Principal Components

Example 1

Summary: Perform principal components: analysis for variable reduction

Code:

// Open data table
dt = Open("$Sample_Data/Body Fat.jmp");
// Principal Components: Variable Reduction
Principal Components(
    Y(
        :"Age (years)"n, :"Weight (lbs)"n,
        :"Height (inches)"n,
        :"Neck circumference (cm)"n,
        :"Chest circumference (cm)"n,
        :"Abdomen circumference (cm)"n,
        :"Hip circumference (cm)"n,
        :"Thigh circumference (cm)"n,
        :"Knee circumference (cm)"n,
        :"Ankle circumference (cm)"n,
        :
        "Biceps (extended) circumference (cm)"n,
        :"Forearm circumference (cm)"n,
        :"Wrist circumference (cm)"n
    ),
    Estimation Method( "Row-wise" ),
    on Correlations,
    Cluster Variables,
    SendToReport(
        Dispatch( {"Summary Plots"},
            "PCA Summary Plots", FrameBox,
            {Frame Size( 51, 37 )}
        ),
        Dispatch( {"Summary Plots"},
            "PCA Summary Plots",
            FrameBox( 2 ),
            {Frame Size( 55, 37 )}
        )
    )
);

Example 2

Summary: Perform principal component analysis (PCA) on variables using the on Correlations option.

Code:

// Open data table
dt = Open("$Sample_Data/Quality Control/Flight Delays.jmp");
// Principal Components
Principal Components(
    Y(
        :AA, :CO, :DL, :F9, :FL, :NW, :UA,
        :US, :WN
    ),
    Estimation Method( "Default" ),
    "on Correlations"
);

Example 3

Summary: Create a loading plot using the Principal Components analysis with specified correlations and eigenvalues on the dataset.

Code:

// Open data table
dt = Open("$Sample_Data/Quality Control/Steam Turbine Historical.jmp");
// Loading plot
Principal Components(
    Y(
        :Fuel, :Steam Flow, :Steam Temp,
        :MW, :Cool Temp, :Pressure
    ),
    Estimation Method( "Default" ),
    "on Correlations",
    Eigenvalues( 1 ),
    Summary Plots( 0 ),
    Loading Plot( 4 )
);

Example 4

Summary: Perform principal components analysis on a dataset containing thickness measurements using the Row-wise estimation method, on covariances, with eigenvalues calculation and arrow lines in the PCA summary plots. Adjust the frame size of the PCA summary plots to 200x200.

Code:

// Open data table
dt = Open("$Sample_Data/Quality Control/Thickness.jmp");
// Principal Components
Principal Components(
    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" ),
    on Covariances,
    Eigenvalues( 1 ),
    Arrow Lines( 1 ),
    SendToReport(
        Dispatch( {"Summary Plots"},
            "PCA Summary Plots", FrameBox,
            {Frame Size( 200, 200 )}
        ),
        Dispatch( {"Summary Plots"},
            "PCA Summary Plots",
            FrameBox( 2 ),
            {Frame Size( 200, 200 )}
        )
    )
);