Automation Reference - JMP

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Application Object Reference for Automating JMP Platform Methods GetEffectName(EffectNumber As Integer) As String Returns a string with the name of the effect identified by the integer passed in as a parameter. GetNumberOfEffects() As Short Returns the number of effects in the Current Estimates table. This allows you to loop through the list of effects if you desire, and to obtain the names with GetEffectName(EffectNumber As Integer) As String. Go() As Boolean Starts the selection process. The process continues to run in the background until the model is finished. LockEffect(EffectNumber As Integer, Flag As Boolean) As Boolean Locks (Flag = True) or unlocks (Flag = False) the effect identified by the ordinal number provided in the first parameter. RemoveAll() As Boolean Removes (deselects) all effects from the model. SetDirection(fitStepDirectionConstants Direction) Allows the specification of how variables enter the regression equation. The direction constant should be one of the fitStepDirectionConstants. Possible values are Forward, Backward, or Mixed. SetProbToEnter(Value As Double) As Boolean Sets the Probability to Enter as a floating point value. See the documentation on Stepwise Regression in the Modeling and Multivariate Methods book for an explanation of these values. SetProbToLeave(Value As Double) As Boolean Sets the Probability to Leave as a floating point value. See the documentation on Stepwise Regression in the Modeling and Multivariate Methods book for an explanation of these values. SetRules(fitStepRulesConstants Rules) Allows the specification of the Rules value, just as in the stepwise dialog. The rules constant should be one of the fitStepRulesConstants. Possible values are Combine, Restrict, No Rules, and Whole Effect. Step() As Boolean Stops after each step of the stepwise process. Stop() As Boolean Stops the background selection process. Page 100
Application Object Reference for Automating JMP Platform Methods Gaussian Process Methods The Gaussian Process object provides a way to launch and manipulate Gaussian process analyses. ContourProfiler(Flag as Boolean) Turn this option on (True) or off (False). LaunchEstimateNuggetParameter(Flag as Boolean) As Boolean Turn this launch option on (True) or off (False). LaunchSpecifyCorrelationType(Type as jmpGaussianCorrelationConstant) As Boolean Specify either Gaussian Process or Cubic Model for the correlation type. LaunchSpecifyMinimumTheta(Theta as Double) As Boolean Specify a Theta value prior to launch, just like in the Gaussian Process launch dialog. Profiler(Flag as Boolean) Turn this option on (True) or off (False). SaveJacknifePredictedValues() Save this formula to the current data table. SavePredictionFormula() Save this formula to the current data table. SaveVarianceFormula() Save this formula to the current data table. SurfaceProfiler(Flag as Boolean) Turn this option on (True) or off (False). Hierarchical Cluster Methods The Hierarchical Cluster platform has methods that apply only to Hierarchical Cluster. You can also use the Cluster Object Methods. ColorMap(clusterColormapConstants mapType) As Boolean Generates a color map of the values across the data range. This method mirrors the feature available from the Cluster platform. DistanceGraph(Flag As Boolean) As Boolean Turns the distance graph on (True) or off (False). Page 101
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Automation Reference - JMP

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