sparse.regression {inferCSN}

Sparse regression model

Description

Sparse regression model

Usage

sparse.regression(
  x,
  y,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  algorithm = "CD",
  regulators_num = ncol(x),
  n_folds = 10,
  percent_samples = 1,
  r_threshold = 0,
  computation_method = "cor",
  verbose = FALSE,
  ...
)

Arguments

x

The data matrix

y

The response vector

cross_validation

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.

regulators_num

The number of non-zore coefficients, 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.

n_folds

The number of folds for cross-validation.

percent_samples

The percent of all samples used for sparse.regression. Default set to 1.

r_threshold

Threshold of R^2 or correlation coefficient.

computation_method

The method used to compute r.

verbose

Print detailed information.

...

Parameters for other methods.

Value

Coefficients

Examples

data("example_matrix")
sparse.regression(
  example_matrix[, -1],
  example_matrix[, 1]
)