Hi @Accio,
thank you for the nicely written example. Clearly, so far a simulation example was missing.
I like how you plot the different effects on the readouts separately and then on-top of each other.

One thing I was wondering is whether we, in addition to the benefit of randomization, also show the benefit of blocking?
We can use the optimize_design function to ensure that most treatments cover most rows and columns (e.g. the randomization is really efficient), and thus get even less bias from the plate effect.
Maybe in your example it does not make much of a difference, but if the randomization is 'unlucky' you could still get many samples of Compound1 in the same column and due to the plate gradient still draw wrong conclusions.
What do you think?
I'm happy to add that part!

Thank you for the kind words! Yes, your proposal makes perfect sense. Please do it.

I planned indeed to expand on the simple example. If you take over the blocking part, then I may contribute the next level: namely adding an operator/plate-batch effect on the top. Does that make sense? I can build then on what you have built.

Best regards and thanks again for the encouraging words.

Thanks David, yes, I'll add that part!

hi @julianesiebourg, I went through the vignette and updated it in accordance with the designit style.

I use a function to generate data with batch effects based on a batch container.

could you please let me know what you think?