updateGMRF: Computes the posterior Gaussian distribution given the...

In eliaskrainski/emisc: Functions for personal utility

Computes the posterior Gaussian distribution given the (mixed) design and precision matrices.

Description

Computes the posterior Gaussian distribution given the (mixed) design and precision matrices.

Usage

updateGMRF(y, Qe, A, Qx)

Arguments

y	the outcome
Qe	the error precision matrix
A	the design matrix
Qx	the prior precision matrix for the latent random (Markov) random field

Examples

## Consider the Orange dataset
## intercept plus linear trend over age with
## additional trend for each tree j
##  y_ij = a0 + b0*age_ij + b_j age_ij + e_ij
y <- Orange$circumference
z0 <- model.matrix(~Tree-1, Orange)
A <- cbind(1, Orange$age, z0*Orange$age)
Qx <- Diagonal(7, c(0,0.001, rep(2843.56, 5)))
Qe <- Diagonal(nrow(Orange), 0.010493)
up <- updateGMRF(y, Qe, A, Qx)
cbind(x=up$mu, sd=sqrt(diag(solve(up$Q))))

## Not run: 
## compare with INLA
  library(INLA)
  ff <- circumference ~ age +
     f(Tree, age, model='iid')
  res <- inla(ff, data=Orange,
     control.inla=list(int.strategy='eb'))
  res$summary.fixed[, 1:2]
  res$summary.random$Tree[, 1:3]
  round(exp(res$mode$theta), 6)

## End(Not run)

eliaskrainski/emisc documentation built on Nov. 18, 2024, 11:02 a.m.