I am examining multiple traits for the effects of various treatments, multiple genotypes, and their interaction. Therefore I have two fixed factors: treatment (4 levels) genotype (12 levels) However, each 'genotype' was kept in 4-5 different 'containers', so there is a nested random effect: container(genotype) [The containers are numbered uniquely in the datasheet.] Therefore my model is: trait1 trait2 trait3 trait4 ~ treatment genotype treatment*genotype container(genotype), where 'treatment' and 'genotype' are fixed, and 'container' is random and nested within 'genotype'. I am interested in significance testing the two fixed factors and their interaction. However, in order to create the correct hypothesis test for each effect, I need to be careful to specify the correct MS divisor for the F ratio given the nested random factor. For the 'genotype' fixed effect I am sure the correct MS term is the 'container(genotype)'; for the 'treatment' fixed effect I think the correct MS term is the residual MS error but am not entirely sure; but for the interaction term I am not at all sure what MS term I should be using. I am using proc glm in SAS v9.3 and this is what I have so far: proc glm data=dataset;
class treatment genotype container ;
model trait1 trait2 trait3 trait4 = treatment|genotype container(genotype) / ss3 nouni;
random container(genotype) / test ;
manova H=treatment;
manova H=genotype E=container(genotype);
manova H=treatment*genotype E=?;
run;
quit; Any help would be greatly appreciated and doesn't have to relate to MANOVA.
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