predict.cv.hd2part: Prediction function for fitted cross validation hd2part...

In jaredhuling/personalized2part: Two-Part Estimation of Treatment Rules for Semi-Continuous Data

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)

jaredhuling/personalized2part documentation built on Sept. 16, 2020, 1:41 p.m.