Hello,
I wil be running RCBD using fit model. I know how to create the nested effect within a single factor, but how do I create nested effect within interaction factors?
ex. Block (Year * Location)
Thank you!
By giving each block a unique level (1-12), you are indicating the nesting indirectly. If you used instread 1-3 in the four envirionments, then you would have to explicityly nest the blocks.
You simply select the columns involved in the interaction first, and then click Cross.
Hi Mark,
I know how to create interaction term, but how do I nest the "Block" within ( Year*Location )?
Thank you.
There are more than one possible model and it is easy to misunderstand your design and model. Would you please provide the following information for clarification?
For the linear model, please use this nomenclature for the sake of clarity:
Y = I + X1 + X2[X1] + X1*X2[X1] + B
I am indicating that there are two factors, X1 (e.g., Site) and X2 (e.g., Line). They have been entered as two main effects and one interaction effect. The changes in X2 are nested in the X1 levels. B is the block (e.g., Day).
(BTW, I am not guaranteeing that my example is estimable but only to show how the nomenclature works!)
Hello Mark,
The three factors are : 10 Varieties, 2 Years, 2 Locations, (3 blocks in each environment, meaning each year and location)
This is my field design for RCBD for one environment : And I am trying to next the Block effect in (year*Location)
I made a custom design that should be equivalent to your RCBD as you described it. (I made up the response values with a Monte Carlo simulation.) It includes the model (table script) with the random block effect.
I attached it for your inspection.
I don't know the standard deviation of your response or the minimum effect that you are trying to detect but this comparison is fair because I use 1 for the standard deviation (Anticipated RMSE) and 1 for the parameter value (with coding of factor levels, the value is equivalent to an effect that is twice the standard deviation) in both analyses. Here is the power analysis using the random block effect in the model:
(RCBD with random block effects in the model)
Here is the power analysis using your model:
(RCBD with fixed block effects in the model)
So you can see that the power is essentially the same for the effects of interest. Your choice should come down to how you think about the block effect. A random effect seems appropriate to me. Why would you want to model Block as a fixed effect? I do not mean that question as a challenge. It is simply something to think about.
Thank you for comparing the power of detection for me. I did't want to treat the nested Block (Year*Location) effect as fixed, effect. I wanted to treat it as random effect, but just didn't know how to run the test by nesting the Block within the interactions of Year and Location. But now since the power of detection was not affected by simply naming the Blocks 1~12 throughhout the 2 Locations and 2 Years, I would do the analysis this way. Thank you very cmuh again!!
Dear Mark,
Thank you for this discussion of RCBD design 3 years ago. I followed this discussion, but I am still uncertain how to specify my model, which is actually much more simple. It also includes an effect nested within a combination of two other effects. Specifically, I have 4 genotypes reared at 2 temperatures and 4-5 individuals per combination of temperature and phenotype measured repeatedly (many observations of swimming speed per individual). So individuals are random effect nested within genotypeXtemperature. When I do as you suggested in the discussion above to specify individual with unique IDs and run this:
Fit Model( Y( :v ), Effects( :Genotype, :T, :T * :Genotype, :G_T_Ind & Random))
I get lost DF's and nothing is evaluated.
There must be a way to specify that individuals are unique within a combination of :T * :Genotype, right?
Thanks in advance!
L.Yampolsky
