elasticNetSMLcv: The Elastic Net penalty for SEM with user supplied alphas and...

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

View source: R/elasticNetSMLcv.R

Description

While elasticNetSML function has a set of default (alpha, lambda) and the optimal one is chosen by 5 fold cv, elasticNetSMLcv tests the combination of a set of alpha an lambda, and choose one as the optimal parameters. elasticNetSMLcv should be combined with elasticNetSMLpoint to obtain the network inference. For each alpha from the set of alphas provided, the function perform 5 fold CV for each user supplied lambda to determine the optimal alpha and lambda for the data.

Usage

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elasticNetSMLcv(Y, X, Missing, B, alpha_factors,lambda_factors, 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_factors

alpha_factors: the set of alphas to be tested, and is in range of (0, 1);

lambda_factors

penalty lambda_factor: the set of lambda to be tested, and is in range of (0, 1);

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

residual

only meaningful for 1 alpha:
col1: lambdas;
col2: mean of residual error in k-fold CV
col3: standard error of residual error in k-fold CV

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 <- elasticNetSMLcv(Y, X, Missing, B, alpha_factors = c(0.75, 0.5, 0.25),
	lambda_factors=c(0.1, 0.01, 0.001), Verbose = 1);

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