glmmTMB (version 1.1.10)

simulate_new: 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,
  family = gaussian,
  newdata,
  newparams,
  ...,
  return_val = c("sim", "pars", "object")
)

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

family

a family function, a character string naming a family function, or the result of a call to a family function (variance/link function) information. See family for a generic discussion of families or family_glmmTMB for details of glmmTMB-specific families.

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, betadisp, theta, etc.) to be used in the model. If b is specified in this list, then the conditional modes/BLUPs will be set to these values; otherwise they will be drawn from the appropriate Normal distribution

...

other arguments to glmmTMB (e.g. family)

return_val

what information to return: "sim" (the default) returns a list of vectors of simulated outcomes; "pars" returns the default parameter vector (this variant does not require newparams to be specified, and is useful for figuring out the appropriate dimensions of the different parameter vectors); "object" returns a fake glmmTMB object (useful, e.g., for retrieving the Z matrix (getME(simulate_new(...), "Z")) or covariance matrices (VarCorr(simulate_new(...))) implied by a particular set of input data and parameter values)

Examples

## use Salamanders data for structure/covariates
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
                         betadisp = log(2), ## log(NB dispersion)
                         theta = log(1)) ## log(among-site SD)
)
sim_obj <- simulate_new(~ mined + (1|site),
            return_val = "object",
             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)
)
data("sleepstudy", package = "lme4")
sim_obj <- simulate_new(~ 1 + (1|Subject) + ar1(0 + factor(Days)|Subject),
             return_val = "pars",
             newdata = sleepstudy,
             family = gaussian,
             newparams = list(beta = c(280, 1),
                         betad = log(2), ## log(SD)
                         theta = log(c(2, 2, 1))),
)