Sparse regression model
sparse_regression(
x,
y,
cross_validation = FALSE,
seed = 1,
penalty = "L0",
algorithm = "CD",
regulators_num = ncol(x),
n_folds = 10,
subsampling = 1,
r_threshold = 0,
computation_method = "cor",
verbose = TRUE,
...
)
The matrix of regulators.
The vector of target.
Logical value, default is FALSE
, whether to use cross-validation.
The random seed for cross-validation, default is 1
.
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.
The type of algorithm used to minimize the objective function, default is CD
.
Currently CD
and CDPSI
are supported.
The CDPSI
algorithm may yield better results, but it also increases running time.
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.
The number of folds for cross-validation, default is 10
.
The percent of all samples used for sparse_regression
, default is 1
.
Threshold of \(R^2\) or correlation coefficient, default is 0
.
The method used to compute `r“.
Logical value, default is TRUE
, whether to print progress messages.
Parameters for other methods.
Coefficients
data("example_matrix")
sparse_regression(
example_matrix[, -1],
example_matrix[, 1]
)
#> [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