Description Usage Arguments See Also Examples
View source: R/specify_variables.R
Lasso screener for SuperLearner()
that always retains specified variables and passes
approximately nVar
variables to SuperLearner()
. When the number of non-zero
coefficients exceeds nVar
, a larger value of the regularization parameter lambda
is chosen to select a smaller set of variables that excludes the ties.
1 2 | screen.glmnet.fix(Y, X, family, alpha = 1, minscreen = 2, nVar = 10,
nfolds = 10, nlambda = 100, fixed.var.index = var.index, ...)
|
Y |
outcome variable (specified in |
X |
data frame |
nVar |
number of non-zero variables to be selected |
var.index |
indices of variables to always be included by the screener |
See glmnet
for additional details on implementing lasso
1 2 3 4 |
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.