adaplassostdseq_lambda: Fits Adaptive adaplasso on a sequence of lambda values based...

In Saptati-Datta/AdapLasso: Computes Adaptive Lasso Using Lars Algorithm

Usage

adaplassostdseq_lambda(
  Xstd,
  Ystd,
  tuning_seq = NULL,
  len_tuning = 60,
  eps = 0.001
)

Arguments

Xstd	n x p design matrix X scaled according to LARS algorithm and centered to mean 0
Ystd	n x 1 centered output vector
tuning_seq	(optional)sequence of tuning parameters
len_tuning	length of desired tuning parameter sequence
eps	precision level for convergence assessment, default 0.001

Value

tuning_seq	the actual sequence of tuning parameters used
beta_lamb	matrix of solutions at each lambda value for a given gamma, dimension is p x len_tuning \itemobj_min_vecvector of optimal values of the objective function for each lambda at solution

Fits Adaptive adaplasso on a sequence of lambda values based on standardized data X <- matrix(rnorm(500), 50, 10) Y <- rnorm(50) gamma <- 2 #Standardizing X and Y std <- standardize(X , Y , gamma) #Deriving weighted and centered design matrix Xstd <- std$Xstd #Deriving centered Y Ystd <- std$Ystd fit <- adaplassostdseq_lambda(Xstd, Ystd)

Saptati-Datta/AdapLasso documentation built on Dec. 18, 2021, 12:57 p.m.