lassoSML: The Lasso penalty for SML

Description Usage Arguments Details Value Author(s) References Examples

View source: R/lassoSML.R

Description

Upon lambda_max to lambda_min in 20 step, the function compute 5 fold CV to determine the optimal lambda for the data.

Usage

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	lassoSML(Y, X, Missing, B, Verbose = 5)

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 SEM

fout

f: the weight for matrix X

stat

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

simTime

computational time

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 <- lassoSML(Y, X, Missing, B, Verbose = 1); 

Example output

	LASSO SML version_1; 30 Genes,  200 samples; Verbose:  1 

	0/15 lambdas. 	lambda_factor: 0.630957	lambda: 86.881122
	1/15 lambdas. 	lambda_factor: 0.398107	lambda: 54.818282
	2/15 lambdas. 	lambda_factor: 0.251189	lambda: 34.587998
	3/15 lambdas. 	lambda_factor: 0.158489	lambda: 21.823551
	4/15 lambdas. 	lambda_factor: 0.100000	lambda: 13.769730
	5/15 lambdas. 	lambda_factor: 0.063096	lambda: 8.688112
	6/15 lambdas. 	lambda_factor: 0.039811	lambda: 5.481828
	7/15 lambdas. 	lambda_factor: 0.025119	lambda: 3.458800
	8/15 lambdas. 	lambda_factor: 0.015849	lambda: 2.182355
	9/15 lambdas. 	lambda_factor: 0.010000	lambda: 1.376973
	10/15 lambdas. 	lambda_factor: 0.006310	lambda: 0.868811
	11/15 lambdas. 	lambda_factor: 0.003981	lambda: 0.548183
	12/15 lambdas. 	lambda_factor: 0.002512	lambda: 0.345880
	13/15 lambdas. 	lambda_factor: 0.001585	lambda: 0.218236
	14/15 lambdas. 	lambda_factor: 0.001000	lambda: 0.137697
	 computation time: 1.252 sec

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