Recursive Partitioning in R

Question

I am aiming to better predict a buying habits of a company's customer base according to several customer attributes (demographic, past purchase categories, etc). I have a data set of about 100,000 returning customers including the time interval from their last purchase (the dependent variable in this study) along with several attributes (both continuous and categorical).

I plan on doing a survival analysis on each segment (segments defined as having similar time intervals across observations) to help understand likely time intervals between purchases. The problem I am encountering is how to best define these segments; i.e. groupings of attributes such that the time interval is sufficiently different between segments and similar within segments. I believe that building a decision tree is the best way to do this, I would suppose using recursive partitioning.

I am new to R and have poked around with the party package's mob command, however I am confused by which variables to include in the model and which to include for partitioning (command: mob(y ~ x1 + ... + xk | z1 + ... + zk), x being model variables and z being partitions). I simply want to build a tree from the set of attributes, so I suppose I want to partition on all of them? Not sure. I have also tried the rpart command but either get no tree or a tree with hundreds of thousands of nodes depending on the cp level.

If anyone has any suggestions, I'd appreciate it. Sorry for the novel and thanks for the help.

Answer 1

From the documentation at ?mob:

MOB is an algorithm for model-based recursive partitioning yielding a tree with fitted models associated with each terminal node.

It's asking for model variables because it will build a model at every terminal node (e.g. linear, logistic) after splitting on the partition variables. If you want to partition without fitting models to the terminal nodes, the function I've used is ctree (also in the party package).