Party package: varimp(..., conditional=TRUE) error: term 1 would require 9e+12 columns (fwd)

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?

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