Mixed linear model with nested and interaction term

Hi Ben,

When I using SAS with default REML, it won't display the sum of square.
It only shows covariance parameter estimates for random effect,
for the fixed effect, it still using Type 3.

I am trying using the code below in r to see the difference
anova(model_MW, ddf="Kenward-Roger")
anova(model_MW, type=3)
anova(model_MW, type=3, ddf="Kenward-Roger")

________________________________
?: Steve Denham [stevedrd at yahoo.com]
????: 2018?5?7? ?? 06:14
?: Ben Bolker; Lin, Heng-An
??: r-sig-mixed-models at r-project.org
??: Re: [R-sig-ME] 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<mailto:r-sig-mixed-models-bounces at r-project.org>] ?? Lin, Heng-An [henganl2 at illinois.edu<mailto:henganl2 at illinois.edu>]
????: 2018?5?4? ?? 02:36
?: Ben Bolker
??: r-sig-mixed-models at r-project.org<mailto: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?
I am really new to this, Thanks for your time!

Heng-An
________________________________________
?q: Ben Bolker [bbolker at gmail.com<mailto:bbolker at gmail.com>]
?H????: 2018?~5??4?? ?U?? 01:39
??: Lin, Heng-An
???: r-sig-mixed-models at r-project.org<mailto: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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