predict.cv.hd2part {personalized2part} | R Documentation |
Prediction function for fitted cross validation hd2part objects
Description
Prediction function for fitted cross validation hd2part objects
Usage
## S3 method for class 'cv.hd2part'
predict(
object,
newx,
model = c("zero", "positive"),
s = c("lambda.min", "lambda.1se"),
type = c("link", "model_response", "response", "coefficients", "nonzero"),
...
)
Arguments
object |
fitted "cv.hd2part" model object
|
newx |
Matrix of new values for x at which predictions are to be made. Must be a matrix; can be sparse as in the
CsparseMatrix objects of the Matrix package
This argument is not used for type = c("coefficients","nonzero")
|
model |
either "zero" for the zero part model or "positive" for the positive part model
|
s |
Value(s) of the penalty parameter lambda at which predictions are required. Default is the entire sequence used to create
the model. For predict.cv.hd2part() , can also specify "lambda.1se" or "lambda.min" for best lambdas estimated
by cross validation.
|
type |
Type of prediction required. type = "link" gives the linear predictors;
type = "model_response" gives the fitted probabilities for the zero part and fitted expected values for the positive part.
type = "response" gives the combined response prediction across the two models using the full unconditional expected
value of the response. When type = "response" , argument "model" is unused.
type = "coefficients" computes the coefficients at the requested values for s .
|
... |
arguments to be passed to predict.hd2part
|
Examples
set.seed(123)
[Package
personalized2part version 0.0.1
Index]