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>