View source: R/sparse.regression.R
sub.inferCSN | R Documentation |
Sparse regression model for single gene
sub.inferCSN(
regulatorsMatrix,
targetsMatrix,
target = NULL,
crossValidation = FALSE,
seed = 1,
penalty = "L0",
algorithm = "CD",
maxSuppSize = NULL,
nFolds = 10,
kFolds = NULL,
rThreshold = 0,
verbose = FALSE
)
regulatorsMatrix |
Regulators matrix |
targetsMatrix |
Targets matrix |
target |
Target genes |
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. |
The weight data table of sub-network
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