predict.pre {pre} | R Documentation |
Predicted values based on final prediction rule ensemble
Description
predict.pre
generates predictions based on the final prediction rule
ensemble, for training or new (test) observations
Usage
## S3 method for class 'pre'
predict(
object,
newdata = NULL,
type = "link",
penalty.par.val = "lambda.1se",
...
)
Arguments
object |
object of class pre .
|
newdata |
optional data.frame of new (test) observations, including all
predictor variables used for deriving the prediction rule ensemble.
|
type |
character string. The type of prediction required; the default
type = "link" is on the scale of the linear predictors. Alternatively,
for count and factor outputs, type = "response" may be specified to obtain
the fitted mean and fitted probabilities, respectively; type = "class"
returns the predicted class membership.
|
penalty.par.val |
character or numeric. Value of the penalty parameter
\lambda to be employed for selecting the final ensemble. The default
"lambda.min" employs the \lambda value within 1 standard
error of the minimum cross-validated error. Alternatively,
"lambda.min" may be specified, to employ the \lambda value
with minimum cross-validated error, or a numeric value >0 may be
specified, with higher values yielding a sparser ensemble. To evaluate the
trade-off between accuracy and sparsity of the final ensemble, inspect
pre_object$glmnet.fit and plot(pre_object$glmnet.fit) .
|
... |
further arguments to be passed to
predict.cv.glmnet .
|
Details
If newdata
is not provided, predictions for training data will be
returned.
See Also
pre
, plot.pre
,
coef.pre
, importance.pre
, cvpre
,
interact
, print.pre
,
predict.cv.glmnet
Examples
set.seed(1)
train <- sample(1:sum(complete.cases(airquality)), size = 100)
set.seed(42)
airq.ens <- pre(Ozone ~ ., data = airquality[complete.cases(airquality),][train,])
predict(airq.ens)
predict(airq.ens, newdata = airquality[complete.cases(airquality),][-train,])
[Package
pre version 1.0.7
Index]