Generate simulated data within a variety of jSDM methodologies

The jsdm_sim_data function can simulate data with either a multivariate generalised mixed model (MGLMM) or a generalised linear latent variable model (GLLVM). The gllvm_sim_data and mglmm_sim_data are aliases for jsdm_sim_data that set method to "gllvm" and "mglmm" respectively.

Usage

jsdm_sim_data(
  S,
  N = NULL,
  D = NULL,
  K = 0L,
  family,
  method = c("gllvm", "mglmm"),
  species_intercept = TRUE,
  Ntrials = NULL,
  site_intercept = "none",
  beta_param = "unstruct",
  zi_param = "constant",
  zi_k = NULL,
  shp_param = "constant",
  shp_k = NULL,
  prior = jsdm_prior(),
  X = NULL,
  zi_X = NULL,
  shp_X = NULL
)

gllvm_sim_data(...)

mglmm_sim_data(...)

Arguments

S: is number of species
N: is number of sites
D: is number of latent variables, used within gllvm method
K: is number of covariates, by default 0
family: is the response family, must be one of "gaussian", "neg_binomial", "poisson", "binomial", "bernoulli", "zi_poisson", or "zi_neg_binomial". Regular expression matching is supported.
method: is the jSDM method to use, currently either "gllvm" or "mglmm" - see details for more information.
species_intercept: Whether to include an intercept in the predictors, must be TRUE if K is 0. Defaults to TRUE.
Ntrials: For the binomial distribution the number of trials, given as either a single integer which is assumed to be constant across sites or as a site-length vector of integers.
site_intercept: Whether a site intercept should be included, potential values "none" (no site intercept) or "ungrouped" (site intercept with no grouping). Defaults to no site intercept, grouped is not supported currently.
beta_param: The parameterisation of the environmental covariate effects, by default "unstruct". See details for further information.
zi_param: For the zero-inflated families, whether the zero-inflation parameter is a species-specific constant (default, "constant"), or varies by environmental covariates ("covariate").
zi_k: If zi_param="covariate", the number of environmental covariates that the zero-inflation parameter responds to. The default (NULL) is that the zero-inflation parameter responds to exactly the same covariate matrix as the mean parameter. Otherwise, a different set of random environmental covariates are generated, plus an intercept (not included in zi_k) and used to predict zero-inflation. Will be ignored if zi_X is supplied.
shp_param: For families with shape parameters, whether the shape parameter is a species-specific constant (default, "constant"), or varies by environmental covariates ("covariate").
shp_k: If shp_param="covariate", the number of environmental covariates that the shape parameter responds to. The default (NULL) is that the shape parameter responds to exactly the same covariate matrix as the mean parameter. Otherwise, a different set of random environmental covariates are generated and used to predict the shape parameter. Will be ignored if shp_X is supplied.
prior: Set of prior specifications from call to jsdm_prior()
X: The X matrix to be used to simulate data, by default NULL - i.e. the X matrix is simulated using a random draw from a standard normal distribution.
zi_X: The zi_X matrix to be used to simulate data, by default NULL - i.e. the zi_X matrix is simulated using a random draw from a standard normal distribution.
shp_X: The shp_X matrix to be used to simulate data, by default NULL - i.e. the shp_X matrix is simulated using a random draw from a standard normal distribution.
...: Arguments passed to jsdm_sim_data

Details

This simulates data based on a joint species distribution model with either a generalised linear latent variable model approach or a multivariate generalised linear mixed model approach.

Models can be fit with or without "measured predictors", and if measured predictors are included then the species have species-specific parameter estimates. These can either be simulated completely independently, or have information pooled across species. If information is pooled this can be modelled as either a random draw from some mean and standard deviation or species covariance can be modelled together (this will be the covariance used in the overall model if the method used has covariance).

Environmental covariate effects ("betas") can be parameterised in two ways. With the "cor" parameterisation all covariate effects are assumed to be constrained by a correlation matrix between the covariates. With the "unstruct" parameterisation all covariate effects are assumed to draw from a simple distribution with no correlation structure. Both parameterisations can be modified using the prior object.

Functions

gllvm_sim_data(): Alias for jsdm_sim_data with method = "gllvm"
mglmm_sim_data(): Alias for jsdm_sim_data with method = "mglmm"