distfreereg (version 1.1)

update.distfreereg: Update distfreereg objects

Description

This is an update method for objects of class distfreereg. The method takes advantage of the override argument of distfreereg to prevent unnecessary recalculation of potentially computationally expensive objects.

Usage

# S3 method for distfreereg
update(object, ..., smart = TRUE, envir = parent.frame())

Value

An updated object of class distfreereg.

Arguments

object

Object of class distfreereg.

...

Additional named parameters to pass to distfreereg.

smart

Logical. If TRUE, then saved values from object are passed to distfreereg using the override argument, when they need not themselves be updated. See details.

envir

Environment passed to eval when evaluating modified call.

Author

Jesse Miller

Details

This function updates an object of class distfreereg. By default, it does so "intelligently" in the sense that it does not unnecessarily recompute elements that are already saved in object. For example, if a new value for covariance is not included in ..., then the value of covariance saved in object is automatically passed to the new call, preventing recalculating Q. If a new value of covariance is specified, then all objects dependent on that (e.g., \(\hat\theta\)) are recomputed.

In particular, the simulated samples depend on the data and function only through the number of observations, the covariates (if any), and the dimension of the parameter space of the function. If none of these change, then the updated object reuses the simulated samples from the supplied object.

The price paid for this efficiency is a potentially "large" value of call in the updated object.

See Also

distfreereg

Examples

set.seed(20240218)
n <- 1e2
func <- function(X, theta) X[,1]^theta[1] + theta[2]*X[,2]
Sig <- runif(n, min = 1, max = 3)
theta <- c(2,5)
X <- matrix(runif(2*n, min = 1, max = 5), nrow = n)
Y <- X[,1]^theta[1] + theta[2]*X[,2] + rnorm(n, sd = sqrt(Sig))
dfr_1 <- distfreereg(Y = Y, X = X, test_mean = func,
                     covariance = list(Sigma = Sig),
                     theta_init = c(1,1))

func_updated <- function(X, theta) X[,1]^theta[1] + theta[2]*X[,2]^2
dfr_2 <- update(dfr_1, test_mean = func_updated)