Function estimates coefficients based on elastic net regularization.
enetEstimation(
x,
response,
control = list(alpha = 0.5, s = "lambda.min", family = "gaussian", grouped = FALSE),
...
)Result is a list with coefficients, coefficient names and the model intercept.
An object of type DocumentTermMatrix.
Response variable including the given gold standard.
(optional) A list of parameters defining the model as follows:
"alpha"Abstraction parameter for switching between LASSO
and ridge regularization (with default alpha=0.5).
Best option is to loop over this parameter and test different alternatives.
"s"Value of the parameter lambda at which the elastic net is evaluated. Default
is s="lambda.1se" which takes the calculated minimum value for \(\lambda\)
and then subtracts one standard error in order to avoid overfitting. This often
results in a better performance than using the minimum value itself given by
lambda="lambda.min".
"family"Distribution for response variable. Default is family="gaussian".
For non-negative counts, use family="poisson". For binary variables
family="binomial". See glmnet for further details.
"grouped" Determines whether grouped function is used (with default FALSE).
Additional parameters passed to function for glmnet.