dss_select_lasso: Lasso for matrix regression
In drkowal/dfosr: Dynamic Function-on-Scalars Regression
Description
Usage
Arguments
Value
View source: R/variable_selection_functions.R
Description
Given a N x M data matrix Y
and a N x P matrix of predictor X,
estimate the model
Y = XB + E
for P x M regression coefficient matrix B.
The penalty applies a lasso penalty to all elements of B.
Usage
1
dss_select_lasso(Y, X, W = NULL)
Arguments
Y
N x M matrix of observations
X
N x P matrix of predictors
W
P x M matrix of weights
Value
The solution path for L values of lambda in the form
of an array of dimension M x P x L
drkowal/dfosr documentation built on May 7, 2020, 3:09 p.m.
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Description Usage Arguments Value
View source: R/variable_selection_functions.R
Description
Given a N x M data matrix Y
and a N x P matrix of predictor X,
estimate the model
Y = XB + E
for P x M regression coefficient matrix B.
The penalty applies a lasso penalty to all elements of B.
Usage
1 | dss_select_lasso(Y, X, W = NULL)
|
Arguments
Y |
|
X |
|
W |
|
Value
The solution path for L values of lambda in the form
of an array of dimension M x P x L
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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