Using cforest on a hierarchically structured dataset
I am facing a hierarchically structured dataset, and I am not sure of
the right way to analyses it with cforest, if their is one.
- - BACKGROUND & PROBLEM
We are analyzing the behavior of some social birds facing different
The behaviors of the birds were recorder during many sessions of 2 hours.
Conditional RF (cforest) are quite useful for this analysis since, we
have a large number of variables describing the temperature during the 2
hours, they are rather highly correlated, and we expect they have some
non linear effects on the behavior.
For the other behaviors, for each individual and each session of 2
hours, we recorded the frequency.
For each session of 2 hours, we have only one value for the variables
related to the temperature, since these variables are for example
minimal and maximal temperature, median temperature, and different
measures of the variance of the temperature.
If I run a classical cforest on this dataset, explaining
Y_behaviour_frequency with Individual, Session and all the X...
variables, I end up with some conditional relative importances similar
to the attached plot:
They are all very very low, but none is negative. The absence of
negative conditional relative importance is annoying since we were
selecting variables using the threshold of minus two times the lowest
conditional relative importance.
- - QUESTIONS
1) have you ever faced one of these situations of
- all very low conditional relative importances
- all positives conditional relative importances
- hierarchically structured dataset analyzed with cforest
I think, but I am not sure, the very low but all positive conditional
importance might come from the hierarchically structured dataset:
Since RF are based on bootstraps, when bootstrapping in at each
iteration, all sessions or almost all sessions of 2 hours are sampled,
although they are the main source of variation.
The bootstrap would need to be itself hierarchic, first sampling the
sessions and then sampling the individual in the sampled session of 2 hours.
2) It's easy to perform such kind of hierarchic bootstrap in R, but have
you ever heard about it in a random forest ?
Thoughts appear from doubts and die in convictions. Therefore, doubts
are an indication of strength and convictions an indication of weakness.
Yet, most people believe the opposite.
- - - - - - - - - - - - - - - - - -
Les réflexions naissent dans les doutes et meurent dans les certitudes.
Les doutes sont donc un signe de force et les certitudes un signe de
faiblesse. La plupart des gens sont pourtant certains du contraire.