MSEENET_Beta: Estimate beta_hat using MSE loss function along with Elastic...

In xueweic/APGD: TGPred: Efficient methods for predicting target genes of a transcription factor by integrating statistics, machine learning, and optimization.

Estimate beta_hat using MSE loss function along with Elastic Net penalty function

Usage

MSEENET_Beta(X, y, lambda0, alpha0, method = "APGD", gamma = 1000, niter = 2000, crit_beta = 1e-04, crit_obj = 1e-08, quiet = FALSE, if.scale=FALSE)

Arguments

X	expressional levels of n_genes target genes (TGs)
y	expressional levels of a transcription factor (TF)
lambda0	lambda0 one of parameters in Elastic Net regression, which controls the number of nonzero coefficients.
alpha0	alpha0 one of parameters in Elastic Net regression, which controls the numerical values of nonzero coefficients.
method	method The current methods must be 'APGD' or 'CVX'
gamma	gamma initial value of gamma in APGD. default: 1000
niter	niter the maximum number of APGD to solve Elastic Net regression. default: 2000
crit_beta	crit_beta converge criterion of change of beta. default: 1e-4
crit_obj	crit_obj converge criterion of change of objective function. default: 1e-8
quiet	quiet decide if exist the output report. default: FALSE
if.scale	if.scale decide if scale the expression levels. default: FALSE

Value

beta	n_genes length vector of estimated regulated effect sizes, where beta_j != 0 indicates j th gene is not selected in ENET regression.

Examples

lambda0 = 200
 
alpha0 = 0.5
 
beta_hat_APGD <- MSEENET_Beta(X, y, lambda0, alpha0, method="APGD", if.scale=TRUE)
 
library("CVXR")
 
beta_hat_CVX <- MSEENET_Beta(X, y, lambda0, alpha0,method="CVX", if.scale=TRUE)

xueweic/APGD documentation built on Sept. 4, 2023, 2:18 a.m.