predict.cv.pcLasso {pcLasso} | R Documentation |
Make predictions from a "cv.pcLasso" object
Description
This function returns the predictions for a new data matrix from a
cross-validated pcLasso model by using the stored "glmfit
" object and
the optimal value chosen for lambda
.
Usage
## S3 method for class 'cv.pcLasso'
predict(object, xnew, s = c("lambda.1se",
"lambda.min"), ...)
Arguments
object |
Fitted " |
xnew |
Matrix of new values for |
s |
Value of the penalty parameter |
... |
Potentially other arguments to be passed to and from methods; currently not in use. |
Details
This function makes it easier to use the results of cross-validation to make
a prediction. Note that xnew
should have the same number of columns as
the original feature space, regardless of whether the groups are overlapping
or not.
Value
Predictions which the cross-validated model makes for xnew
at
the optimal value of lambda
. Note that the default is the "lambda.1se" for lambda,
to make this function consistent with cv.glmnet
in the glmnet
package. The output is predictions of E(y|xnew)
: these are probabilities
for the binomial family.
See Also
cv.pcLasso
and predict.pcLasso
.
Examples
set.seed(1)
x <- matrix(rnorm(100 * 20), 100, 20)
y <- rnorm(100)
cvfit <- cv.pcLasso(x, y, ratio = 0.8)
predict(cvfit, xnew = x[1:5, ])
predict(cvfit, xnew = x[1:5, ], s = "lambda.min")