MCMC Sampling
1 2 3 4 5 6 | mcmc_sampling(dataset, alg, nsamp, nburnin = 0, nsubsamp = 1, ngrid = 100,
nugget = "1,1", prec_alpha = 0.01, prec_beta = 0.01, TrjL = NULL,
Nleap = NULL, szkappa = NULL, rand_leap = NULL, f_init = rep(1, ngrid
- 1), kappa = 1, covariates = NULL, betas = rep(0, 2 +
length(covariates)), samp_alg = "none", kappa_alg = "gibbs",
beta_vars = rep(100, length(betas)), printevery = 100)
|
dataset |
|
alg |
string selecting which MCMC sampler to use. Options are "HMC", "splitHMC", "MALA", "aMALA", and "ESS". |
nsamp |
integer number of MCMC steps to compute. |
nburnin |
integer number of MCMC steps to discard as burn-in. |
nsubsamp |
integer after burn-in, how often to record a step to the output. |
ngrid |
integer number of grid point in the latent field. |
nugget |
string selecting which "nugget" adjustment to apply to the precision matrix to make it full-rank. Options are '1,1' for an adjustment to the first element, 'diag' for an adjustment to the entire main diagonal, or 'none' which may result in a non-full-rank precision matrix. |
prec_alpha, prec_beta |
numeric shape and rate parameters for the prior on precision. |
TrjL |
numeric tuning parameter. |
Nleap |
integer tuning parameter. |
szkappa |
numeric tuning parameter. |
rand_leap |
logical tuning parameter. |
f_init |
numeric vector starting log effective population size values. |
kappa |
numeric starting kappa. |
covariates |
list of functions representing covariate trajectories that (may) influence sampling frequency. |
betas |
numeric vector of starting values for the beta hyperparameters. |
samp_alg |
string selecting sampling algorithm for sampling time intensity coefficients. One of "none" (default), "fixed", "MH", and "ESS". |
kappa_alg |
selects sampling algorithm for kappa. One of "gibbs" (default) or "whiten". |
beta_vars |
numeric vector prior variances of the beta hyperparameters. |
printevery |
integer how many MCMC steps between writing output to the console. |
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