MSEENET_SP: Estimate selection probability using MSE loss function along...

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

Estimate selection probability using MSE loss function along with ENET penalty function solving by APGD

Usage

MSEENET_SP(X, y, alphas, n_lambda, ratio=1e-2, B = 500, gamma = 1000, niter = 2000, crit_beta = 1e-04, crit_obj = 1e-08, timer = TRUE)

Arguments

X	expressional levels of n_genes target genes (TGs)
y	expressional levels of a transcription factor (TF)
alphas	the grid sets of alpha (in [0,1]) used to calculate selection probabilities of genes.
n_lambda	the number of lambdas
ratio	the ratio of the smallest lambda. default: 0.01
B	the number of half-sample resamplings used to calculate selection probabilities of genes. default: 500
gamma	initial value of gamma in APGD. default: 1000
niter	the maximum number of APGD to solve Elastic Net regression. default: 2000
crit_beta	converge criterion of change of beta. default: 1e-4
crit_obj	converge criterion of change of objective function. default: 1e-8
timer	decide if exist the output report. default: FALSE

Value

SP	n_genes length vector of selection probability.

Examples

alphas <- seq(0.1,0.9,0.1)
  
n_lambda <- 10
  
B0 <- 100
  
ratio <- 0.01

SP_ENET = MSEENET_SP(X, y, alphas, n_lambda, ratio, B=B0, gamma=1000, niter=2000, timer=FALSE)

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