NSglm.test {NSAE}

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