Hi @Dinesh,
Sure thing. That p-value is calculated with Z as the reference distribution, and the Variance Components table has all the ingredients to get the test statistic. You can save the table yourself (right click > make into data table), and then make two new columns, the ratio of the variance component to the standard error, and then a second column, a lookup of that quotient in the z-distribution, with some simple arithmetic to find the proportion more extreme than that value in the reference distribution. Here's an example of that:
Names Default To Here( 1 );
dt = Open( "$SAMPLE_DATA\Cholesterol Stacked.jmp" );
platform = dt << Fit Model(
Y( :Y ),
Effects(
:Treatment,
:Month,
:Name( "AM/PM" ),
:Treatment * :Month,
:Treatment * :Name( "AM/PM" ),
:Month * :Name( "AM/PM" ),
:Treatment * :Month * :Name( "AM/PM" )
),
Random Effects(
:Patient[:Treatment],
:Patient * :Month[:Treatment],
:Patient * :Name( "AM/PM" )[:Treatment],
:Patient * :Month * :Name( "AM/PM" )[:Treatment]
),
NoBounds( 1 ),
Personality( "Standard Least Squares" ),
Method( "REML" ),
Emphasis( "Minimal Report" ),
Run(
:Y << {Summary of Fit( 1 ), Analysis of Variance( 0 ), Parameter Estimates( 1 ), Lack of Fit( 0 ),
Scaled Estimates( 0 ), Plot Actual by Predicted( 0 ), Plot Regression( 0 ),
Plot Residual by Predicted( 0 ), Plot Studentized Residuals( 0 ), Plot Effect Leverage( 0 ),
Plot Residual by Normal Quantiles( 0 )}
)
);
Wait( 0 );
Report( platform )[Outline Box( "Response Y" )][Outline Box( "REML Variance Component Estimates" )][
Table Box( 1 )] << Make Into Data Table;
Report( platform ) << Close Window;
New Column( "Var Component/Std Error",
Numeric,
"Continuous",
Format( "Best", 12 ),
Formula( :Var Component / :Std Error ),
Set Selected
);
New Column( "Prob > |Z|",
Numeric,
"Continuous",
Format( "Best", 12 ),
Formula( (1 - Normal Distribution( :Name( "Var Component/Std Error" ) )) * 2 ),
Set Selected
);
This will return the following (showing JMP 14 so you can see JMP's calculation of the Wald P-value, which you can confirm is the same as the one calculated in the method I described above).
@julian