R: Predictive Analysis

Chapter 12. Multilevel Analyses

In Chapter 10, Classification with k-Nearest Neighbors and Naïve Bayes, we discussed association with k-Nearest Neighbors and Naïve Bayes. In the previous chapter, we examined classification trees using notably C4.5, C50, CART, random forests, and conditional inference trees.

In this chapter, we will discuss:

• Nested data and the importance of dealing with them appropriately
• Multilevel regression including random intercepts and random slopes
• The comparison of multilevel models
• Prediction using multilevel modeling

Nested data

If you have nested data, this chapter is essential for you! What is meant by nested data is that observations share a common context. The examples include:

• Consumers nested within shops
• Employees nested ...