On Sun, 29 Apr 2012, Abigail Clifton wrote:
Hi,
This is my code (my data is attached):
library(languageR)
library(rms)
library(party)
OLDDATA <- read.csv("/Users/Abigail/Documents/OldData250412.csv")
OLDDATA$YD <- factor(OLDDATA$YD, label=c("Yes", "No"))
OLDDATA$ND <- factor(OLDDATA$ND, label=c("Yes", "No"))
attach(OLDDATA)
defaults <- cbind(YD, ND)
I'm not really sure what you are trying to do here. It seems that you have
a bivariate response, where each of the responses is a binary factor. If
so, you have to specify this as
cforest(YD + ND ~ LN + LV + ..., ...)
because cbind(YD, ND) loses the factor property of the data and hence
leades to the error below.
But it may also be conceivable that YD/ND count the number of
successes/failures, similar to a binary GLM. If so, you need to construct
a binary factor response y = factor(c("Success", "Failure", "Success",
"Failure", ...)) with the corresponding w = c(nsuc1, nfail1, nsuc2,
nfail2, ....)) and then call cforest(y ~ ..., weights = w).
hth,
Z
set.seed(47)
data.controls <- cforest_unbiased(ntree=500, mtry=3)
data.cforest <- cforest(defaults~LN+LV+LT+RV+MR+TL+DIA+CB, data = OLDDATA,
controls=data.controls)
data.cforest.varimp <- varimp(data.cforest, conditional = TRUE)
barplot(sort(data.cforest.varimp))
And this is the error I get:
data.cforest <- cforest(defaults~LN+LV+LT+RV+MR+TL+DIA+CB, data = OLDDATA,
controls=data.controls)
Error in xt[[j]][nas, drop = FALSE] <- 0 :
(subscript) logical subscript too long
Basically I want to reduce the number of factors I've got (9) to fewer, say
the 5 most important ones? Hence I imagined Random Forests would be a good
idea. However I get the error given above. Is it possible to smarten up the
code and correct the error? Or has anybody got other ideas of how I can
reduce the amount of factors I have?
Kind regards,
AJC
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and provide commented, minimal, self-contained, reproducible code.
