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# Interpreting variable significance in proc mixed

Hi,

I am running a mixed model (procedure mixed) in which a 3-factor variable is highly significat (PR>F : <.0001). However, when I run lsmeans (pdiff) and check the difference between the three factors, there is no difference between them.

Does anyone know what the reason for this result can be? The dataset is large (over 10000 observations).

Regards

/Stina

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## Re: Interpreting variable significance in proc mixed

[ Edited ]

Naturally, it is hard to give an explanation without seeing your code and the results (hint, hint)

--
Paige Miller
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Posts: 4

## Re: Interpreting variable significance in proc mixed

Ok. Here is the code and the results. I'm checking if there are differences in production between plants grown in 3 different locations. Length and Age are also important parameters.

proc mixed data = mydata;
class Location Plot Plant;
model Production = Location Length Age
/ddfm = Sat noint solution outpred = OriginalScale htype = 1,3;
random Int / subject = Plot type = un ;
random Int / subject = Plant type = un ;
lsmeans Location/ pdiff;
run;title;

 Solution for Fixed Effects Effect Location Estimate Standard DF t Value Pr > |t| Error Location EK 0.147 0.1205 39.9 1.22 0.2299 Location PG 0.1119 0.1354 190 0.83 0.4095 Location TO -0.167 0.1422 162 -1.17 0.2421 Length 15.084 0.1213 493 12.44 <.0001 Age 0.1678 0.02495 580 6.72 <.0001 LengthFromStadiu*Age -0.07746 0.01455 601 -5.33 <.0001 Age*Location EK -0.1021 0.03355 523 -3.04 0.0025 Age*Location PG -0.08182 0.02484 516 -3.29 0.0011 Age*Location TO 0 . . . . Type 1 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F Location 3 14.4 348.94 <.0001 Length 1 424 474.32 <.0001 Age 1 454 16.52 <.0001 Length*Age 1 597 20.6 <.0001 Age*Location 2 492 6.77 0.0013 Type 3 Tests of Fixed Effects Effect Num DF Den DF F Value Pr > F Location 3 67.5 1.57 0.2055 Length 1 493 154.73 <.0001 Age 1 597 44.3 <.0001 Length*Age 1 601 28.36 <.0001 Age*Location 2 515 6.77 0.0012 Least Squares Means Effect Location Estimate Standard DF t Value Pr > |t| Error Location EK 12.416 0.09533 24 13.02 <.0001 Location PG 13.022 0.07318 26.8 17.8 <.0001 Location TO 14.102 0.07072 12.8 19.94 <.0001 Differences of Least Squares Means Effect Location Location Estimate Standard DF t Value Pr > |t| Error Location EK PG -0.06065 0.1199 24.6 -0.51 0.6175 Location EK TO -0.1686 0.1186 18.9 -1.42 0.1716 Location PG TO -0.108 0.1019 18.3 -1.06 0.3032

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## Re: Interpreting variable significance in proc mixed

[ Edited ]

I am running a mixed model (procedure mixed) in which a 3-factor variable is highly significat (PR>F : <.0001).

The Type III Test shows a p-value of 0.2055, which indicates no significant differences. You probably should be using the Type III Test here.

--
Paige Miller
New Contributor
Posts: 4

## Re: Interpreting variable significance in proc mixed

Ok, do you have any idea why the III method is better here? I normally use Type I.

Or maybe this question is more suited for a statistical forum than a programming forum?

/Stina

Posts: 2,823

## Re: Interpreting variable significance in proc mixed

Type I assumes that the variable has been entered into the model first, and that the sequence of terms in the model is meaningful. The sequence of your model terms is arbitrary.

Type III assumes that the variable has been entered into the model last. This is more appropriate, it measures the significance of a model term assuming the effects of the other terms has already been accounted for.

--
Paige Miller
New Contributor
Posts: 4