How to create a Nested effect within interaction factors?

[#1]

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!

[#2]

You simply select the columns involved in the interaction first, and then click Cross.

[#3]

Hi Mark,

I know how to create interaction term, but how do I nest the "Block" within ( Year*Location )?

Thank you.

[#4]

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?

• What is your RCBD? That is, what are the factors, factor levels, treatments, and blocking. In particular, how does the nesting occur in your design?
• What is your intended linear model?

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!)

[#5]

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)

[#6]

Thank you for the clarification, it really helps a lot.

So to be sure I undestand, you have all 10 varieties in every combination of location (2) and year (2), an environment. Each variety is tested in triplicate and arranged as 3 coimplete blocks within each environment. So you have 10 x 2 x 2 x 3 = 120 observations arranged in 12 blocks?

It seems to me that the model should treat block as a random effect without nesting. Instead of indicating block = 1, 2, or 3, indicate it as 1 through 12 in the data table. Select the block effect in the model, click the red triangle next to Attributes, and select Random Effect.

I don't understand why you would treat the block as a fixed effect or need to nest it based on your description.

[#7]

Mark,

Thank you so much for your time and effort simulating the data. 

I was trying to treat the nested Block (Y*L) as random effect, not fixed.

I wonder what would be the differences between the two methods on the power of distingushing the differences in the factors and reducing the effects from field variances?. How do I justify myslef not nesting the blocks statistically?

Thank you so much again ! 

[#8 · Accepted solution]

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.

[#9]

Is there any differences in the power of detecting differences in Varieties using these two methods? 

Thank you!!

[#10]

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.