Factorial experiments are very common in agriculture and they are usually laid down to test for the significance of interactions between experimental factors. For example, genotype assessments may be performed at two different nitrogen fertilisation...

R-bloggers

Nonlinear mixed-effects models (NLME) can test for interactions in factorial experiments with repeated measures. Using agricultural growth data as an example, the article demonstrates fitting logistic growth curves with the nlme() function in R, accounting for block and plot clustering. The approach involves specifying fixed effects for genotype-nitrogen interactions and random effects for blocks/plots, then using likelihood ratio tests to assess interaction significance. The method properly handles non-independent observations that naive nonlinear regression would violate.

Testing for interactions in nonlinear regression