ebpSP {NSAE}

Spatial ebp for proportion under generalized linear mixed model

Description

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

Usage

ebpSP(
  formula,
  vardir,
  Ni,
  ni,
  proxmat,
  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

proxmat

a D*D proximity matrix of D small areas. The matrix must be row-standardized.

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

• rho : estimated spatial correlation

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

• variance : a covariance matrix of estimated variance components

• loglike : value of the loglikelihood

• deviance : value of the deviance

Examples

# Load data set
data(headcount)
# Fit a generalized linear mixed model with SAR spcification using headcount data
result <- ebpSP(ps~x1, var, N, n, Wmatrix, "REML", 100, 1e-04, headcount)
result