sparse.regression: Sparse regression model

In inferCSN: Inferring Cell-Specific Gene Regulatory Network

Sparse regression model

Description

Sparse regression model

Usage

sparse.regression(
  X,
  y,
  crossValidation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  maxSuppSize = NULL,
  nFolds = 10,
  kFolds = NULL,
  rThreshold = 0,
  verbose = FALSE
)

Arguments

X	The data matrix
y	The response vector
crossValidation	Check whether cross validation is used.
seed	The seed used in randomly shuffling the data for cross-validation.
penalty	The type of regularization. This can take either one of the following choices: "L0" and "L0L2". For high-dimensional and sparse data, such as single-cell sequencing data, "L0L2" is more effective.
algorithm	The type of algorithm used to minimize the objective function. Currently "CD" and "CDPSI" are supported. The CDPSI algorithm may yield better results, but it also increases running time.
maxSuppSize	The number of non-zore coef, this value will affect the final performance. The maximum support size at which to terminate the regularization path. Recommend setting this to a small fraction of min(n,p) (e.g. 0.05 * min(n,p)) as L0 regularization typically selects a small portion of non-zeros.
nFolds	The number of folds for cross-validation.
kFolds	The number of folds for sample split.
rThreshold	rThreshold.
verbose	Print detailed information.

Value

The coefficients

inferCSN documentation built on Nov. 2, 2023, 6:27 p.m.