similar to: Classification tree with a random variable

Hi, I have done an analysis using 'rpart' to construct a Classification Tree. I am wanting to retain the output in tree form so that it is easily interpretable. However, I am wanting to compare the 'accuracy' of the tree to a Random Forest to estimate how much predictive ability is lost by using one simple tree. My understanding is that the error automatically displayed by the two

FWIW, I wrote a little function to extract variable importance as defined in the CART book a while ago. It's rather limited: Only works for regression problem, and you need to set maxsurrogate=0 and maxcompete=0. It may (or may not) help you: varimp.rpart <- function(x) { dev <- x$frame[, c("var", "dev")] dev <- dev[dev$var != "<leaf>",

Dear List, My first R function is a rip-off bagging algorithm from pg. 138 of Everitt and Hothorn's "Handbook of Statistical Analyses using R" (HSAUR). I'm using recursive partitioning to develop a set of useful variables in diagnosing ADHD. I'm running this in ESS in XEmacs 21.4.19, R 2.4.1 on Slackware Linux 11.0 with a 2.6 kernel. This is almost an entire script,

Greeting- I have a mixed network of ms-windows, macintosh and freebsd systems. I am setting up a FreeBSD 9.0 system as a PDC using samba. I can from a FreeBSD box attach to the SMB server as a user that is defined on the Samba Server. [wynkoop at dt0 ~]$ smbclient -L hp1 Enter wynkoop's password: Domain=[HARAPARTNERS] OS=[Unix] Server=[Samba 3.6.4] Sharename Type

Dear all, I have a 100 iteration loop. Within each loop, there are some calls to rpart() like: ctl <- rpart.control(maxcompete=0, maxsurrogate=0, maxdepth=10) temp <- rpart(y~., x, w=wt, method="class", parms=list(split="gini"), control=ctl) res <- log(predict.rpart(temp, type="prob")) newres <- log(predict.rpart(temp, newdata=newx,

Dear R users, I'm working with the rpart package and want to evaluate the performance of user defined split functions. I have some problems in understanding the meaning of the xval argument in the two functions rpart.control and xpred.rpart. In the former it is defined as the number of cross-validations while in the latter it is defined as the number of cross-validation groups. If I am

Dear R users, I have been using the rpart procedure to predict the occurrence of depression in a large data file. Since the prevalence is very low (5%), I have been using classification trees with a loss matrix that penalized false negatives more than false positives. I have become interesested in bagging these (successful!) classification trees, and have been using the ipred package for

Dear R's I'm trying to use specific rather than random cross-validation groups in mvpart. The man page says: xval Number of cross-validations or vector defining cross-validation groups. And I found this reply to the list by Terry Therneau from 2006 The rpart function allows one to give the cross-validation groups explicitly. So if the number of observations was 10, you could use

Hi, I'm using samba 3.0.22 on a Linux/Debian machine. I'm trying to get printer drivers on the server automatically picked by the XP clients on the server. $ rpcclient localhost -U flavien -c 'enumdrivers' Password: [Windows NT x86] Printer Driver Info 1: Driver Name: [hp1] The user "flavien" has PrintOperator privileges : $ net rpc

My log says suddenly: Dec 14 02:35:16 hp1 kernel: [1492482.232822] o2net: no longer connected to node hp2 (num 1) at 192.168.1.2:7777 Dec 14 02:35:18 hp1 kernel: [1492483.960150] BUG: soft lockup - CPU#1 stuck for 61s! [kvm:32398] I have no idea what happens here and why - but the result are a lot of problems with virtual machines. Viele Gr??e Andreas Rittershofer -- Hier k?nnte keine

Hi this is my first post. I am trying to run a simulation for a computer playing Von Neumann poker and adjusting it's expectation of an opponent's behavior according to how the opponent plays. This program involves random generation of "hands" and shifting of parameters. However, when I run the code, no errors come up, but the program doesn't do anything. Could someone

Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error

I have a large Sweave report that reads data from a database file. Some of the columns are 1-character strings containing only +, - or NA. An example for such a table is shown below, and can be downloaded for easier testing from http://www.menne-biomed.de/uni/test.zip (For security reasons, the file is zipped) table test hp hp1 + a - + library(RODBC) channel =

Hi, In my company , we have 25 HP printers at different floors. All printesr are configured on One Linux Machine. Each printer has one administrator. My task is share the printer to that particular administrator. For example if i have 2 printers ( HP1 and HP2) , i want to share HP1 printer to only 192.168.0.1 and HP2 printer to only 192.168.0.2. If we use host allow &

Dear R users, I know cross-validation does not work in rpart with user defined split functions. As Terry Therneau suggested, one can use the xpred.rpart function and then summarize the matrix of the predicted values into a single "goodness" value. I need only a confirmation: set for example xval=10, if I correctly understood a single column of the matrix obatined by xpred.rpart gives

Depending on the setting of par()$usr, the 'at' setting in mtext(.., adj=0, outer=T) may cause the text to appear in an anomalous position (e. g. in the first instance below, at the left of the plot region rather than at 'at=0' in the figure region), or the text may not appear at all. If one does not set the 'at' parameter the text appears (with 'adj=0') on the

Dear R friends- I'm attempting to generate a regression tree with one gradient predictor and multiple responses, trying to test if change in size (turtle.data$Clength) acts as a single predictor of ten multiple diet taxa abundances (prey.data) Neither rpart or mvpart seem to allow me to do multiple responses. (Or if they can, I'm not using the functions properly.) > library(rpart)

I was wandering if anybody ever tried to compare the classification accuracy of nnet to other (rpart, tree, bagging) models. From what I know, there is no reason to expect a significant difference in classification accuracy between these models, yet in my particular case I get about 10% error rate for tree, rpart and bagging model and 80% error rate for nnet, applied to the same data. Thanks.

Hi All, I have to say upfront that I am a complete neophyte when it comes to programming. Nevertheless I enjoy the challenge of using R because of its incredible statistical resources. My problem is this .........I am running a regression tree analysis using "rpart" and I need to run the calculation repeatedly (say n=50 times) to obtain a distribution of results from which I will pick

Dear R community, I've been writing simple functions for the past year and half and have come across a similar problem several times. The execution of a function within my own function produces NaN's or fails to execute as intended. My conundrum is that I can execute the function outside of my function without error, so it's difficult for me, as a novice functioneer, to figure out