predict.mid: Predict Method for fitted MID Models

Description

predict.mid() is an S3 method for "mid" objects that obtains predictions from a fitted MID model. It can be used to predict on new data or to retrieve the fitted values from the original data.

Usage

# S3 method for mid
predict(
  object,
  newdata = NULL,
  na.action = "na.pass",
  type = c("response", "link", "terms"),
  terms = object$terms,
  ...
)

Value

predict.mid() returns a numeric vector of MID model predictions, or a matrix if type = "terms".

Arguments

object: a "mid" object to be used to make predictions.
newdata: a data frame of the new observations. If NULL, the original fitted values are extracted and returned.
na.action: a function or character string specifying what should happen when the data contain NA values.
type: the type of prediction required. One of "response", "link", or "terms".
terms: a character vector of term labels, specifying a subset of component functions to use for predictions.
...: arguments to be passed to other methods (not used in this method).

Details

The type argument allows you to specify the scale of the prediction. By default (type = "response"), the function returns predictions on the original scale of the response variable. Alternatively, you can obtain predictions on the scale of the linear predictor by setting type = "link". For a detailed breakdown, setting type = "terms" returns a matrix where each column represents the contribution of a specific model term on the linear predictor scale.

The terms argument allows for predictions based on a subset of the model's component functions, excluding others.

Examples

Run this code

data(airquality, package = "datasets")
test <- 1:10
mid <- interpret(Ozone ~ .^2, airquality[-test, ], lambda = 1, link = "log")

# Predict on new data
predict(mid, airquality[test, ])

# Get predictions on the link scale
predict(mid, airquality[test, ], type = "link")

# Get the contributions of specific terms
predict(mid, airquality[test, ], terms = c("Temp", "Wind"), type = "terms")