01-13-2017 04:25 AM
I am examining multiple traits for the effects of various treatments, multiple genotypes, and their interaction.
Therefore I have two fixed factors:
However, each 'genotype' was kept in 4-5 different 'containers', so there is a nested random effect:
[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.
01-16-2017 03:36 PM
Ouch. Since PROC GLM does not correctly calculate standard errors for split plot models (which is what this is), you may want to consider using PROC GLIMMIX. Example 45.5 Joint Modeling of Binary and Count Data is a place to get started. It would help to know what distribution(s) your trait variables came from. Work through that example carefully, and I think you will see how to apply it to your situation.
01-16-2017 07:17 PM
01-16-2017 08:18 PM - edited 01-16-2017 08:19 PM
For a short illustration of using the MIXED procedure to do MANOVA, see
The column compares GLM and MIXED, and also notes the use of a mixture of distributions in GLIMMIX like @SteveDenham suggested, which would be very handy if different traits followed different distributions.
Edit: You can certainly use GLIMMIX for data that follow a normal distribution.