Sparse regression model

Sparse regression model

fit_srm(
  x,
  y,
  cross_validation = FALSE,
  seed = 1,
  penalty = "L0",
  regulators_num = ncol(x),
  n_folds = 5,
  verbose = TRUE,
  ...
)

Arguments

x

The matrix of regulators.

y

The vector of target.

cross_validation

Logical value, default is FALSE, whether to use cross-validation.

seed

The random seed for cross-validation, default is 1.

penalty

The type of regularization, default is L0. This can take either one of the following choices: L0, L0L1, and L0L2. For high-dimensional and sparse data, L0L2 is more effective.

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.

n_folds

The number of folds for cross-validation, default is 5.

verbose

Logical value, default is TRUE, whether to print progress messages.

...

Parameters for other methods.

Value

A list of the sparse regression model. The list has the following components:

model

The sparse regression model.

metrics

A list of metrics.

coefficients

A list of coefficients.

Examples

data("example_matrix")
fit_srm(
  x = example_matrix[, -1],
  y = example_matrix[, 1]
)
#> $model
#> 
#> $metrics
#> $metrics$r_squared
#> [1] 0.4358336
#> 
#> 
#> $coefficients
#> $coefficients$variable
#>  [1] "g10" "g11" "g12" "g13" "g14" "g15" "g16" "g17" "g18" "g2"  "g3"  "g4" 
#> [13] "g5"  "g6"  "g7"  "g8"  "g9" 
#> 
#> $coefficients$coefficient
#>  [1]  0.006073601  0.006290166  0.010752371 -0.001518502 -0.013052947
#>  [6]  0.016748325 -0.024070066  0.069284763 -0.563969602  0.217868939
#> [11]  0.007553373 -0.017714243  0.023644447 -0.018219713  0.015716106
#> [16]  0.005408364 -0.028352269
#> 
#>