Mixed Model

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

Fit a model using the Standard Least Squares personality with specified mixture effects and interactions.

// Open data table
dt = Open("$Sample_Data/Design Experiment/Piepel.jmp");
// Model
Fit Model(
    Effects(
        :X1 & Mixture, :X2 & Mixture,
        :X3 & Mixture, :X1 * :X2,
        :X1 * :X3, :X2 * :X3
    ),
    Y( :Y ),
    No Intercept,
    PERSONALITY(
        "Standard Least Squares"
    )
);

Load an existing data table and open it in the Custom Design platform for editing.

// Open data table
dt = Open("$Sample_Data/Design Experiment/Software Factors.jmp");
// Load and Edit in Custom Design
DOE(
    Custom Design,
    Load Factors( Current Data Table() )
);

Perform a variance component analysis using the REML method and fit a model for shrinkage with random effects in the Standard Least Squares personality.

// Open data table
dt = Open("$Sample_Data/Investment Castings.jmp");
// Model: REML
Fit Model(
    Censor Code( "" ),
    Y( :Shrinkage ),
    Effects(
        :Casting[:Temperature] & Random,
        :Temperature
    ),
    Personality(
        "Standard Least Squares"
    ),
    Method( "REML" ),
    Set Alpha Level( 0.05 )
);

Fit a standard least squares model with mean as the response variable, grp as the effect, and N as the frequency variable.

// Open data table
dt = Open("$Sample_Data/Noah Decay.jmp");
// Fit Model
Fit Model(
    Freq( :N ),
    Y( :mean ),
    Effects( :grp ),
    Personality(
        "Standard Least Squares"
    ),
    Run( :mean << {{:grp << {}}} )
);

Create a Pareto Plot for cause analysis using failure data and frequency counts.

// Open data table
dt = Open("$Sample_Data/Quality Control/Failure.jmp");
// Pareto Plot
Pareto Plot(
    Cause( :failure ),
    Freq( :N )
);

Fit a Weibull-distributed parametric survival model with the Load effect.

// Open data table
dt = Open("$Sample_Data/Reliability/Comptime.jmp");
// Parametric Survival Model
Fit Model(
    Y( :ExecTime ),
    Effects( :Load ),
    Personality( "Parametric Survival" ),
    Distribution( Weibull )
);

Build a multivariate scatterplot matrix with pairwise estimation method for household income, IQ, eighth-grade math, high school graduates, gross state product, vegetable consumption, smokers, physical activity, obese, college degrees, and alcohol consumption.

// Open data table
dt = Open("$Sample_Data/US Demographics.jmp");
// Multivariate
Multivariate(
    Y(
        :Household Income, :IQ,
        :Eighth Grade Math,
        :High School Graduates,
        :Gross State Product,
        :Vegetable Consumption, :Smokers,
        :Physical Activity, :Obese,
        :College Degrees,
        :Alcohol Consumption
    ),
    Estimation Method( "Pairwise" ),
    Scatterplot Matrix(
        Density Ellipses( 1 ),
        Shaded Ellipses( 0 ),
        Ellipse Color( 3 )
    )
);

Perform choice analysis with gender effects using the Choice function.

// Open data table
dt = Open("$Sample_Data/Laptop Runs.jmp");
// Choice with Gender
Open( "$Sample_Data/Laptop Profile.jmp" );
Open(
    "$Sample_Data/Laptop Subjects.jmp"
);
Choice(
    Response Data Table(
        Data Table( "Laptop Runs" )
    ),
    Profile DataTable( Laptop Profile ),
    Subject DataTable(
        Data Table( "Laptop Subjects" )
    ),
    Response Subject ID( :Person ),
    Response Grouping(
        :Survey, :Choice Set
    ),
    Response Profile ID Choices(
        :Choice1, :Choice2
    ),
    Profile ID( :Choice ID ),
    Profile Grouping(
        :Survey, :Choice Set
    ),
    Profile Effects(
        :Hard Disk, :Speed, :Battery Life,
        :Price
    ),
    Subject Subject ID( :Person ),
    Subject Effects( :Gender ),
    "Firth Bias-Adjusted Estimates"n( 1 ),
    Response Profile ID Chosen(
        :Response
    ),
    Likelihood Ratio Tests( 1 )
);

Partition a dataset into clusters based on multiple predictor variables using the Partition platform.

// Open data table
dt = Open("$Sample_Data/Liver Cancer.jmp");
// Partition
Partition(
    Y( :Severity ),
    X(
        :BMI, :Age, :Time, :Markers,
        :Hepatitis, :Jaundice
    ),
    Specify Profit Matrix(
        [1 -3, -5 1, . .],
        "High",
        "Low",
        "Undecided"
    ),
    Show Fit Details( 1 ),
    Informative Missing( 1 ),
    Initial Splits(
        :Time >= 2.571,
        {:Time < 12.714, {:Time < 2.857,
        {}, {:Markers == {1}, {}, {:Age
         >= 62.8603}}}, {:Markers == {0},
        {}, {:Jaundice == {0}}}},
        {:Age >= 66.7315, {}, {:Markers
         == {1}, {:BMI >= 20.679}}}
    )
);