saeHB.TF.beta (version 0.2.0)

betaTF: Small Area Estimation using Hierarchical Bayes Twofold Subarea Level Model under Beta Distribution

Description

Function betaTF used for estimation of subarea and area means simultaneously under Twofold Subarea Level Small Area Estimation Model Using Hierarchical Bayesian Method with Beta distribution The range of data must be \(0<y<1\).

Usage

betaTF(
  formula,
  area,
  weight,
  iter.update = 3,
  iter.mcmc = 1000,
  coef = NULL,
  var.coef = NULL,
  thin = 1,
  burn.in = floor(iter.mcmc/2),
  sigma2.u = 1,
  sigma2.v = 1,
  data
)

Value

This function returns a list with following objects:

Est_sub

A dataframe contains the values, standard deviation, and quantile of Subarea mean Estimates using Twofold Subarea level model under Hierarchical Bayes method

Est_area

A dataframe contains the values, standard deviation, and quantile of Area mean Estimates using Twofold Subarea level model under Hierarchical Bayes method

area_randeff

A dataframe contains area random effect

sub_randeff

A dataframe contains subarea random effect

refVar

A dataframe that contains estimated subarea and area random effect variance \((\sigma_{u}^{2}\) and \(\sigma_{v}^{2})\)

coefficient

A dataframe that contains the estimated model coefficient \(\beta\)

plot

Trace, Density, Autocorrelation Function Plot of coefficient

Arguments

formula

Formula that describe the fitted model

area

Index that describes the code relating to area in each subarea.This should be defined for aggregation to get area estimator. Index start from 1 until m

weight

Vector contain proportion units or proportion of population on each subarea. \(w_{ij}\)

iter.update

Number of updates perform ( default = 3)

iter.mcmc

Number of total iterations per chain (default = 1000)

coef

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

Vector contains prior initial value of variance of Coefficient of Regression Model for fixed effect with default vector of 1 with the length of the number of regression coefficients

thin

Thinning rate, must be a positive integer

burn.in

Number of iterations to discard at the beginning

sigma2.u

Number of prior initial value of variance of subarea random effect

sigma2.v

Number of prior initial value of variance of area random effect

data

The data frame

Examples

fit <- betaTF(y~X1+X2,area="codearea",weight="w",data=dataBeta, iter.mcmc = 500)