com.esri.arcgis.geoprocessing.tools.spatialstatisticstools
Class OrdinaryLeastSquares

java.lang.Object
  com.esri.arcgis.geoprocessing.AbstractGPTool
      com.esri.arcgis.geoprocessing.tools.spatialstatisticstools.OrdinaryLeastSquares

All Implemented Interfaces:

GPTool

public class OrdinaryLeastSquares
extends AbstractGPTool

Performs global Ordinary Least Squares (OLS) linear regression to generate predictions or to model a dependent variable in terms of its relationships to a set of explanatory variables. Results are accessible from the Results window. The Ordinary Least Squares tool is contained in the Spatial Statistics Tools tool box.

Usage tips:

• If you execute this tool in the , the summary report will also be displayed in the. The OLS tool also produces an output feature class and optional tables with coefficient information and diagnostics. All of these are accessible from the Results window. The output feature class is automatically added to the Table of Contents, with a hot/cold rendering scheme applied to model . A full explanation of each output is provided in .
• Results from OLS regression are only trustworthy if your data and regression model satisfy all of the assumptions inherently required by this method. Consult the table, in to ensure your model is properly specified.
• Dependent and Explanatory variables should be numeric fields containing a variety of values. OLS cannot solve when variables have all the same value (all the values for a field are 9.0, for example). Linear regression methods, like OLS, are not appropriate for predicting binary outcomes (e.g., all of the values for the dependent variable are either 1 or 0).
• The Unique ID field links model predictions to each feature. Consequently, the Unique ID values must be unique for every feature, and typically should be a permanent field that remains with the feature class. If you don't have a Unique ID field, you can easily create one by adding a new integer field to your feature class table and calculating the field values to be equal to the FID/OID field. You cannot use the FID/OID field directly for the Unique ID parameter.
• Whenever there is statistically signficant spatial autocorrelation of the regression residuals the OLS model will be considered misspecified and, consequently, results from OLS regression are unreliable. Be sure to run the tool on your regression residuals to assess this potential problem. Statistically significant spatial autocorrelation of regression residuals almost always indicates a key missing explanatory variable.
• You should visually inspect the over and under predictions evident in your regression residuals to see if they provide clues about potential missing variables from your regression model. It sometimes helps to run on the residuals to help you visualize spatial clustering of the over and under predictions.
• When misspecification is the result of trying to model nonstationary variables using a global model (OLS is a global model), then may be used to improve predictions and to better understand the (regional variation) inherent in your explanatory variables.
• When the result of a computation is infinity or undefined, the output for non-shapefiles will be Null; for shapefiles the output will be -DBL_MAX = -1.7976931348623158e+308.
• The optional coefficient and/or diagnostic output tables, if they already exist, will be overwritten when the Geoprocessing Option to is checked ON.
• Map layers can be used to define the Input Feature Class. When using a layer with a selection, only the selected features are included in the analysis.
• When using shapefiles, keep in mind that they cannot store null values. Tools or other procedures that create shapefiles from non-shapefile inputs may store or interpret null values as zero. This can lead to unexpected results. See also .

Field Summary

Fields inherited from class com.esri.arcgis.geoprocessing.AbstractGPTool

vals

Constructor Summary

OrdinaryLeastSquares()
Creates the Ordinary Least Squares tool with defaults.

OrdinaryLeastSquares(java.lang.Object inputFeatureClass, java.lang.Object uniqueIDField, java.lang.Object outputFeatureClass, java.lang.Object dependentVariable, java.lang.Object explanatoryVariables)
Creates the Ordinary Least Squares tool with the required parameters.

Method Summary

java.lang.Object	getCoefficientOutputTable() Returns the Coefficient Output Table parameter of this tool .
java.lang.Object	getDependentVariable() Returns the Dependent Variable parameter of this tool .
java.lang.Object	getDiagnosticOutputTable() Returns the Diagnostic Output Table parameter of this tool .
java.lang.Object	getExplanatoryVariables() Returns the Explanatory Variables parameter of this tool .
java.lang.Object	getInputFeatureClass() Returns the Input Feature Class parameter of this tool .
java.lang.Object	getOutputFeatureClass() Returns the Output Feature Class parameter of this tool .
java.lang.String	getToolboxAlias() Returns the alias of the tool box containing this tool.
java.lang.String	getToolboxName() Returns the name of the tool box containing this tool.
java.lang.String	getToolName() Returns the name of this tool.
java.lang.Object	getUniqueIDField() Returns the Unique ID Field parameter of this tool .
void	setCoefficientOutputTable(java.lang.Object coefficientOutputTable) Sets the Coefficient Output Table parameter of this tool .
void	setDependentVariable(java.lang.Object dependentVariable) Sets the Dependent Variable parameter of this tool .
void	setDiagnosticOutputTable(java.lang.Object diagnosticOutputTable) Sets the Diagnostic Output Table parameter of this tool .
void	setExplanatoryVariables(java.lang.Object explanatoryVariables) Sets the Explanatory Variables parameter of this tool .
void	setInputFeatureClass(java.lang.Object inputFeatureClass) Sets the Input Feature Class parameter of this tool .
void	setOutputFeatureClass(java.lang.Object outputFeatureClass) Sets the Output Feature Class parameter of this tool .
void	setUniqueIDField(java.lang.Object uniqueIDField) Sets the Unique ID Field parameter of this tool .

Methods inherited from class com.esri.arcgis.geoprocessing.AbstractGPTool

getParameterValues, toString

Methods inherited from class java.lang.Object

clone, equals, finalize, getClass, hashCode, notify, notifyAll, wait, wait, wait

Constructor Detail

OrdinaryLeastSquares

public OrdinaryLeastSquares()

Creates the Ordinary Least Squares tool with defaults.

