Type: Package
Title: Robust Maximum Likelihood Estimation for Spatial Error Model
Version: 0.1.1.1
Date: 2025-07-20
Maintainer: Vural Yildirim <vurall_yildirim@hotmail.com>
Description: Provides robust estimation for spatial error model to presence of outliers in the residuals. The classical estimation methods can be influenced by the presence of outliers in the data. We proposed a robust estimation approach based on the robustified likelihood equations for spatial error model (Vural Yildirim & Yeliz Mert Kantar (2020): Robust estimation approach for spatial error model, Journal of Statistical Computation and Simulation, <doi:10.1080/00949655.2020.1740223>).
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 7.3.2
Depends: R (≥ 2.10)
NeedsCompilation: no
Author: Vural Yildirim ORCID iD [aut, cre], Yeliz Mert Kantar ORCID iD [aut, ths]
Packaged: 2025-07-20 17:58:52 UTC; Vural
Repository: CRAN
Date/Publication: 2025-07-20 18:10:02 UTC

Spatial Robust MLE Package

Description

Robust Maximum Likelihood Estimation for Spatial Error Model.

Author(s)

Vural Yildirim vurall_yildirim@hotmail.com

Yeliz Mert Kantar

References

Yildirim, V. and Kantar, Y.M. (2020). Robust estimation of spatial error model. in Journal of Statistical Computation and Simulation. doi:10.1080/00949655.2020.1740223

Initial coefficients of individual pension system data

Description

Initial coefficients of individual pension system data were obtained by MLE.

Usage

IPS_coefs

Format

A list with 10 values, which are:

(Intercept)

intercept

Labor_Rate

labor rate

Unemployment_Rate

unemployment rate

Sex_Ratio

sex ratio

Urbanization_Rate

urbanization rate

Deposit_Rate

deposit rate

Illiteracy_Rate

illiteracy rate

HDI

human development index

lambda

spatial autocorrelation parameter

s2

variance

The individual pension system data of Turkey

Description

This is individual pension system data of Turkey for analysing spatial error model.

Usage

IPS_data

Format

A list with 10 variables, which are:

ID

provinces ID

Province

provinces names

RPIPS

participant rate of individual pension system

Labor_Rate

labor rate

Unemployment_Rate

unemployment rate

Sex_Ratio

sex ratio

Urbanization_Rate

urbanization rate

Deposit_Rate

deposit rate

Illiteracy_Rate

illiteracy rate

HDI

human development index

Robust Maximum Likelihood Estimation for Spatial Error Model

Description

This package provides robust maximum likelihood estimation for spatial error model.

Usage

RoMLE.error(
  initial.beta,
  initial.s2,
  initial.lambda,
  W,
  y,
  x,
  phi.function,
  converge.v,
  iter,
  print.values
)

Arguments

initial.beta

initial value of coefficients

initial.s2

initial value of varaince

initial.lambda

initial value of autocorrelation parameters

W

a symmetric weight matrix

y

dependent variable

x

independent variables

phi.function

a robust m-estimator function, should be set as 1 for Cauchy, 2 for Welsch, 3 for Insha and 4 for Logistic

converge.v

converge value for fisher scoring algorithm, can be set as 1e-04

iter

iteration number for fisher scoring algorithm, set by users (e.g. 100)

print.values

printing estimated values for each step until converge, should be set TRUE or FALSE

Value

coefficients, lambda, s2, Phi

References

Yildirim, V. and Kantar, Y.M. (2020). Robust estimation of spatial error model. in Journal of Statistical Computation and Simulation doi:10.1080/00949655.2020.1740223

Yildirim, V., Mert Kantar, Y. (2019). Spatial Statistical Analysis of Participants in The Individual Pension System of Turkey. Eskisehir Teknik Universitesi Bilim Ve Teknoloji Dergisi B - Teorik Bilimler, 7(2), 184-194 doi:10.20290/estubtdb.518706

Examples

#spdep library can be used to create a weight matrix from listw
#require(spdep)
#W <- as(listw, "CsparseMatrix")

#example 1
data(TRQWM)
data(unemployment_data)
data(unemployment_coefs)

y <- unemployment_data$unemployment
x <- unemployment_data$urbanization

#initial values was taken from MLE
initial.beta <- unemployment_coefs[1:2,2]
initial.lambda <- unemployment_coefs[3,2]
initial.s2 <- unemployment_coefs[4,2]

RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x,
            phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)

#example 2
data(TRQWM)
data(IPS_data)
data(IPS_coefs)
y <- IPS_data[,3]
x <- IPS_data[,4:10]

#initial values was taken from MLE
initial.beta <- IPS_coefs[1:8,2]
initial.lambda <- IPS_coefs[9,2]
initial.s2 <- IPS_coefs[10,2]
RoMLE.error(initial.beta, initial.s2, initial.lambda, W=TRQWM, y, x,
            phi.function=3, converge.v=0.0001, iter=100, print.values=TRUE)

Queen weight matrix of Turkey

Description

This is queen continugity weight matrix of Turkey.

Usage

TRQWM

Format

A symmetric matrix with 81x81 values,

V

provinces ID

Initial coefficients of unemployment data

Description

Initial coefficients of unemployment data were obtained by MLE.

Usage

unemployment_coefs

Format

A list with 4 values, which are:

(Intercept)

intercept

Unemployment_Rate

unemployment rate

lambda

spatial autocorrelation parameter

s2

variance

Unemployment data of Turkey

Description

This is unemployment data of Turkey for analysing spatial error model.

Usage

unemployment_data

Format

A list with 4 variables, which are:

ID

provinces ID

province

provinces names

unemployment

unemployment rate

urbanization

urbanization rate