make_agfh_sampler: Maker Function: Agnostic Fay-Herriot Sampler

In agfh: Agnostic Fay-Herriot Model for Small Area Statistics

Maker Function: Agnostic Fay-Herriot Sampler

Description

A maker function that returns a function. The returned function is a sampler for the agnostic Fay-Herriot model.

Arguments

X	observed independent data to be analyzed
Y	observed dependent data to be analyzed
D	known precisions of response Y
var_gamma_a	latent variance prior parameter, rgamma shape
var_gamma_b	latent variance prior parameter, rgamma rate
S	vector of starting support values for g(\cdot)
kern.a0	scalar variance parameter of GP kernel
kern.a1	scalar lengthscale parameter of GP kernel
kern.fuzz	scalar noise variance of kernel

Details

Creates a Metropolis-within-Gibbs sampler of the agnostic Fay-Herriot model (AGFH).

Value

Returns a sampler, itself a function of initial parameter values (a list with values for \beta, \theta, the latent variance of \theta, and starting values for g(.), typically zeros), number of samples, thinning rate, and scale of Metropolis-Hastings jumps for \theta sampling.

Source

Marten Thompson [e-mail]

Examples

  n <- 10
  X <- matrix(1:n, ncol=1)
  Y <- 2*X + rnorm(n, sd=1.1)
  D <- rep(1, n)
  ag <- make_agfh_sampler(X, Y, D)

  params.init <- list(
    beta=1,
    theta=rep(0,n),
    theta.var=1,
    gamma=rep(0,n)
  )
  ag.out <- ag(params.init, 5, 1, 0.1)

agfh documentation built on July 9, 2023, 6:44 p.m.