to determine the optimal network topology.

Usage

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	elasticNetSMLpoint(Y, X, Missing, B, alpha_factor, lambda_factor, Verbose)

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

alpha_factor

alpha_factor: in range of (0, 1); must be scalar

lambda_factor

penalty lambda_factor: in range of (0, 1); must be scalar

Verbose

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

Details

the function perform selection path from lambda_max to lambda, 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);
	OUT <- elasticNetSMLpoint(Y, X, Missing, B,
		alpha_factor = 0.5, lambda_factor = 0.1, Verbose = 1);

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