Mixed model

Imagine you have several dolls that you like to play with. Each doll is a little different from the others: one has brown hair, another has blonde hair, and another has curly hair. You also have different sets of clothes that fit each doll, but some of the outfits can be worn by more than one doll.

A mixed model is like having all of these dolls and outfits, but also having a special box where you can store some extra clothes and accessories that don't fit any of your dolls. Just like how the dolls are all a little different, a mixed model allows you to model data that has both similar and different characteristics.

The model has two parts: a fixed part and a random part. The fixed part is like the clothes that fit specific dolls, while the random part is like the extra clothes in the box that can be used with any doll.

In statistical terms, the fixed part of the model represents variables that are known and have a specific effect on the outcome you're interested in, while the random part represents variables that are unknown and have a more general effect.

For example, if you were studying the growth of sunflowers, you might have a fixed effect for the amount of water the plants receive and a random effect for the amount of sunlight they get. The water amount has a specific, predictable effect on sunflower growth, while the amount of sunlight can vary and have a less predictable effect due to factors like weather patterns.

Overall, the mixed model allows you to account for both specific and general factors that can affect your data, and helps you gain a deeper understanding of the underlying patterns and relationships in your study.