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Wald p value not shown in JMP version 12

Dinesh

I am currently using JMP 12 for my analysis . I need technical inputs on following aspect

I am using mixed effects model to assess whether the difference between sites is significant
I would like to know the wald p value corresponding to Random effect Site *time [batch] to infer whether impact is significant or not

Please let me know if there is any possibility see wald p value info in version 12 . If there is no option please let me know the formula or procedure that can be used to calculate the wald p value

Is it possible to share script for generating wald p value in JMP version 12 mixed model.jpg

Looking forward for your assistance in this regard as soon as possible as its critical for our project

Accepted answers

jules

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).

@jules

All comments

txnelson

From the JMP documentation,
Help==>Books==>Fitting Linear Models
it states for Fitting Standard Least Square models
Wald p-Value Gives the p-value for the test that the covariance parameter is equal to zero.
This column appears only when you have selected Unbounded Variance Components in
the Fit Model launch window.

Dinesh

As you can see the snapshot I have shared , I am unable to get wald p value
inspite of selecting unbound variance component in fit model launch window

jules

Hi @Dinesh,

P-values are not displayed for variance components in JMP 12; these p-values were added in JMP 13. I realize it's not exactly what you're looking for, but you can use the confidence intervals in much the same way as the hypothesis test at an alpha of 1-confidence level by looking to see if 0 is contained in the interval. If it is not, your p-value (for the corresponding hypothesis test) is < 1-confidence level. If you need to change this confidence level, you can do that when you launch Fit Model: click the top red triangle in the launch window > Alpha Level.

I hope this helps some!

@jules

Dinesh

Thanks a lot Julian .That was really helpful. Is it possible to provide
script specifically to generate this wald p value . We had used used jmp
trial 14 while writing protocol and committed to include wald p value in
results .
But now we have licensed only for 12 . So to bind to that protocol , we
have to include wald p value in report . It would be of great help if you
can share the same .

Thanks a lot again for your inputs

jules

Hi @Dinesh,

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).

@jules