Multilevel modeling in R

Now that we have examined (laconically) the basics of multilevel modeling equations, we can turn to how to build multilevel models in R and predict unseen data.

For this purpose, we will first load our dataset produced using the same procedure as mentioned previously (except that the attributes are not scaled). Here again, there are 100 generated observations for each of the 17 hospitals:

NursesML = read.table("NursesML.dat", header = T, sep = " ")

The null model

We will examine the variation in our attributes considering hospitals and observations as a unit of analysis, that is, we will compare whether there is more variation at the hospital and observation levels. What we could do is compute this by hand.

The following will compute the mean for the attribute we want to predict (WorkSat) for each of the hospitals:

means = aggregate(NursesML[,4], by=list(NursesML[,5]), 
   FUN=mean)[2]

We can display the variance of work satisfaction in hospitals and observations as follows...