NSglm.test: Parametric bootstrap-based spatial nonstationarity test for...

In NSAE: Nonstationary Small Area Estimation

Parametric bootstrap-based spatial nonstationarity test for generalized linear mixed model

Description

This function performs a parametric bootstrap-based test procudure for testing spatial nonstationarity in the data.

Usage

NSglm.test(
  formula,
  vardir,
  Ni,
  ni,
  lat,
  lon,
  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
lat	a vector of latitude for each small area
lon	a vector of longitude 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 class "htest" containing the following components:

method

a character string indicating what type of test was performed.

p.value

the p-value for the test.

data.name

a character string giving the name of the data.

Examples

# Load data set
data(headcount)
# Testing spatial nonstationarity of the data
result <- NSglm.test(y~x1, var, N,n,lat,long, "REML", 10, 1e-04, headcount[1:10,])
result

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