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hi, I am encoutering a very little problem that seemed to be so easy to solve.... I need to divide the array > A<-c(1:200) into two subsets at random. Therefore I use the function "sample" in R: > S<-sample(A,100) for a random sample of size 100. Then I need the values in A that are not selected in S to be put in another array, there is my problem! Is there anyway to do

hi, I would like to subsample the array c(1:200) at random into ten subsamples v1,v2,...,v10. I tried with to go progressively like this: > x<-c(1:200) > v1<-sample(x,20) > y<-x[-v1] > v2<-sample(y,20) and then I want to do: >x<-y[-v2] Error: subscript out of bounds.

Is it possible to make multiple plots i.e. to represent more than one function on the same figure? Thank you Email: botti23@libero.it [[alternative HTML version deleted]]

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()

Dear all For my research, I am learning classification now. I was trying some example about classification tree pakages, such as tree and rpart, for instance, in Pima.te dataset have 8 variables (include class=type): library(rpart) library(datasets) pima.rpart <- rpart(type ~ npreg+glu+bp+skin+bmi+ped+age,data=Pima.te, method='class') plot(pima.rpart, uniform=TRUE) text(pima.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 Experts, I am new to R, using decision tree model for getting segmentation rules. A) Using behavioural data (attributes defining customer behaviour, ( example balances, number of accounts etc.) 1. Clustering: Cluster behavioural data to suitable number of clusters 2. Decision Tree: Using rpart classification tree for generating rules for segmentation using cluster number(cluster id) as target

Hi all, What parameter do I normally change in the rpart function? How do I set the "cp" option? Is there a way to read off error rate directly from the "rpart" function for training data; I imagine for testing data I have to apply a "predict", but for training data I guess the error count would be somewhere existing once the "rpart" function is

Hi, all Our understanding of how classification trees in Rpart treat missing is that if the variable is ordinal(continous), Rpart, by default, imputes a value for missing. How do we do the classification tree and tell Rpart not to impute. That is, what command is used to turn off the imputation. Also, if we do get true missing, how does classification tree analysis in Rpart treat missing when

I was under the impression that the tree method (e.g. as implemented in rpart) was insensitive to monotonic transformations of the dependent variable. e.g. Breiman Olshen et al. Classification and Regression Trees state "In a standard data structure [a tree] is invariant under all monotone transformations of individual ordered varaibles" (p. 57) However, I get very different results

Dear helpers I want to use rpart several times in a loop to build a classification tree. My problem is that rpart needs a formula as argument and for that the variables need to have names and this doesn't happen in my case. Every iteration in the loop has a different dataset with several variables (ex. 38 or more) and so I can't type the names by hand every time. Is there any function

Hi everyone, Could somebody explain to me what is the package/function for classification analysis. I am performing analysis of music files in the form of MIDI files. I end up with about 750 dependent variables from the analysis, I also have a number of independent/grouping variables that I set manually. What I would like is to be able to predict which group a particular MIDI files belongs to

Hi, I am building a regression tree (method=anova) by using rpart package and as a final result I get the final leaves characterized by different means and standard deviations for the dependent variable. However, differently from the classification tree for categorical variables I cannot find a way to assign each observation to a leaf, i.e. I can find no frame whcih contains the observation id

Hi, I think I'm missing something very obvious, but I am missing it, so I would be very grateful for help. I'm using rpart to analyse data on skull base morphology, essentially predicting sex from one or several skull base measurements. The sex of the people whose skulls are being studied is known, and lives as a factor (M,F) in the data. I want to get back predictions of gender, and

Hello Gurus: When I plot a survival fit using rpart for the classification tree, for each node, there is a decimal based number above the event/total. I tried to see if it's the exponential ratio or logrithmics, neither seem to be the case. I'm wondering if anyone knows what they are. Thanks, Karen _________________________________________________________________ Find hidden words,

I am trying to create a classification tree using either tree or rpart but when it comes to plotting the results the formatting I get is different than what I see in all the tutorials. What I would like to see is the XX/XX format but all I get is a weird decimal value. I was also wondering how you know which is yes and which is no in each leaf of the tree? Is yes always on the left?

Dear all R users and helpers: I wonder whether there is a library call trees ( classification trees) in R? If there is, where can download it? Thank you very much. Xin Qi

I've been groping my way through a classification/discrimination problem, from a consulting client. There are 26 observations, with 4 possible categories and 24 (!!!) potential predictor variables. I tried using lda() on the first 7 predictor variables and got 24 of the 26 observations correctly classified. (Training and testing both on the complete data set --- just to get started.) I

Hi, I am using the rpart package to build a classification tree. I did manage to build a tree with data on a previous project. However, when attampting to build a tree on a project I am working on, I seem to be getting the error shown below: > nhg3.rp <- rpart(profitresp ~., nhg3, method="class") > plot(nhg3.rp, branch=0.4, uniform=T); text(nhg3.rp, digits=3) Error in

Hello all, I am currently working with rpart to classify vegetation types by spectral characteristics, and am comming up with poor classifications based on the fact that I have some vegetation types that have only 15 observations, while others have over 100. I have attempted to supply prior weights to the dataset, though this does not improve the classification greatly. Could anyone supply some