ApplyLasso: This function applies L1 regularised Lasso regression on the...
In HamdaBinteAjmal/DBN4GRN: An R Package for Learning Sparse DBN Structures to Reverse Engineer Gene Regulatory Networks
Description
Usage
Arguments
Value
Description
This function applies L1 regularised Lasso regression on the dataset. The regularisation parameter is selected by cross validation.
It uses function from package "lars".
Usage
1
ApplyLasso(data, pred_pos = NULL)
Arguments
data
a data.frame or a matrix with n rows (=time points) and p columns (=genes) containing the gene expression time series.
pred_pos
indices of the potential predictor variables in the dataset. If the value is NULL, all variables are considered as potential predictors.
Value
a matrix of Lasso regression coefficients.
HamdaBinteAjmal/DBN4GRN documentation built on Dec. 27, 2021, 2:08 p.m.
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Description Usage Arguments Value
Description
This function applies L1 regularised Lasso regression on the dataset. The regularisation parameter is selected by cross validation. It uses function from package "lars".
Usage
1 | ApplyLasso(data, pred_pos = NULL)
|
Arguments
data |
a data.frame or a matrix with n rows (=time points) and p columns (=genes) containing the gene expression time series. |
pred_pos |
indices of the potential predictor variables in the dataset. If the value is NULL, all variables are considered as potential predictors. |
Value
a matrix of Lasso regression coefficients.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
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