cpo: Improved estimates for the CPO/PIT-values

In andrewzm/INLA: Functions which allow to perform full Bayesian analysis of latent Gaussian models using Integrated Nested Laplace Approximaxion

Description

Improve the estimates of the CPO/PIT-values be recomputing the model-fit by removing data-points.

Usage

inla.cpo(result,
         force = FALSE,
         verbose = TRUE,
         recompute.mode = TRUE)

Arguments

result	An object of class inla, ie a result of a call to inla()
force	If TRUE, then recompute all CPO/PIT values and not just those with result$cpo$failure > 0.
verbose	Run in verbose mode?
recompute.mode	Should be mode (and the integration points) be recomputed when a data-point is removed or not?

Value

The object returned is the same as result but the new improved estimates of the CPO/PIT values replaced.

Author(s)

Havard Rue [e-mail]

See Also

inla

Examples

n = 10
y = rnorm(n)
r = inla(y ~ 1, data = data.frame(y), control.compute = list(cpo=TRUE))
## Not run:
rr = inla.cpo(r, force=TRUE)
## End(Not run)

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