Initializes the array of tool parameters with the default values specified when the tool was created.

OrdinaryLeastSquares

public OrdinaryLeastSquares(java.lang.Object inputFeatureClass,
                            java.lang.Object uniqueIDField,
                            java.lang.Object outputFeatureClass,
                            java.lang.Object dependentVariable,
                            java.lang.Object explanatoryVariables)

Creates the Ordinary Least Squares tool with the required parameters.

Initializes the array of tool parameters with the values as specified for the required parameters and with the default values for the other parameters.

Parameters:

inputFeatureClass - the feature class containing the dependent and independent variables for analysis.

uniqueIDField - an integer field containing a different value for every feature in the Input Feature Class.

outputFeatureClass - the output feature class to receive dependent variable estimates and residuals.

dependentVariable - the numeric field containing values for what you are trying to model.

explanatoryVariables - a list of fields representing explanatory variables in your regression model.

Method Detail

getInputFeatureClass

public java.lang.Object getInputFeatureClass()

Returns the Input Feature Class parameter of this tool . This parameter is the feature class containing the dependent and independent variables for analysis. This is a required parameter.

Returns:

the Input Feature Class

setInputFeatureClass

public void setInputFeatureClass(java.lang.Object inputFeatureClass)

Sets the Input Feature Class parameter of this tool . This parameter is the feature class containing the dependent and independent variables for analysis. This is a required parameter.

Parameters:

inputFeatureClass - the feature class containing the dependent and independent variables for analysis.

getUniqueIDField

public java.lang.Object getUniqueIDField()

Returns the Unique ID Field parameter of this tool . This parameter is an integer field containing a different value for every feature in the Input Feature Class. This is a required parameter.

Returns:

the Unique ID Field

setUniqueIDField

public void setUniqueIDField(java.lang.Object uniqueIDField)

Sets the Unique ID Field parameter of this tool . This parameter is an integer field containing a different value for every feature in the Input Feature Class. This is a required parameter.

Parameters:

uniqueIDField - an integer field containing a different value for every feature in the Input Feature Class.

getOutputFeatureClass

public java.lang.Object getOutputFeatureClass()

Returns the Output Feature Class parameter of this tool . This parameter is the output feature class to receive dependent variable estimates and residuals. This is a required parameter.

Returns:

the Output Feature Class

setOutputFeatureClass

public void setOutputFeatureClass(java.lang.Object outputFeatureClass)

Sets the Output Feature Class parameter of this tool . This parameter is the output feature class to receive dependent variable estimates and residuals. This is a required parameter.

Parameters:

outputFeatureClass - the output feature class to receive dependent variable estimates and residuals.

getDependentVariable

public java.lang.Object getDependentVariable()

Returns the Dependent Variable parameter of this tool . This parameter is the numeric field containing values for what you are trying to model. This is a required parameter.

Returns:

the Dependent Variable

setDependentVariable

public void setDependentVariable(java.lang.Object dependentVariable)

Sets the Dependent Variable parameter of this tool . This parameter is the numeric field containing values for what you are trying to model. This is a required parameter.

Parameters:

dependentVariable - the numeric field containing values for what you are trying to model.

getExplanatoryVariables

public java.lang.Object getExplanatoryVariables()

Returns the Explanatory Variables parameter of this tool . This parameter is a list of fields representing explanatory variables in your regression model. This is a required parameter.

Returns:

the Explanatory Variables

setExplanatoryVariables

public void setExplanatoryVariables(java.lang.Object explanatoryVariables)

Sets the Explanatory Variables parameter of this tool . This parameter is a list of fields representing explanatory variables in your regression model. This is a required parameter.

Parameters:

explanatoryVariables - a list of fields representing explanatory variables in your regression model.

getCoefficientOutputTable

public java.lang.Object getCoefficientOutputTable()

Returns the Coefficient Output Table parameter of this tool . This parameter is the full pathname to an optional table that will receive model coefficients, standard errors, and probabilities for each explanatory variable. This is an optional parameter.

Returns:

the Coefficient Output Table

setCoefficientOutputTable

public void setCoefficientOutputTable(java.lang.Object coefficientOutputTable)

Sets the Coefficient Output Table parameter of this tool . This parameter is the full pathname to an optional table that will receive model coefficients, standard errors, and probabilities for each explanatory variable. This is an optional parameter.

Parameters:

coefficientOutputTable - the full pathname to an optional table that will receive model coefficients, standard errors, and probabilities for each explanatory variable.

getDiagnosticOutputTable

public java.lang.Object getDiagnosticOutputTable()

Returns the Diagnostic Output Table parameter of this tool . This parameter is the full pathname to an optional table that will receive model summary diagnostics. This is an optional parameter.

Returns:

the Diagnostic Output Table

setDiagnosticOutputTable

public void setDiagnosticOutputTable(java.lang.Object diagnosticOutputTable)

Sets the Diagnostic Output Table parameter of this tool . This parameter is the full pathname to an optional table that will receive model summary diagnostics. This is an optional parameter.

Parameters:

diagnosticOutputTable - the full pathname to an optional table that will receive model summary diagnostics.

getToolName

public java.lang.String getToolName()

Returns the name of this tool.

Returns:

the tool name

getToolboxName

public java.lang.String getToolboxName()

Returns the name of the tool box containing this tool.

Returns:

the tool box name

getToolboxAlias

public java.lang.String getToolboxAlias()

Returns the alias of the tool box containing this tool.

Returns:

the tool box alias