species_mix.simulate | R Documentation |
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"
)
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'. |
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")
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