- formula
A formula for the covariate part of the model using the syntax of the
lm() function. Offsets can be included here using the offset() function. The
response, offset and each covariate are vectors of length K*1. The response can
contain missing (NA) values.
- formula.omega
A one-sided formula object with no response variable (left side of the "~")
needed, specifying the covariates in the logistic regression model for
modelling the probability of an observation being a structural zero. Each
covariate (or an offset) needs to be a vector of length K*1. Only required for
zero-inflated Poisson models.
- family
One of either "binomial","poisson" or "zip", which respectively specify a binomial
likelihood model with a logistic link function, a Poisson likelihood model with a
log link function, or a zero-inflated Poisson model with a log link function.
- data
An optional data.frame containing the variables in the formula.
- trials
A vector the same length as the response containing the total number of trials
for each area. Only used if family="binomial".
- W
A non-negative K by K neighbourhood matrix (where K is the number of spatial units).
Typically a binary specification is used, where the jkth element equals one if
areas (j, k) are spatially close (e.g. share a common border) and is zero otherwise.
The matrix can be non-binary, but each row must contain at least one non-zero entry.
- burnin
The number of MCMC samples to discard as the burn-in period in each chain.
- n.sample
The overall number of MCMC samples to generate in each chain.
- thin
The level of thinning to apply to the MCMC samples in each chain to reduce their
autocorrelation. Defaults to 1 (no thinning).
- n.chains
The number of MCMC chains to run when fitting the model. Defaults to 1.
- n.cores
The number of computer cores to run the MCMC chains on. Must be less than or
equal to n.chains. Defaults to 1.
- prior.mean.beta
A vector of prior means for the regression parameters beta (Gaussian priors are
assumed). Defaults to a vector of zeros.
- prior.var.beta
A vector of prior variances for the regression parameters beta (Gaussian priors
are assumed). Defaults to a vector with values 100,000.
- prior.tau2
The prior shape and scale in the form of c(shape, scale) for an Inverse-Gamma(shape, scale)
prior for tau2. Defaults to c(1, 0.01).
- prior.sigma2
The prior shape and scale in the form of c(shape, scale) for an Inverse-Gamma(shape, scale)
prior for sigma2. Defaults to c(1, 0.01).
- prior.mean.delta
A vector of prior means for the regression parameters delta (Gaussian priors are
assumed) for the zero probability logistic regression component of the model.
Defaults to a vector of zeros. Only used if family="multinomial".
- prior.var.delta
A vector of prior variances for the regression parameters delta (Gaussian priors
are assumed) for the zero probability logistic regression component of the model.
Defaults to a vector with values 100,000. Only used if family="multinomial".
- MALA
Logical, should the function use Metropolis adjusted Langevin algorithm (MALA)
updates (TRUE, default) or simple random walk updates (FALSE) for the regression
parameters.
- verbose
Logical, should the function update the user on its progress.