simulate_new: Simulate from covariate/metadata in the absence of a real...

In glmmTMB: Generalized Linear Mixed Models using Template Model Builder

Simulate from covariate/metadata in the absence of a real data set (EXPERIMENTAL)

Description

See vignette("sim", package = "glmmTMB") for more details and examples, and vignette("covstruct", package = "glmmTMB") for more information on the parameterization of different covariance structures.

Usage

simulate_new(
  object,
  nsim = 1,
  seed = NULL,
  newdata,
  newparams,
  ...,
  show_pars = FALSE
)

Arguments

object	a one-sided model formula (e.g. ~ a + b + c (peculiar naming is for consistency with the generic function, which typically takes a fitted model object)
nsim	number of simulations
seed	random-number seed
newdata	a data frame containing all variables listed in the formula, including the response variable (which needs to fall within the domain of the conditional distribution, and should probably not be all zeros, but whose value is otherwise irrelevant)
newparams	a list of parameters containing sub-vectors (beta, betazi, betad, theta, etc.) to be used in the model
...	other arguments to glmmTMB (e.g. family)
show_pars	(logical) print structure of parameter vector and stop without simulating?

Examples

## use Salamanders data for structure/covariates
simulate_new(~ mined + (1|site),
             zi = ~ mined,
             newdata = Salamanders, show_pars  = TRUE)
sim_count <- simulate_new(~ mined + (1|site),
             newdata = Salamanders,
             zi = ~ mined,
             family = nbinom2,
             newparams = list(beta = c(2, 1),
                         betazi = c(-0.5, 0.5), ## logit-linear model for zi
                         betad = log(2), ## log(NB dispersion)
                         theta = log(1)) ## log(among-site SD)
)
head(sim_count[[1]])

glmmTMB documentation built on Oct. 7, 2023, 5:07 p.m.