simulate_gls: Simulate fitted values from an object of class 'gls'

In nlraa: Nonlinear Regression for Agricultural Applications

Simulate fitted values from an object of class gls

Description

Simulate values from an object of class gls. Unequal variances, as modeled using the ‘weights’ option are supported, and there is experimental code for dealing with the ‘correlation’ structure. This generates just one simulation from these type of models. To generate multiple simulations use simulate_lme

Usage

simulate_gls(
  object,
  psim = 1,
  na.action = na.fail,
  naPattern = NULL,
  data = NULL,
  ...
)

Arguments

object	object of class gls
psim	parameter simulation level, 0: for fitted values, 1: for simulation from fixed parameters (assuming a fixed vcov matrix), 2: for simulation considering the uncertainty in the residual standard error (sigma), this returns data which will appear similar to the observed values
na.action	default ‘na.fail’. See predict.gls
naPattern	missing value pattern. See predict.gls
data	the data argument is needed when using this function inside user defined functions. It should be identical to the data used to fit the model.
...	additional arguments (it is possible to supply a newdata this way)

Details

This function is based on predict.gls function

It uses function mvrnorm to generate new values for the coefficients of the model using the Variance-Covariance matrix vcov. This variance-covariance matrix refers to the one for the parameters ‘beta’, not the one for the residuals.

Value

It returns a vector with simulated values with length equal to the number of rows in the original data

See Also

predict.gls simulate_lme

Examples

require(nlme)
data(Orange)

fit.gls <- gls(circumference ~ age, data = Orange, 
               weights = varPower())

## Visualize covariance matrix
fit.gls.vc <- var_cov(fit.gls)
image(log(fit.gls.vc[,ncol(fit.gls.vc):1]))

sim <- simulate_gls(fit.gls)

nlraa documentation built on May 29, 2024, 2:01 a.m.