sampler: Produce samples from the approximated joint posterior for the...
In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Produce samples from the approximated joint posterior for the hyperparameters

1	inla.hyperpar.sample(n, result, intern=FALSE)

`n`	Integer. Number of samples required.
`result`	An `inla`-object, f.ex the output from an `inla`-call.
`intern`	Logical. If `TRUE` then produce samples in the intern scale for the hyperparmater, if `FALSE` then produce samples in the user-scale. (For example log-precision (intern) and precision (user-scale))

A matrix where each sample is a row. The contents of the column is described in the rownames.

1
2
3

r = inla(y ~ 1, data = data.frame(y=1:10), family = "t")
x = inla.hyperpar.sample(10,r)
str(x)

andrewzm/INLA documentation built on May 10, 2019, 11:12 a.m.

andrewzm/INLA index

README.md

Note that we can't provide technical support on individual packages. You should contact the package authors for that.

Description