Spatial Error Model (SEM)

Maximum Likelihood Spatial Error Model (공간오차모형, SEM)을 수행하빈다.

Syntax

SpatialErrorModel (SimpleFeatureCollection inputFeatures, String dependentVariable, String explanatoryVariables, SpatialConcept spatialConcept, DistanceMethod distanceMethod, StandardizationMethod standardization, Double searchDistance) : SimpleFeatureCollection, SpatialErrorResult

Input Parameters

Identifier

Description

Type

Default

Required

inputFeatures

종속변수와 독립변수를 포함하고 있는 입력 레이어입니다.

SimpleFeatureCollection

dependentVariable

종속변수값을 가진 숫자 필드입니다.

String

explanatoryVariables

회귀 분석에 사용할 쉼표로 구분된 설명 변수 숫자 필드의 목록입니다.

String

spatialDiagnotics

Lagrange multiplier, Moran’s I 등 공간 진단을 포함할 지 여부를 설정합니다.

Boolean

spatialConcept

피처들 간에 공간 관계를 설정하는 방식을 선택합니다.

SpatialConcept

InverseDistance

distanceMethod

분석 대상 피처로부터 이웃 피처까지의 거리를 계산하는 방법을 설정합니다.

DistanceMethod

Euclidean

standardization

통계량 계산시 행 표준화 적용 여부를 설정합니다.

StandardizationMethod

None

searchDistance

역거리 혹은 고정 거리 옵션 선택 시 기준 값을 지정합니다.

Double

0.0

Process Outputs

Identifier

Description

Type

Default

Required

resFeatures

종속 변수 추정치와 잔차를 포함한 출력 레이어입니다.

SimpleFeatureCollection

report

분석 결과입니다.

SpatialErrorResult

Constraints

  • Output은 XML로 반환한다.

Examples

a1_2000 필드를 종속변수로, a2_2000, a3_2000, a4_2000 필드를 설명변수로 분석한 결과는 다음의 XML 포맷으로 반환됩니다.

<?xml version="1.0" encoding="UTF-8"?>
<SpatialErrorModel>
  <ModelName>Maximum Likelihood Spatial Error</ModelName>
  <Dataset>seoul_series</Dataset>
  <DependentVariable>a1_2000</DependentVariable>
  <NumberOfObservations>25</NumberOfObservations>
  <NumberOfVariables>4</NumberOfVariables>
  <DegreesOfFreedom>21</DegreesOfFreedom>
  <MeanDependentVar>18229.716524000003</MeanDependentVar>
  <SdDependentVar>5222.973372203831</SdDependentVar>
  <LagCoefficient>-0.24851881360049768</LagCoefficient>
  <RSquared>0.2518656668858821</RSquared>
  <PseudoRSquared>0.25196101716592434</PseudoRSquared>
  <SigmaSquareML>1.8812637542498086E7</SigmaSquareML>
  <SeOfRegression>4337.353748830972</SeOfRegression>
  <LogLikelihood>-245.02018916073624</LogLikelihood>
  <AkaikeCriterion>498.0403783214725</AkaikeCriterion>
  <SchwarzCriterion>502.9158816209453</SchwarzCriterion>
  <Summary>
    <Variable>
      <Variable>CONSTANT</Variable>
      <Coefficient>-88317.68976363928</Coefficient>
      <StandardError>37270.805072442025</StandardError>
      <ZStatistic>-2.369621197931709</ZStatistic>
      <Probability>0.017806318045455766</Probability>
    </Variable>
    <Variable>
      <Variable>a2_2000</Variable>
      <Coefficient>995.8416093808737</Coefficient>
      <StandardError>382.5707273790552</StandardError>
      <ZStatistic>2.603026154675402</ZStatistic>
      <Probability>0.009240490401231434</Probability>
    </Variable>
    <Variable>
      <Variable>a3_2000</Variable>
      <Coefficient>693.7113928797462</Coefficient>
      <StandardError>621.213541267178</StandardError>
      <ZStatistic>1.1167035919157267</ZStatistic>
      <Probability>0.2641210995973676</Probability>
    </Variable>
    <Variable>
      <Variable>a4_2000</Variable>
      <Coefficient>136.8233103433231</Coefficient>
      <StandardError>551.1711267400561</StandardError>
      <ZStatistic>0.24824107016014255</ZStatistic>
      <Probability>0.8039478914561922</Probability>
    </Variable>
    <Variable>
      <Variable>lambda</Variable>
      <Coefficient>-0.24851881360049768</Coefficient>
      <StandardError>0.3002979071631366</StandardError>
      <ZStatistic>-0.8275742443502613</ZStatistic>
      <Probability>0.40791166731613593</Probability>
    </Variable>
  </Summary>
</SpatialErrorModel>