elasticNetSMLpoint {sparseSEM} | R Documentation |
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.
elasticNetSMLpoint(Y, X, Missing, B, alpha_factor, lambda_factor, Verbose)
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 |
the function perform selection path from lambda_max to lambda, calculate power and FDR
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 |
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
Anhui Huang; Dept of Electrical and Computer Engineering, Univ of Miami, Coral Gables, FL
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.
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);