lassoSEM: The Lasso penalty for SEM

In sparseSEM: Elastic Net Penalized Maximum Likelihood for Structural Equation Models with Network GPT Framework

The Lasso penalty for SEM

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

	lassoSEM(Y, X, Missing, B, verbose = 5)

Arguments

Y	gene expression M by N matrix
X	The network node attribute matrix with dimension of M by N. Theoretically, X can be L by N matrix, with L being the total node attributes. In current implementation, each node only allows one and only one attribute. If you have more than one attributes for some nodes, please consider selecting the top one by either correlation or principal component methods. If for some nodes there is no attribute available, fill in the rows with all zeros. See the yeast data 'yeast.rda' for example. X is normalized inside the function.
Missing	missing data in Y
B	true network topology if available
verbose	describe the information output from -1 - 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

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. (2014). "Sparse model learning for inferring genotype and phenotype associations." Ph.D Dissertation. University of Miami(1186).

Examples

	library(sparseSEM)
	data(B);
	data(Y);
	data(X);
	data(Missing);
	## Not run: OUT <- lassoSEM(Y, X, Missing, B, verbose = 0); 

sparseSEM documentation built on Aug. 9, 2023, 5:07 p.m.