# check_diagnostics: Quick Diagnostics Checks for 'run_mcmc' Output In bssm: Bayesian Inference of Non-Linear and Non-Gaussian State Space Models

 check_diagnostics R Documentation

## Quick Diagnostics Checks for run_mcmc Output

### Description

Prints out the acceptance rate, smallest effective sample sizes (ESS) and largest Rhat values for a quick first check that the sampling worked. For further checks, see e.g. bayesplot and coda packages.

### Usage

check_diagnostics(x)


### Arguments

 x Results object of class mcmc_output from run_mcmc.

### Details

For methods other than IS-MCMC, the estimates are based on the improved diagnostics from the posterior package.For IS-MCMC, these Rhat, bulk-ESS, and tail-ESS estimates are based on the approximate posterior which should look reasonable, otherwise the IS-correction does not make much sense. For IS-MCMC, ESS estimates based on a weighted posterior are also computed.

### Examples

set.seed(1)
n <- 30
phi <- 2
rho <- 0.9
sigma <- 0.1
beta <- 0.5
u <- rexp(n, 0.1)
x <- rnorm(n)
z <- y <- numeric(n)
z[1] <- rnorm(1, 0, sigma / sqrt(1 - rho^2))
y[1] <- rnbinom(1, mu = u * exp(beta * x[1] + z[1]), size = phi)
for(i in 2:n) {
z[i] <- rnorm(1, rho * z[i - 1], sigma)
y[i] <- rnbinom(1, mu = u * exp(beta * x[i] + z[i]), size = phi)
}

model <- ar1_ng(y, rho = uniform_prior(0.9, 0, 1),
sigma = gamma_prior(0.1, 2, 10), mu = 0.,
phi = gamma_prior(2, 2, 1), distribution = "negative binomial",
xreg = x, beta = normal_prior(0.5, 0, 1), u = u)

out <- run_mcmc(model, iter = 1000, particles = 10)
check_diagnostics(out)


bssm documentation built on Nov. 2, 2023, 6:25 p.m.