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 ______________________________________________ R-help@r-project.org mailing list https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
