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SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

Re: SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

Thank you very much. It works.

Re: SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

Thanks for clarification.

I work in environments where both SAS and JMP are in use; therefore I appreciate consistency between both. In case where certain differences are intended by SAS Institute, these should always be indicated in the documentation.

Re: SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

The difference you observed is caused by the Kackar-Harville correction applied by SAS Proc Mixed when the DDFM=KENWARDROGER option is set. JMP doesn’t apply this adjustment to the BLUP standard errors. For details, please see http://www.jmp.com/support/help/13/The_Kackar-Harville_Correction.shtml#1502602

To get the same results, replace DDFM=KENWARDROGER with DDFM=SATTERTHWAITE in your SAS code.

Re: SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

I think that this kind of question is best served by support@jmp.com. They are very friendly, almost as friendly as the people here!

Re: SAS PROC MIXED and JMP Random Effect Variance Estimation are Different

Dear All,

I am trying to compare the results of SAS PROC Mixed and JMP mixed model. I thought the results would be the same, but they are not. I hope JMP can give the same results of SAS PROC MIXED. Could anyone help me with it?

My SAS code as following:

proc mixed data=Data;

class sample;

model result= time / DDFM=KENWARDROGER s cl E3 alphap=0.05 residual outp=mixedout;

random sample /s;

ods output SolutionF=F SolutionR=Ran ;

run;

The results for the fixed effect are

Effect	Estimate	Standard Error	DF	t Value	Pr > \|t\|	Alpha	Lower	Upper
Intercept	7.2638	0.197	15.4	36.86	<.0001	0.05	6.8449	7.6828
time	-0.4636	0.0615	14	-7.54	<.0001	0.05	-0.5955	-0.3317

The results for the random effect are:

Effect	lot	Estimate	Std Err Pred	DF	t Value	Pr > \|t\|
sample	1	-0.01193	0.1762	4.95	-0.07	0.9487
sample	2	0.1647	0.1762	4.95	0.93	0.3932
sample	3	-0.2869	0.1762	4.95	-1.63	0.165
sample	4	0.1799	0.1762	4.95	1.02	0.3546
sample	5	-0.04579	0.1762	4.95	-0.26	0.8055

Then, I used JMP's interface to generate the following code:

DATA Data1; INPUT time sample result; Lines;

1 1 7.04755019105043

2 1 5.84207852539687

3 1 6.02392278004353

4 1 5.43660061149986

1 2 7.27729854732895

2 2 6.5688837201289

3 2 5.6460380590458

4 2 5.88401060914374

1 3 6.15741854776455

2 3 5.90111584525169

3 3 5.78059226421567

4 3 4.91383166620419

1 4 6.98283726578696

2 4 6.3260371191925

3 4 6.09507016140813

4 4 6.06037720591234

1 5 6.95244413898138

2 5 6.36002369549625

3 5 5.94090548712508

4 5 4.9001077313589

;

RUN;

PROC MIXED ASYCOV DATA=Data1 ALPHA=0.05;

CLASS sample;

MODEL result = time/ SOLUTION DDFM=KENWARDROGER;

RANDOM sample / SOLUTION ;

RUN;

I got:

Parameter Estimates

Term	Estimate	Std Error	DFDen	t Ratio	Prob>\|t\|
Intercept	7.2638308	0.197046	15.45	36.86	<.0001*
time	-0.463589	0.061501	14	-7.54	<.0001*

Random Effect Predictions

Term	BLUP	Std Error	DFDen	t Ratio	Prob>\|t\|
sample[1]	-0.011928	0.153244	4.948	-0.08	0.9410
sample[2]	0.1647348	0.153244	4.948	1.07	0.3320
sample[3]	-0.28692	0.153244	4.948	-1.87	0.1207
sample[4]	0.1799016	0.153244	4.948	1.17	0.2938
sample[5]	-0.045789	0.153244	4.948	-0.30	0.7772

It seems like Std error estimates for random effects are not the same in JMP and SAS. I am wondering if there is anyone know how could I code the JMP to make it is the same results as SAS.