Multivariate Analysis

More examples for this topic using the sample data files provided with JMP

Set multiple response modeling type column property

// Open data table
dt = Open("$Sample_Data/Big Class Families.jmp");
// Set Multiple Response
:sibling ages <<
Set Modeling Type( "Multiple Response" );
:sports <<
Set Modeling Type( "Multiple Response" );
:countries visited <<
Set Modeling Type( "Multiple Response" );
:family cars <<
Set Modeling Type( "Multiple Response" );

Perform a Bivariate Analysis to Examine the Relationship Between Difference and Mean.

// Open data table
dt = Open("$Sample_Data/Dogs.jmp");
// Fit diff by mean
Bivariate( Y( :diff ), X( :mean ) );

Perform Multiple Correspondence Analysis (MCA) on categorical variables TV, Film, Art, and Restaurant, and display the total cross table.

// Open data table
dt = Open("$Sample_Data/Employee Taste.jmp");
// MCA-level
Multiple Correspondence Analysis(
    Y( :TV, :Film, :Art, :Restaurant ),
    Cross Table( Show Total( 1 ) )
);

Perform Multiple Correspondence Analysis (MCA) on categorical data variables with Subject as the supplementary variable, and generate a cross table with total values shown.

// Open data table
dt = Open("$Sample_Data/Employee Taste.jmp");
// MCA-subject
Multiple Correspondence Analysis(
    Y( :TV, :Film, :Art, :Restaurant ),
    X( :Subject ),
    Cross Table( Show Total( 1 ) )
);

Save T Square statistics and configuration for a multivariate control chart involving six variables.

// Open data table
dt = Open("$Sample_Data/Quality Control/Aluminum Pins Historical.jmp");
// Save T Square
dt = Current Data Table();
MyTchart =
Multivariate Control Chart(
    Y(
        :Diameter1, :Diameter2,
        :Diameter3, :Diameter4, :Length1,
        :Length2
    ),
    Subgroup( :subgroup )
);
MyTchart << Save T Square;

Generate a Model Driven Multivariate Control Chart (MDMCC) for the entire dataset, incorporating all identified process variables and including statistical prediction error and normalized DModX plots for comprehensive analysis.

// Open data table
dt = Open("$Sample_Data/Quality Control/Flight Delays.jmp");
// MDMCC with entire data
Model Driven Multivariate Control Chart(
    Process(
        :AA, :CO, :DL, :F9, :FL, :NW, :UA,
        :US, :WN
    ),
    Time ID( :Flight date ),
    Statistical Prediction Error Plot,
    Normalized DModX Plot
);

Perform multivariate analysis using Row-wise estimation method and create a scatterplot matrix with density ellipses color coded.

// Open data table
dt = Open("$Sample_Data/Quality Control/Thickness.jmp");
// Multivariate
Multivariate(
    Y(
        :Thickness 01, :Thickness 02,
        :Thickness 03, :Thickness 04,
        :Thickness 05, :Thickness 06,
        :Thickness 07, :Thickness 08,
        :Thickness 09, :Thickness 10,
        :Thickness 11, :Thickness 12
    ),
    Estimation Method( "Row-wise" ),
    Scatterplot Matrix(
        Density Ellipses( 1 ),
        Shaded Ellipses( 0 ),
        Ellipse Color( 3 )
    )
);

Builld a custom display window with four analysis placed horizontally

// Open data table
dt = Open("$Sample_Data/Cola Heart Rate.jmp");
// Fit Y by X Group
New Window(
    "Cola Heart Rate- Fit Y by X of Heart Rate",
    H List Box(
        Oneway(
            Y( :Heart Rate ),
            X( :Drink ),
            Box Plots( 0 ),
            Mean Diamonds( 0 ),
            SendToReport(
                Dispatch( {}, "",
                    NomAxisBox,
                    Rotated Tick Labels(
                        1
                    )
                )
            )
        ),
        Oneway(
            Y( :Heart Rate ),
            X( :Testers ),
            Box Plots( 0 ),
            Mean Diamonds( 0 )
        ),
        Oneway(
            Y( :Heart Rate ),
            X( :"Time (Raw)"n ),
            Box Plots( 0 ),
            Mean Diamonds( 0 )
        ),
        Bivariate(
            Y( :Heart Rate ),
            X( :"Time (Numeric)"n )
        )
    )
);

Perform Multiple Correspondence Analysis with supplementary rows for subject and gender, and generate detailed coordinates and scaling for the first three dimensions.

// Open data table
dt = Open("$Sample_Data/Employee Taste.jmp");
// MCA-subject-supp-gender
Multiple Correspondence Analysis(
    Y( :TV, :Film, :Art, :Restaurant ),
    X( :Subject ),
    Z( :Gender ),
    Cross Table( Show Total( 1 ) ),
    Cross Table of Supplementary Rows(
        Show Total( 1 )
    ),
    Show Coordinates( 1 ),
    Select dimension( 1, 3 ),
    SendToReport(
        Dispatch( {}, "Variable Summary",
            OutlineBox,
            {Close( 1 )}
        ),
        Dispatch(
            {"Correspondence Analysis"},
            "2", ScaleBox,
            {Format( "Fixed Dec", 12, 0 ),
            Min( -2 ), Max( 3.5 ),
            Inc( 1 ), Minor Ticks( 1 )}
        ),
        Dispatch(
            {"Correspondence Analysis"},
            "Details", OutlineBox,
            {Close( 1 )}
        ),
        Dispatch(
            {"Correspondence Analysis"},
            "Row and Column Coordinates",
            OutlineBox,
            {Close( 1 )}
        )
    )
);

Perform confirmatory factor analysis (CFA) with a single-factor conflict model using the SEM platform.

// Open data table
dt = Open("$Sample_Data/Job Satisfaction.jmp");
// SEM: CFA 1Factor Conflict UI
Structural Equation Models(
    Model Variables(
        :Person_C, :Intra_C, :Inter_C
    ),
    Model Specification(
        New Latent( "Conflict" ),
        Means(
            {"Constant", {:Person_C,
            :Intra_C, :Inter_C}}
        ),
        Loadings(
            {"Conflict", {:Person_C,
            :Intra_C, :Inter_C}, {1}}
        ),
        Variances(
            {:Person_C, {:Person_C}},
            {:Intra_C, {:Intra_C}},
            {:Inter_C, {:Inter_C}},
            {"Conflict", {"Conflict"}}
        )
    )
);