Fit Model

The following section describes scripting-only messages for MANOVA, Generalized Linear Models, Nominal and Ordinal Logistic, and Standard Least Squares.

MANOVA

The following message for a Fit MANOVA object is available only in JSL:

fit_model_object << ( Response[1] << (Effect[1] << ... ) );

Where the third message could be one of the following:

Test Details // show or hide the test details for an individual effect

Centroid Plot // show or hide the centroid plot for an individual effect

Save Canonical Scores // save the canonical scores for an individual effect

Contrast /* run a customized F test contrasting different levels for

			an effect in the model*/

The following example saves canonical scores for the Response Function, which in this data, is Sum:

dt = Open( "$SAMPLE_DATA/Dogs.jmp" );

obj = dt << Fit Model(

	Y( :LogHist0, :LogHist1, :LogHist3, :LogHist5 ),

	Effects( :drug, :dep1, :drug * :dep1 ),

	Personality( "Manova" ),

	Run( Response Function( Sum ) )

);

obj << (Response[1] << (Effect[1] << Save Canonical Scores) );

Generalized Linear Models

The following message for a Fit GLM object is available only in JSL. The message saves the parametric formula to a new column in the data table

Parametric Formula( )

Nominal and Ordinal Logistic

The following messages for Fit Nominal Logistic and Fit Ordinal Logistic objects are available only in JSL:

fit_model_object << Get SAS Data Step

// create a SAS DATA step to score the data

fit_model_object << Get MM SAS Data Step

// create SAS code that you can register in the SAS Model Manager

Standard Least Squares

The following JSL messages return the requested item from the fitted model, such as variance components, p-values, parameter estimates, and so on:

fit_model_object << Get Variance Components( );

fit_model_object << Get Effect Names( );

fit_model_object << Get Effect PValues( );

fit_model_object << Get Estimates( );

fit_model_object << Get Parameter Names( );

fit_model_object << Get Random Effect Names( );

fit_model_object << Get Std Errors( );

fit_model_object << Get X Matrix( );

fit_model_object << Get XPX Inverse( );

fit_model_object << Get Y Matrix( );

The following message for Standard Least Squares objects is available only in JSL:

obj << Get SQL prediction expression;

The following example saves prediction formulas as SQL expressions and outputs them to the log:

dt = Open( "$SAMPLE_DATA/Tiretread.jmp" );

obj = dt << Fit Model(

	Y( :ABRASION, :HARDNESS ),

	Effects( :SILICA, :SILANE, :SULFUR ),

	Personality( "Standard Least Squares" ),

	Run

);

code = obj << Get SQL Prediction Expression;

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