The function genCluster generates multilevel or clustered data based on a previously generated data set that is one “level” up from the clustered data. For example, if there is a data set that contains the school level (considered here to be level 2), classrooms (level 1) can be generated. And then, students (now level 1) can be generated within classrooms (now level 2)

In the example here, we do in fact generate school-, class-, and student-level data. There are eight schools, four of which are randomized to receive an intervention. The number of classes per school varies, as does the number of students per class. (It is straightforward to generate fully balanced data by using constant values.) The outcome of interest is a test score, which is influenced by gender and the intervention. In addition, test scores vary by schools, and by classrooms, so the simulation provides random effects at each of these levels.

We start by defining the school level data:

gen.school <- defData(varname = "s0", dist = "normal", formula = 0, variance = 3, 
    id = "idSchool")
gen.school <- defData(gen.school, varname = "nClasses", dist = "noZeroPoisson", formula = 3)

set.seed(282721)

dtSchool <- genData(8, gen.school)
dtSchool <- trtAssign(dtSchool, n = 2)

dtSchool
##    idSchool         s0 nClasses trtGrp
## 1:        1  0.9732297        3      1
## 2:        2  3.5741932        4      1
## 3:        3  0.1121028        3      0
## 4:        4  0.5147236        4      1
## 5:        5  0.4594058        1      0
## 6:        6 -0.1287554        4      0
## 7:        7  2.4400170        2      1
## 8:        8 -1.2496060        1      0

The classroom level data are generated with a call to genCluster, and then school level data is added by a call to addColumns:

gen.class <- defDataAdd(varname = "c0", dist = "normal", formula = 0, variance = 2)
gen.class <- defDataAdd(gen.class, varname = "nStudents", dist = "noZeroPoisson", 
    formula = 20)

dtClass <- genCluster(dtSchool, "idSchool", numIndsVar = "nClasses", level1ID = "idClass")
dtClass <- addColumns(gen.class, dtClass)

head(dtClass, 10)
##     idSchool        s0 nClasses trtGrp idClass          c0 nStudents
##  1:        1 0.9732297        3      1       1  1.62726560        16
##  2:        1 0.9732297        3      1       2 -0.69640102        16
##  3:        1 0.9732297        3      1       3  1.53921338        13
##  4:        2 3.5741932        4      1       4 -1.58765603        30
##  5:        2 3.5741932        4      1       5 -2.35483046        26
##  6:        2 3.5741932        4      1       6  0.45956895        20
##  7:        2 3.5741932        4      1       7 -0.88401500        26
##  8:        3 0.1121028        3      0       8 -1.46100446        19
##  9:        3 0.1121028        3      0       9  0.07024057        19
## 10:        3 0.1121028        3      0      10  1.09465368        21

Finally, the student level data are added using the same process:

gen.student <- defDataAdd(varname = "Male", dist = "binary", 
    formula = 0.5)
gen.student <- defDataAdd(gen.student, varname = "age", dist = "uniform", 
    formula = "9.5; 10.5")
gen.student <- defDataAdd(gen.student, varname = "test", dist = "normal", 
    formula = "50 - 5*Male + s0 + c0 + 8 * trtGrp", variance = 2)
dtStudent <- genCluster(dtClass, cLevelVar = "idClass", numIndsVar = "nStudents", 
    level1ID = "idChild")

dtStudent <- addColumns(gen.student, dtStudent)

This is what the clustered data look like. Each classroom is represented by a box, and each school is represented by a color. The intervention group is highlighted by dark outlines: