Hi,

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

temperature conditions.

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.

Visually the dataset thus looks like this:

Y_behaviour_frequency Individual Session X1 X2 X3 ...

0.5 ind1 S1 5 10 7 ...

0.55 ind2 S1 5 10 7 ...

0.2 ind3 S1 5 10 7 ...

... S1 5 10 7 ...

0.3 ind1 S2 15 7 50 ...

0.01 ind5 S2 15 7 50 ...

... S2 15 7 50 ...

0.4 ind1 S3 2 8 5 ...

0.05 ind3 S3 2 8 5 ...

0.1 ind4 S3 2 8 5 ...

0.2 ind5 S3 2 8 5 ...

... S3 2 8 5 ...

... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...

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 ?

The question was asked 4 years ago:

here:

https://stats.stackexchange.com/questions/62840/random-forest-and-cluster-level-bootstrappingand here:

https://stats.stackexchange.com/questions/93156/random-forest-on-multi-level-hierarchical-structured-databut the main track "hie-ran-forest" also called "HieRanFor" seems

aborted. (

https://r-forge.r-project.org/R/?group_id=2021)

Thanks for your help,

cheers.

hugo

--

- no title specified

Hugo Mathé-Hubert

BU-G19

postdoc

eawag (Swiss Federal Institute of Aquatic Science and Technology)

Evolutionary Ecology

<

http://www.eawag.ch/en/department/eco/main-focus/evolutionary-ecology/>-

About me

<

http://www.eawag.ch/en/aboutus/portrait/organisation/staff/profile/hugo-mathe-hubert/>

Überlandstrasse 133

P.O.Box 611

8600 Dübendorf, Switzerland

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