Mixed linear model with nested and interaction term

Hi Heng-An,
What do you get when you let SAS use the default REML method (i.e. remove the method=type3 statement)?? I suspect that it is much closer to the R results, and would be what most SAS modelers would consider more appropriate for this design.
Steve Denham Senior Director, Bioinformatics Sciences ?MPI Research, Inc.

**? Sorry I didn't notice that the format of the previous email was off, so I just send the same email again

 
Here is my SAS syntax and output : 

proc mixed data=A method=type3; class Location Block Treatment;
model Yield= Treatment/ddfm=kr;
random Location Location*Treatment Block(Location);
run;quit;

 Source? ? ? ? ? ? ? ? ? ? ? Df? Sum_of_squares? F_value? 
 Treatment? ? ? ? ? ? ? ? 4? ? 46.196951? ? ? ? ? 0.41
 Location? ? ? ? ? ? ? ? ? ? 2? ? 4670.0979652? ? 44.74
 Location*Treatment? 8? ? 224.44332? ? ? ? ? 1.66
 Block (Location)? ? ? ? 9? ? 369.782487? ? ? ? 2.43
 Residual? ? ? ? ? ? ? ? ? ? 34? ? 574.051330


And here is R output:

anova(model_MW)

Analysis of Variance Table
? ? ? ? ? ? ? Df? Sum Sq? Mean Sq? F value
Treatment 4? 34.847? 8.7118? ? ? 0.5085


I am not sure why the sum of square, and the F- value are different.? 
Maybe is because I use type III in SAS and in lmer is using REML? 

I would also like to check the sum of square of other factors as SAS did, is there any way could do this in lmer? 
I am really new to this, Thanks for your time! 

Heng-An 
________________________________________
?: R-sig-mixed-models [r-sig-mixed-models-bounces at r-project.org] ?? Lin, Heng-An [henganl2 at illinois.edu]
????: 2018?5?4? ?? 02:36
?: Ben Bolker
??: r-sig-mixed-models at r-project.org
??: Re: [R-sig-ME] Mixed linear model with nested and interaction term

Thanks!!


Here is my SAS syntax and output :

proc mixed data=A method=type3; class Location Block Treatment;
model Yield= Treatment/ddfm=kr;
random Location Location*Treatment Block(Location);
run;quit;

Source

DF

Sum of Squares

Mean Square

Error DF

F Value

Pr > F

Treatment

4

46.196951

11.549238

8.0509

0.41

0.7954

Location

2

4670.979652

2335.489826

9.2885

44.74

<.0001

Location*Treatment

8

224.443332

28.055417

34

1.66

0.1442

Block(Location)

9

369.782487

41.086943

34

2.43

0.0295

Residual

34

574.051330

16.883863

.

.

.













And here is R output:

anova(model_MW)

Analysis of Variance Table
? ? ? ? ? ? ? Df? Sum Sq? Mean Sq? F value
Treatment 4? 34.847? 8.7118? ? ? 0.5085


I am not sure why the sum of square, and the F- value are different.
Maybe is because I use type III in SAS and in lmer is using REML?

I would also like to check the sum of square of other factors as SAS did, is there any way could do this in lmer?
[[elided Yahoo spam]]

Heng-An
________________________________________
?q: Ben Bolker [bbolker at gmail.com]
?H????: 2018?~5??4?? ?U?? 01:39
??: Lin, Heng-An
???: r-sig-mixed-models at r-project.org
?D??: Re: [R-sig-ME] Mixed linear model with nested and interaction term

This seems like a reasonable model specification. Can you show us
the results you're getting from R and SAS, and your SAS syntax (some
people here understand that language), so that we can see what looks
different? (It would help if you also wrote a few sentences about
what you see as the important differences between the results.)

Hi all,

I am analyzing my data with following model,

model1 <- lmer(Yield~Treatment+(1|Location)+(1|Location:Treatment)+(1|Location:Block), data=A)

in here, I want to set an random interaction term (Location*treatment) and an random nested term (block nested within location).

But I couldn't get similar ANOVA results when I compare the output with SAS porc mixed output.

So, I think i might make some mistake in the model in R...

Can anyone give me some suggestion?

Thanks in advance!

Heng-An

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