similar to: anyway to force rpart() to include a specific predictor

Dear R-list, I am using the rpart/mvpart-package for selecting a right-sized regression tree by 10-fold cross-validation. My question: Is there a possibility to find out for every observation in which of the ten folds it is lying? I want to use the same folds for validating another regression method (moving averages) in order to choose the better one. Thanks a lot, Pedro

Good morning, everyone, I am sorry for this off-topic post but think I can get great answer from this list. My question is what is the best OS on PC (laptop) for statistical computing and why. I really appreciate your insight. Have a nice day.

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,

hello, I have a question about making n step ahead forecasts in cases where test and validation sets are availiable. For instance, I would like to make one step ahead forecasts on the WWWusage data so I hold out the last 10 observations as the validation set and fit an ARIMA model on the first 90 observations. I then use a for loop to sequentially add 9 of the holdout observations to make 1

Hi, I am trying to make a multi-class classification tree by using rpart. I used MASS package'd data: fgl to test and it works well. However, when I used my small-sampled data as below, the program seems to take forever. I am not sure if it is due to slowness or there is something wrong with my codes or data manipulation. Please be advised ! The data is described as the output from str()

Hi, there: I am working on a classification problem by using rpart. when my response variable y is binary, the trees grow very fast, but if I add one more case to y, that is making y has 3 cases, the tree growing cannot be finished. the command looks like: x<-rpart(r0$V142~.,data=r0[,1:141], parms=list(split='gini'), cp=0.01) changing cp or removing parms does not help.

Hi all, I ran a discriminant analysis with some data and want to get a general idea of prediction error rate. Some have suggested using X-fold cross validation procedure. Anyone know if there is a function for this in R? Thanks, Wade [[alternative HTML version deleted]]

Dear all, I would like to model the relationship between y and x. y is binary variable, and x is a count variable which may be possion-distribution. I think it is better to divide x into intervals and change it to a factor before calling glm(y~x,data=dat,family=binomail). I try to use rpart. As y is binary, I use "class" method and get the following result. >

Does anyone know a reason why, in principle, a call to randomForest cannot accept a data frame with missing predictor values? If each individual tree is built using CART, then it seems like this should be possible. (I understand that one may impute missing values using rfImpute or some other method, but I would like to avoid doing that.) If this functionality were available, then when the trees

Hi, I searched the archives and did not find a good solution to that. assume I have 10 sets and I want to have the common character elements of them. how could i do that? -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Not sure if anyone has posted on this problem ... I want to use rpart to build a binary tree on a relatively large dataset with ~1400 data points and 15 predictors. But I've noticed that rpart fails almost immediately in the call to C_s_to_rp, as that code returns nonsense. Looking at the code itself isn't terribly helpful, and there don't seem to be any hard limits coded anywhere.

Is there an optimal / minimum sample size for attempting to construct a classification tree using /rpart/? I have 27 seagrass disturbance sites (boat groundings) that have been monitored for a number of years. The monitoring protocol for each site is identical. From the monitoring data, I am able to determine the level of recovery that each site has experienced. Recovery is our

Hi, there: I am wondering if there is a quick way to "reverse" a list like this: t0 <- list(a=1, b=1, c=2, d=1) reverst t0 to t1 > t1 $`1` [1] "a" "b" "d" $`2` [1] "c" thanks. -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

Hi, I am wondering how to make a function Fun to make the following work: t0 <- (paste("hgu133a", "ENTREZID", sep="")) xx <- as.list(Fun(t0)) # make it work like xx<-as.list(hgu133aENTREZID) thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..." ---Matrix III

I remember in one slide of Prof. Ripley's presentation overhead, he said the most popular data analysis software is excel. So is there any resource or tutorial on this topic? Thank you so much!

Right now, I am using SAS and S+/R. As a new year resolution, I am planning to learn a scripting language. from statisticians' point of view, which scripting language is worth to learn, perl, python, or any other recommendation? (Most likely, I will be learning it in windows.) Since I am not in research, I will prefer one widely used in industry and related to statistical work. if you

Dear listers, a little off-topic: I am looking for and compare algorithms which can calculate "distance" or "similarity" between two gene lists with different lengths. Any paper, any implementation in R and any suggestion is welcome! Thanks, -- Weiwei Shi, Ph.D Research Scientist GeneGO, Inc. "Did you always know?" "No, I did not. But I believed..."

hi, is there some good summary on clustering methods in R? It seems there are many packages involving it. And I have two questions on clustering here: 1. Is there a way of evaluate the effecitives (or seperation) of clustering (rather than by visualization)? 2. Is there a search method (like genetic search) which can help find the best subset of attributes which gives best seperation? Thanks,

Dear all, I have a data matrix with 7 independent variables, and each of them is a trinary variable ( - 1, 0 , 1), and I would like to know what kinds of R package or method I should use to perform the clustering. Thanks for any comment or suggestion. -- Lam C. Tsoi (Alex) Medical University of South Carolina [[alternative HTML version deleted]]

I really have headache when I do google search for information about "R". Any tricks or tips? thx.