beta_err_gen: Generate Data with Beta Sampling Errors

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

Generate Data with Beta Sampling Errors

Description

The traditional Fay-Herriot small area model has a Normal latent variable and Normal observed response errors. This method generates data with Normal latent variables and Beta errors on the response. Note that the sampling errors are transformed so their mean and variance match the the first two moments of the traditional model.

Usage

  beta_err_gen (M, p, D, lambda, a, b)

Arguments

M	number of areal units
p	dimension of regressors i.e. x \in R^p
D	vector of precisions for response, length M
lambda	value of latent variance
a	first shape parameter of Beta distribution
b	second shape parameter of Beta distribution

Value

A list containing

D	copy of argument 'D'
beta	vector of length 'p' latent coefficients
lambda	copy of argument 'lambda'
X	matrix of independent variables
theta	vector of latent effects
Y	vector of responses
err	vector of sampling errors
name	name of sampling error distribution, including shape parameters

Source

Marten Thompson [e-mail]

Examples

  M <- 50
  p <- 3
  D <- rep(0.1, M)
  lamb <- 1/2
  dat <- beta_err_gen(M, p, D, lamb, 1/2, 1/4)

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