NSAE (version 0.4.0)

ebpNP: Nonparametric ebp using spatial spline for proportion under generalized linear mixed model

Description

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

Usage

ebpNP(
  formula,
  vardir,
  n.knot,
  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

n.knot

number of knot in spatial splines. Default is 25 knot

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 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 lambda : estimated spatial intensity paprameter randomeffect : a data frame with the values of the area specific random effect gamma : a data frame with the values of the spatially correlated random effect variance : a covariance matrix of estimated variance components

Examples

# NOT RUN {
# Load data set
data(headcount)
# Fit a nonparametric generalized linear mixed model using headcount data
result <- ebpNP(y~x1, var,25, N, n,  lat, long, "REML", 100, 1e-04,headcount)
result
# }