Predict Method for fitted MID Models

predict() methods for obtaining predictions from a fitted MID model ("mid") or a collection of MID models ("mids"). It can be used to predict on new data or to retrieve the fitted values from the original data.

Usage

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

# S3 method for class 'mids'
predict(object, ...)

Arguments

object: a fitted model object of class "mid", or a collection object ("mids") to be used for prediction.
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.
...: further arguments passed to or from other methods.

Value

For a single "mid" object, predict.mid() returns a numeric vector if type is "response" or "link", or a numeric matrix if type = "terms".

For a collection ("mids"), predict.mids() returns a numeric matrix where each column corresponds to a model if type is "response" or "link", or a list of numeric matrices if type = "terms".

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

data(airquality, package = "datasets")
test <- 1:10
mid <- interpret(Ozone ~ .^2, airquality[-test, ], lambda = 1, link = "log")
#> 'model' not passed: response variable in 'data' is used

# Predict on new data
predict(mid, airquality[test, ])
#>  [1] 20.567319 13.072444 15.205206 11.577466  7.076900 12.749324 13.802332
#>  [8]  9.219133  6.915020 18.056718

# Get predictions on the link scale
predict(mid, airquality[test, ], type = "link")
#>  [1] 3.023703 2.570507 2.721638 2.449061 1.956836 2.545478 2.624838 2.221281
#>  [9] 1.933696 2.893518

# Get the contributions of specific terms
predict(mid, airquality[test, ], terms = c("Temp", "Wind"), type = "terms")
#>             Temp        Wind
#>  [1,] -0.4407593  0.03339958
#>  [2,] -0.4721933 -0.07545538
#>  [3,] -0.4522206 -0.08764899
#>  [4,] -0.7351731 -0.11169699
#>  [5,] -1.0414042 -0.30023272
#>  [6,] -0.4872913 -0.25282426
#>  [7,] -0.5338234 -0.10880959
#>  [8,] -0.9269931 -0.16332033
#>  [9,] -0.8125819 -0.08896022
#> [10,] -0.4332497 -0.10880959
#> attr(,"constant")
#> [1] 3.44554