screen.wgtd.elasticnet | R Documentation |
Performs feature selection via cv.glmnet
.
screen.wgtd.elasticnet(
Y,
X,
family,
obsWeights,
id,
alpha = 0.5,
k = 2,
nfolds = 10,
nlambda = 100,
...
)
screen.wgtd.lasso(..., alpha = 1)
Y |
Outcome (numeric vector). See |
X |
Predictor variable(s) (data.frame or matrix). See
|
family |
Error distribution to be used in the model:
|
obsWeights |
Optional numeric vector of observation weights. See
|
id |
Cluster identification variable. Currently unused. |
alpha |
The elasticnet mixing parameter. Default for
|
k |
Minimum number of features to select. Only used if fewer than this
number of features are selected using the optimal |
nfolds |
Number of cross-validation folds to use when choosing optimal
|
nlambda |
Number of |
... |
Currently unused. |
A logical vector with length equal to ncol(X)
# based on example in SuperLearner package
set.seed(1)
n <- 100
p <- 20
X <- matrix(rnorm(n*p), nrow = n, ncol = p)
X <- data.frame(X)
Y <- X[, 1] + sqrt(abs(X[, 2] * X[, 3])) + X[, 2] - X[, 3] + rnorm(n)
obsWeights <- 1/runif(n)
screen.wgtd.elasticnet(Y, X, gaussian(), obsWeights, seq(n), k = 3)
screen.wgtd.lasso(Y, X, gaussian(), obsWeights, seq(n), k = 3)
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