2 Hierarchical structure

There are two parts to simulating hierarchical data. First you need to have a hierarchical data structure and second you need parameters at each of the different hierarchical levels. The data structure is essentially a data.frame (or matrix), with all the grouping factors and their levels, as we would see in a typical dataset. Lets take the blue tit dataset we explored earlier:

data(BTdata)
head(BTdata)

##        tarsus       back  animal     dam fosternest  hatchdate  sex
## 1 -1.89229718  1.1464212 R187142 R187557      F2102 -0.6874021  Fem
## 2  1.13610981 -0.7596521 R187154 R187559      F1902 -0.6874021 Male
## 3  0.98468946  0.1449373 R187341 R187568       A602 -0.4279814 Male
## 4  0.37900806  0.2555847 R046169 R187518      A1302 -1.4656641 Male
## 5 -0.07525299 -0.3006992 R046161 R187528      A2602 -1.4656641  Fem
## 6 -1.13519543  1.5577219 R187409 R187945      C2302  0.3502805  Fem

Here animal, dam, fosternest and sex make up the data structure.

In this Section, we will first demonstrate how to make a simple hierarchical structure using the make_structure function. simulate_population also allows pre-existing data structures to be incorporated into simulations. The remaining part of the section details how to simulate hierarchical data once you have a hierarchical data structure.