similar to: Partitioning Around Mediods then rpart to follow

Hi, I need some help understanding the output from PAM. When I look at the output it doesn't list the cluster number by the median vlaues on each of the variables (like it does with k-means) Instead I have the following: So I know for instance cluster 1 has a mean for variable1 of 33.33, however when I run PAM i get: variable 1 variable2 29 32 12 97 12

I have created plots of rpart objects with the fancy option for text.rpart ("fancy" creates ellipses and rectangles and labels branches with splitting criteria). The ellipses and rectangles are supposed to "interrupt" the tree lines (as seen in Therneau and Atkinson 1997, page 48, Fig. 18, http://www.mayo.edu/hsr/techrpt/61.pdf), but they do not, even when I use Therneau and

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

On 08/14/2014 05:00 AM, r-devel-request at r-project.org wrote: > Dear list > > > For my work, it would be helpful if rpart worked seamlessly with an > empty model: > > library(rpart); rpart(formula=y~0, data=data.frame(y=factor(1:10))) > > Currently, an unrelated error (originating from na.rpart) is thrown. > > At some point in the near future, I'd like to

Dear r-help-list: If I use the rpart method like cfit<-rpart(y~.,data=data,...), what kind of tree is stored in cfit? Is it right that this tree is not pruned at all, that it is the full tree? If so, it's up to me to choose a subtree by using the printcp method. In the technical report from Atkinson and Therneau "An Introduction to recursive partitioning using the rpart

There have been various messages about packages tree and rpart whilst I have been travelling, and I have now prepared updates. tree ==== Tree is one of the oldest packages on CRAN (Feb 2000 apart from adding the maintainer field), and I had been hoping that it would fade away in favour of rpart. 1) sys.parent needed to be replaced by parent.frame in all but the most recent R (post 1.3.0).

Hi, I am trying to use the rpart library with my own set of functions on a survival object. I get an immeadiate segmentation fault when i try calling rpart with my list of functions. I get the same problem with the logrank example from Therneau,s S-rpart library though their anova example works. Should I report this as a bug, as even if my functions are structured improperly, that should lead to

Dear all, I'm writing my own method to be used in Rpart by defining the list of functions named init, split and eval. I'm following the example given in the file 'tests/usersplits.R' in the sources. By now I'm able to define the split function (and it works correctly in the tree construction) while I have some problems with the init and the eval function. The task I'm

All the documentation that I have on survival splitting is found in the technical report you mention. However, there is both a short form and a long form of this on our web site, did you get the larger one (52 pages)? I admit it is not a lot. There are no other split algorithms implimented for survival data. It would be possible to add your own. Attached is a slightly updated version of the

Hi all, I'm currently using the 'rpart' function to run some regression analysis and I am at the point where I wish to prune my overfitted trees. Having read the documentation I understand that to do this requires the use of the complexity parameter. My question is how to go about choosing the correct complexity parameter for my tree? In some places

The question is about the direction vector in rpart. There are (at least) two preferred ways to lay out a tree, wrt the question of which obs are sent left and which right. 1. Send the smaller y values to the left. In the final tree, there will be a graphical ordering with smaller y's to the left and larger ones to the right. One has a "left bad, right good"

Hello again I've looked through ?rpart, Atkinson & Therneau (1997), Chap 10 of Venables and Ripley, Breman et al., and the r hgelp archives but haven't seen the answer to these two questions 1) How does rpart deal with asymmetric loss matrices? Breiman et al. suggest some possibilities, but, of course, do not say how rpart does it. 2) In the loss matrix, which direction (column or

> However the as.data.frame(a) transforms the matrix into a numeric > data.frame so when I implement the rpart algorithm it automatically > returns a regression classification tree. Look at help(rpart). The program uses the type of the y variable to GUESS at what you want for the "method" argument. It often guesses wrong. Simply add method="class" as an

Dear r-help, I am looking for additional documentation on the "adj" column in rpart's splits matrix. The help says: adj gives the adjusted concordance for surrogate splits I am looking info about "adjusted concordance". I cannot find this phrase in either Therneau & Atkinson original RPART documentation or the CART book. This question came up in the

So there are a couple parts to this question. I am trying to implement the rpart/random forest algorithms on a transaction lists. That is to say i am trying to train models in order to deduce what are the most predictive transactions within a customers history in order apply this model to future test data and identify accounting irregularities(ie. this account had x and y so they should have also

Hello dear R-help members, I would appreciate any help in understanding how the rpart function computes the "improve" (which is given in fit$split) when using the split='information' parameter. Thanks to Professor Atkinson help, I was able to find how this is done in the case that split='gini'. By following the explanation here:

partition.tree plots in 2d the partition of a classification tree produced by the function tree (assuming the data frame from which it was computed has two continuous predictors). I get an error when I feed a tree produced by rpart to partition.tree (since trees produced by rpart are superclasses of those produced by tree). Is there an equivalent of partition.tree for objects of class rpart?

Hi Everyone, This example code results in R 'crashing'; that is the R application closes with no warnings or error messages. #----------------------- myD <- read.table(stdin(), header=TRUE, nrows=20) Broth Salt pH Temp N Y Growth 1 310 9.0 2.92 10 90.0 NA 0 2 615 6.0 7.82 30 1.0 2 1 3 217 2.0 7.34 10 7.0 8

I want to specify a 2-level mixed model using the lme function in order to test an a priori hypothesis about the between-group values of the slopes but don't know how to do this . Here is the problem. Consider first the case of a single group. The model is: Y_i= a +bX_i + error where I indexes the different values of X and Y in this group . The a priori hypothesis of the slope is: b=K.

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