Laplace.sampling.SPDE: Independence sampler for conditional simulation of a Gaussian...
In PrevMap: Geostatistical Modelling of Spatially Referenced Prevalence Data

Description Usage Arguments Details Value Author(s) See Also

This function simulates from the conditional distribution of a Gaussian process given binomial y. The Guassian process is also approximated using SPDE.

Laplace.sampling.SPDE(
  mu,
  sigma2,
  phi,
  kappa,
  y,
  units.m,
  coords,
  mesh,
  control.mcmc,
  messages = TRUE,
  plot.correlogram = TRUE,
  poisson.llik
)

`mu`	mean vector of the Gaussian process to approximate.
`sigma2`	variance of the Gaussian process to approximate.
`phi`	scale parameter of the Matern function for the Gaussian process to approximate.
`kappa`	smothness parameter of the Matern function for the Gaussian process to approximate.
`y`	vector of binomial observations.
`units.m`	vector of binomial denominators.
`coords`	matrix of two columns corresponding to the spatial coordinates.
`mesh`	mesh object set through `inla.mesh.2d`.
`control.mcmc`	control parameters of the Independence sampler set through `control.mcmc.MCML`.
`messages`	logical; if `messages=TRUE` then status messages are printed on the screen (or output device) while the function is running. Default is `messages=TRUE`.
`plot.correlogram`	logical; if `plot.correlogram=TRUE` the autocorrelation plot of the conditional simulations is displayed.
`poisson.llik`	logical: if `poisson.llik=TRUE` then conditional conditional distribution of the data is Poisson; `poisson.llik=FALSE` then conditional conditional distribution of the data is Binomial.

Binomial model. Conditionally on the random effect S, the data y follow a binomial distribution with probability p and binomial denominators units.m. The logistic link function is used for the linear predictor, which assumes the form

\log(p/(1-p))=S.

The random effect S has a multivariate Gaussian distribution with mean mu and covariance matrix Sigma.

A list with the following components

samples: a matrix, each row of which corresponds to a sample from the predictive distribution.

Emanuele Giorgi e.giorgi@lancaster.ac.uk

Peter J. Diggle p.diggle@lancaster.ac.uk

control.mcmc.MCML.

PrevMap documentation built on Oct. 7, 2021, 5:07 p.m.