Stacked Regression

This function combines one or more existing prediction models into a so/called meta-model.

Usage

stackedglm(models, family = binomial, data)

Arguments

models

a list containing the historical prediction models, which can be defined in several ways. For instance, historical regression models can be specified using a named vector containing the regression coefficients of the individual predictors (no need to include the intercept term). List items may also represent an object for which the function predict() exists.

family

a description of the error distribution and link function to be used in the meta-model. This can be a character string naming a family function, a family function or the result of a call to a family function. (See family for details of family functions.)

data

an optional data frame, list or environment (or object coercible by as.data.frame to a data frame) containing the variables in the model. If not found in data, the variables are taken from environment(formula), typically the environment from which stackedglm is called.

Author

Thomas Debray <thomas.debray@gmail.com>