hb_unit {saeHB.unit}

Basic Unit Level Model (Battese-Harter-Fuller model) using Hierarchical Bayesian Approach

Description

This function gives the Hierarchical Bayesian (HB) based on a basic unit level model (Battese-Harter-Fuller model).

Usage

hb_unit(
  formula,
  data_unit,
  data_area,
  domain,
  iter.update = 3,
  iter.mcmc = 10000,
  coef,
  var.coef,
  thin = 3,
  burn.in = 2000,
  tau.u = 1,
  seed = 1,
  quiet = TRUE,
  plot = TRUE
)

Arguments

formula

an object of class formula that contains a description of the model to be fitted. The variables included in the formula must be contained in the data.

data_unit

data frame containing the variables named in formula and domain.

data_area

data frame containing the variables named in formula and domain. Each remaining column contains the population means of each of the p auxiliary variables for the D domains.

domain

Character or formula for domain column names in unit data data_unit and area data data_area. (example : "County" or ~County)

iter.update

Number of updates with default 3

iter.mcmc

Number of total iterations per chain with default 10000

coef

a vector contains prior initial value of Coefficient of Regression Model for fixed effect with default vector of 0 with the length of the number of regression coefficients

var.coef

a vector contains prior initial value of variance of Coefficient of Regression Model with default vector of 1 with the length of the number of regression coefficients

thin

Thinning rate, must be a positive integer with default 2

burn.in

Number of iterations to discard at the beginning with default 2000

tau.u

Prior initial value of inverse of Variance of area random effect with default 1

seed

number used to initialize a pseudorandom number generator (default seed = 1). The random number generator method used is "base::Wichmann-Hill".

quiet

if TRUE, then messages generated during compilation will be suppressed (default TRUE).

plot

if TRUE, the autocorrelation, trace, and density plots will be generated (default TRUE).

Value

The function returns a list with the following objects : Estimation Est, random effect variance refVar, beta coefficient Coefficient and MCMC result result_mcmc

References

1. Battese, G. E., Harter, R. M., & Fuller, W. A. (1988). An error-components model for prediction of county crop areas using survey and satellite data. Journal of the American Statistical Association, 83(401), 28-36.

2. Rao, J. N., & Molina, I. (2015). Small area estimation. John Wiley & Sons.

Examples

library(dplyr)

Xarea <- cornsoybeanmeans %>%
   dplyr::select(
      County = CountyIndex,
      CornPix = MeanCornPixPerSeg,
      SoyBeansPix = MeanSoyBeansPixPerSeg
   )

corn_model <- hb_unit(
   CornHec ~ SoyBeansPix + CornPix,
   data_unit = cornsoybean,
   data_area = Xarea,
   domain = "County",
   iter.update = 20
)