Compute the model expectations

Within this package, the predict method computes the expectations of a particular model provided to object using the data provided to data. It plays a pivotal role in both the estimation and simulation of the models provided in this package.

Usage

# S4 method for class 'exponential'
predict(object, data)

# S4 method for class 'quasi_hyperbolic'
predict(object, data)

# S4 method for class 'double_exponential'
predict(object, data)

Arguments

object

Instance of the model-class

data

Instance of the dataset-class

Value

Instance of the dataset-class where the slot y contains the predictions of the model

Examples

# Create an exponential discounting model
my_model <- exponential(
  parameters = list(
    "alpha" = 0,
    "beta" = 1,
    "gamma" = 0.5
  ),
  covariance = 1
)
#> Warning: The parameter "gamma" should be a matrix: Changing type.
#> Warning: The parameter "beta" should be a matrix: Changing type assuming a single independent variable.
#> Warning: The argument "covariance" should be a matrix: Changing type.

# Create an instance of the dataset with only predictor values. It will throw
# a warning because Y is implied to be empty, but we capture this warning 
# with suppressWarnings()
data <- dataset(
  X = matrix(
    rnorm(10),
    nrow = 10,
    ncol = 1
  )
) |>
  suppressWarnings()

# Compute the values of Y as expected by the exponential discounting model 
# defined in my_model
predict(
  my_model,
  data
)
#> An object of class "dataset"
#> 
#> Slot "Y": 10x1matrix
#>             [,1]
#> [1,] -0.43043027
#> [2,] -0.01072361
#> [3,]  0.16383291
#> [4,]  0.07244100
#> [5,]  0.75006434
#> [6,]  1.19169966
#> 
#> Slot "X": 10x1matrix
#>              [,1]
#> [1,] -0.430430267
#> [2,]  0.204491526
#> [3,]  0.169194719
#> [4,] -0.009475454
#> [5,]  0.713843838
#> [6,]  0.816667486