Automation Reference - JMP

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Application Object Reference for Automating JMP Platform Methods ScoreData(Flag As Boolean) As Boolean Turns the option On (True) or Off (False). ScoreOptions(discrimScoreOptions option, Flag As Boolean) As Boolean Select an option and then specify if the option should be turned On (Flag is True) or Off (False). Examples are “Show Classification Counts” and “Select Uncertain Rows”. ScoreSelectUncertainRows(Value As Double) As Boolean This Score option takes a value where you specify how much the points differ from 0 or 1. ShowCanonicalPlot(Flag As Boolean) As Boolean Turns the option On (True) or Off (False). ShowGroupMeans(Flag As Boolean) As Boolean Turns the option On (True) or Off (False). ShowWithinCovariances(Flag As Boolean) As Boolean Turns the option On (True) or Off (False). SpecifyPriors(discrimPriorsOptions option) Allows Equal Probabilities and Proportional to Occurrence priors specifications to be used. StepwiseSetup This method doesn't take any parameters. StepwiseSetup brings up a selection panel, but you cannot automate the items within the panel. So, invoking StepwiseSetup will require user interaction to continue the analysis. Please use it with care. DistribFit Object Methods The DistribFit object, produced from the Distribution object FitDistribution method, allows further manipulation of the fit output. DensityCurve(Flag As Boolean) These are display options that can be set (True) or reset (False). GoodnessOfFit(Flag As Boolean) These are display options that can be set (True) or reset (False). QuantilePlot(Flag As Boolean) These are display options that can be set (True) or reset (False). QuantilePlotAction(distributionFitQuantilePlotConstants action, VARIANT_BOOL flag) As Boolean Manipulates the Distribution Fit Quantile Plot. The type of action that is performed (e.g. Rotate or turning on Confidence Limits) is governed by the first parameter. The flag turns the option on (True) or off (False). Page 82
Application Object Reference for Automating JMP Platform Methods Quantiles(UpperLimit As Double, LowerLimit As Double, Target As Double) Returns the unscaled and uncentered distribution specific upper and lower percentiles that you specify. LabelCumPoints(Flag As Boolean) Turns the option on (True) or off (False). RemoveFit() Removes the fit from the analysis. The object pointer is no longer valid after this call. SaveDensityFormula() Saves the density values into a new column of the data table. SaveFittedQuantiles() Saves the fitted quantile values into a new column of the data table. SpecLimits(lower as Double, upper as Double, target as Double) Displays the specification limits for a capability analysis. Distribution Object Methods The Distribution object provides a way to launch and manipulate distribution analyses. BetaBinomialFit(Sample Size as Integer, Sample Column as String) As Fit Perform a Beta Binomial Fit on an existing Distribution. If you enter a numeric value for sample size, you must enter an empty string ("") as the Sample Column name. As an alternative, you can enter the name of a column that contains the sample size as the second parameter. In that case, the first parameter is ignored. A Fit object is returned for further manipulation. BinomialFit(Sample Size as Integer, Sample Column as String) As Fit Perform a Binomial Fit on an existing Distribution. If you enter a numeric value for sample size, you must enter an empty string ("") as the Sample Column name. As an alternative, you can enter the name of a column that contains the sample size as the second parameter. In that case, the first parameter is ignored. A Fit object is returned for further manipulation. CapabilityAnalysis(LowerLimit As Double, UpperLimit As Double, Target As Double, Sigma As Double) Performs a capability analysis with the provided lower spec limit, upper spec limit, target value and sigma. CDFPlot(Flag As Boolean) Display option that can be set by specifying True for parameter, or reset by specifying False. ConfidenceInterval(Alpha As Double) Compute the confidence intervals, with the given alpha levels, for means and standard deviations if the columns are continuous and for proportions if the columns are discrete. Page 83
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