st_tgmrf | R Documentation |
Uses this function to fit spatio-temporal data using non-guassian copulas.
st_tgmrf(y, X, n_reg, n_var, beta, nu, eps, mu, rho_s, rho_t, rho_st, tau, family, type, mat_type, method, nsim, burnin, thin, E, n, neigh, prior_param, MCMC_config, fix_rho, range, verbose, c_beta, c_eps, c_mu, c_nu, c_rho, conj_beta)
y |
Response variable |
X |
Matrix or data.frame of covariates |
n_reg |
Number of regions in study |
n_var |
Number of variables in study |
beta |
Vector of the initial values to coeficients vector |
nu |
A Initial value to variance of copula |
rho_s |
Spatial dependence parameter |
rho_t |
Temporal dependence parameter |
rho_st |
Spatio-temporal dependence parameter |
tau |
Vector of precisions of variables |
family |
'Poisson' or 'Binary' |
type |
Depends of family. 'lognormal', 'lognormal-precision', 'gamma-shape', 'gamma-scale', 'weibull-shape', 'weibull-scale' for Poisson family 'beta-logit', 'beta-probit', 'beta-alpha', 'beta-beta' for Binomial family |
mat_type |
car or leroux |
method |
'arms' or 'metropolis' |
nsim |
Number of MCMC iterations |
burnin |
number of discards iterations |
thin |
Lag to collect the observations |
E |
Offsets for Poisson data |
n |
Number of trials in Binomial data |
neigh |
Neighborhood structure of nb class or a neighborhood matrix |
prior_param |
List of priors parameters. Gaussian for beta vector and IGamma for nu. Entrys are: 'nu' and 'beta' |
MCMC_config |
Parameters to tunning MCMC |
fix_rho |
A list informing if any rho (rho_s, rho_t, rho_st) is fixed |
range |
Range to use during the MCMC. Entrys are: 'rho_s', 'rho_t' and 'rho_st' |
range |
A list informing a range for sampling rho (rho_s, rho_t, rho_st). Each entre must be a vector in the interval (-1, 1). |
beta A matrix with samples of beta
nu A vector with samples of nu
rho A vector with samples of rho
s_tgmrf(1,1,1,1)
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