Simulate species mix data for model fitting.

Simulate species mix data for model fitting.

species_mix.simulate(
  archetype_formula,
  species_formula,
  all_formula = NULL,
  data,
  offset = NULL,
  nArchetypes = 3,
  alpha = NULL,
  beta = NULL,
  gamma = NULL,
  delta = NULL,
  logTheta = NULL,
  powers = NULL,
  size = NULL,
  family = "bernoulli"
)

Arguments

archetype_formula	formula to simulate species_mix data, needs to have the format: cbind(spp1,spp2,spp3,...,sppN)~1 + x1 + x2
species_formula	formula to simulate species_mix species-specific responses, e.g: ~1
all_formula	formula to simulate biases in the data
data	a matrix of variables to simulate data from.
offset	used to offset sampling effort for abundance data (log link function).
nArchetypes	number of groups to simulate.
alpha	coefficients for each species archetype. vector S long.
beta	coefficients for each species archetype. Matrix of G x number of parameters. Each row is a different species archetype.
gamma	coefficients for each species archetype. Matrix of S x number of parameters. Each row is a different species archetype.
delta	coefficients for all_formula, these should describe overall biases in the dataset.
logTheta	coefficients for the dispersion variables for negative.binomial and gaussian distributions - should be number of species long and on the natural log scale.
powers	Is the power parameter for Tweedie distribution.
size	Is for the binomial model and this represents the number of binomial trials per site, can be fixed or vary.
family	Which statistical distribution to simulate data for. 'bernoulli','binomial', 'gaussian', 'ippm', 'negative.binomial' and 'poisson'.

Examples

# \donttest{
archetype_formula <- stats::as.formula(paste0('cbind(',paste(paste0('spp',
1:20),collapse = ','),")~1+x1+x2"))
species_formula <- stats::as.formula(~1)
beta <- matrix(c(-3.6,0.5,
                 -0.9,1.0,
                  0.9,-2.9,
                  2.2,5.4),
                4,2,byrow=TRUE)
dat <- data.frame(y=rep(1,100),
                  x1=stats::runif(100,0,2.5),
                  x2=stats::rnorm(100,0,2.5))
simulated_data <- species_mix.simulate(archetype_formula,species_formula,
                                       data=dat, nArchetypes = 4, beta=beta,
                                       family="bernoulli")
#> Random alpha from normal (-1,0.5) distribution
# }