I would like to build a forest of regression trees to see how well some
covariates predict a response variable and to examine the importance of
the
covariates. I have a small number of covariates (8) and large number of
records (27368). The response and all of the covariates are continuous
variables.
A cursory examination of the covariates does not suggest they are
correlated
in a simple fashion (e.g. the variance inflation factors are all fairly
low)
but common sense suggests there should be some relationship: one of them
is
the day of the year and some of the others are environmental parameters
such
as water temperature. For this reason I would like to follow the advice
of
Strobl et al. (2008) and try the authors' conditional variable
importance
measure. This is implemented in the party package by calling varimp(...,
conditional=TRUE). Unfortunately, when I call that on my forest I
receive
the error:
varimp(myforest, conditional=TRUE)
Error in model.matrix.default(as.formula(f), data = blocks) :
term 1 would require 9e+12 columns
Does anyone know what is wrong?
Hi Jason,
the particular feature doesn't scale well in its current implementation.
Anyway, thanks for looking up previous reports closely. I can offer to
have a look at your data if you send them along with the code to reproduce
the problem.
Best,
Torsten
I noticed a post in June 2011 where a user reported this message and the
ultimate problem was that the importance measure was being conditioned
on
too many variables (47). I have only a small number of variables here so
I
guessed that was not the problem.
Another suggestion was that there could be a factor with too many
levels. In
my case, all of the variables are continuous. Term 1 (x1 below) is the
day
of the year, which does happen to be integers 1 ... 366. But the
variable is
class numeric, not integer, so I don't believe cforest would treat it as
a
factor, although I do not know how to tell whether cforest is treating
something as continuous or as a factor.
Thank you for any help you can provide. I am running R 2.13.1 with party
0.9-99994. You can download the data from
http://www.duke.edu/~jjr8/data.rdata (512 KB). Here is the complete
code:
load("\\Temp\\data.rdata")
nrow(df)
[1] 27368
summary(df)
y x1 x2 x3
x4 x5 x6 x7
x8
Min. : 0.000 Min. : 1.0 Min. :0.0000 Min. : 1.00
Min.
: 52 Min. : 0.008184 Min. :16.71 Min. :0.0000000 Min. :
0.02727
1st Qu.: 0.000 1st Qu.:105.0 1st Qu.:0.0000 1st Qu.: 30.00 1st
Qu.:1290 1st Qu.: 6.747035 1st Qu.:23.92 1st Qu.:0.0000000 1st
Qu.:
0.11850
Median : 1.282 Median :169.0 Median :0.2353 Median : 38.00
Median
:1857 Median :11.310277 Median :26.35 Median :0.0001569 Median :
0.14625
Mean : 5.651 Mean :178.7 Mean :0.2555 Mean : 55.03
Mean
:1907 Mean :12.889021 Mean :26.31 Mean :0.0162043 Mean :
0.20684
3rd Qu.: 5.353 3rd Qu.:262.0 3rd Qu.:0.4315 3rd Qu.: 47.00 3rd
Qu.:2594 3rd Qu.:18.427410 3rd Qu.:28.95 3rd Qu.:0.0144660 3rd
Qu.:
0.20095
Max. :195.238 Max. :366.0 Max. :1.0000 Max. :400.00
Max.
:3832 Max. :29.492380 Max. :31.73 Max. :0.3157486 Max.
:11.76877
library(HH)
<output deleted>
vif(y ~ ., data=df)
x1 x2 x3 x4 x5 x6 x7 x8
1.374583 1.252250 1.021672 1.218801 1.015124 1.439868 1.075546 1.060580
library(party)
<output deleted>
mycontrols <- cforest_unbiased(ntree=50, mtry=3) # Small
forest
but requires a few minutes
myforest <- cforest(y ~ ., data=df, controls=mycontrols)
varimp(myforest)
x1 x2 x3 x4 x5 x6
x7
x8
11.924498 103.180195 16.228864 30.658946 5.053500 12.820551
2.113394
6.911377
varimp(myforest, conditional=TRUE)
Error in model.matrix.default(as.formula(f), data = blocks) :
term 1 would require 9e+12 columns
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and provide commented, minimal, self-contained, reproducible code.
