ebp: EBP for proportion under generalized linear mixed model

In NSAE: Nonstationary Small Area Estimation

EBP for proportion under generalized linear mixed model

Description

This function gives the ebp and the estimate of mean squared error (mse) for proportion based on a generalized linear mixed model.

Usage

ebp(
  formula,
  vardir,
  Ni,
  ni,
  method = "REML",
  maxit = 100,
  precision = 1e-04,
  data
)

Arguments

formula	an object of class list of formula, describe the model to be fitted
vardir	a vector of sampling variances of direct estimators for each small area
Ni	a vector of population size for each small area
ni	a vector of sample size for each small area
method	type of fitting method, default is "REML" method
maxit	number of iterations allowed in the algorithm. Default is 100 iterations
precision	convergence tolerance limit for the Fisher-scoring algorithm. Default value is 1e-04
data	a data frame comprising the variables named in formula and vardir

Value

The function returns a list with the following objects:

ebp

a vector with the values of the estimators for each small area

mse

a vector of the mean squared error estimates for each small area

sample

a matrix consist of area code, ebp, mse, standard error (SE) and coefficient of variation (CV)

fit

a list containing the following objects:

• estcoef : a data frame with the estimated model coefficients in the first column (beta), their asymptotic standard errors in the second column (std.error), the t statistics in the third column (tvalue) and the p-values of the significance of each coefficient in last column (pvalue)

• refvar : estimated random effects variance

• randomeffect : a data frame with the values of the random effect estimators

• loglike : value of the loglikelihood

• deviance : value of the deviance

• loglike1 : value of the restricted loglikelihood

Examples

# Load data set
data(headcount)
# Fit generalized linear mixed model using HCR data
result <- ebp(y~x1, var, N, n,"REML",100,1e-04, headcount)
result

NSAE documentation built on May 28, 2022, 1:08 a.m.