Fit a zero-inflated negative-binomial model with spatial Gaussian-process random effects in only the count component.
Usage
ZINB_GP_spatial_count(
X,
y,
Vs,
Ds,
nsim,
burn,
thin = 1,
save_ypred = FALSE,
print_iter = 100,
print_progress = FALSE,
lsPrior = NULL,
sigmaPrior = NULL,
noisePrior = NULL,
mh_sd_r = NULL,
kern = NULL
)Arguments
- X
Fixed-effect design matrix with N rows.
- y
Non-negative integer count response of length N.
- Vs
Sparse or dense spatial random-effect design matrix. It should have N rows and one column per spatial location.
- Ds
Spatial distance matrix with one row and column per full spatial level, including the baseline level omitted from
Vs. Diagonal entries must be zero.- nsim
Total number of MCMC iterations.
- burn
Number of burn-in iterations.
- thin
Store every thin-th iteration after burn-in.
- save_ypred
Whether to save posterior predictive counts and at-risk draws.
- print_iter
Print progress every print_iter iterations.
- print_progress
Whether to print MCMC progress.
- lsPrior
Prior and proposal controls for spatial GP length scales.
- sigmaPrior
Inverse-gamma prior parameters for GP variances.
- noisePrior
Beta prior and MH controls for GP noise ratios.
- mh_sd_r
Proposal standard deviation for NB dispersion r.
- kern
Kernel function accepting a squared-distance matrix and a length scale.