Thank you for the pointer to: help("BinaryTree-class").
It probably isn't the prettiest solution, but I was able to extract what
I needed by doing something like the following:
nodes = where(fit)
freq = treeresponse(fit)
for(i in 1:100)
{
if(nodes[i]==40)
{
print(paste(i,nodes[i],freq[i]))
}
}
where the for loop from 1:100 is big enough to find at least one
instance of a datapoint that gets classified in terminal node "40" and
to print out its associated frequency distribution. I can change the
"40" to whatever terminal node I'm after and make the for loop larger if
1:100 is too small to find at least one instance.
--Steve
-----Original Message-----
From: Achim Zeileis [mailto:achim.zeil...@uibk.ac.at]
Sent: Wednesday, April 07, 2010 4:57 PM
To: Steven J. Clark
Cc: r-help@r-project.org
Subject: Re: [R] extracting ctree() output information
On Wed, 7 Apr 2010, Steven J. Clark wrote:
> Hi,
>
> I am new to R and am using the ctree() function to do customer
> segmentation. I am using the following code to generate the tree:
>
> treedata$Response<-factor(treedata$Conversion)
> fit<-ctree(Response ~
> .,controls=ctree_control(mincriterion=0.99,maxdepth=4),data=treedata)
> plot(fit)
> print(fit)
>
> The variable "Response" above equals 1 if the customer responded to an
> offering and 0 otherwise. Everything works great, however I am
> struggling to extract the information I need from the output. When I
> look at the output from print(fit) I see things similar to:
>
> 4) age <=42; criterion 1, statistic = 73.055
> 5)* weights = 5843
>
> What this is telling me is that 5,843 customers ended up being
> classified into the group labeled 5. What I would really like to
> know, however, is what proportion of this 5,843 had Response=1 and
> what proportion had Response=0 so that I could make some inference
> about the
> P(Response) for customers that match the demographic characteristics
> of each terminal node.
See
help("BinaryTree-class")
especially the examples section. With where(fit) you can query the node
number and with treeresponse(fit) you get a list with the frequency
distribution. There are also some worked examples how you can compute
other quantities of interest.
hth,
Z
> Any help on how to extract this information would be greatly
> appreciated
> -- thanks!
>
> [[alternative HTML version deleted]]
>
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