similar to: Memory problems with large dataset in rpart

Hello everyone: I keeps using rsync to backup my files on my laptop from one folder to another, and to reduce disk usage, so I use "--link-dest" option for incremental backup like this: *rsync -a --link-dest=/local/old /tmp/myfile /local/new* And "/local/old" is backuped some days ago, I use "--link-dest" option to keep unchanged files as links to new destination

Hello, I saw some patches about PCI-passthrough for HVM(non-IOMMU) and I am interested in it. I want to assign my graphics card(nVidia GeForce 7900GS) to an HVM domain (Vista) in order to run 3D-intensive work(for example, 3D-games). What I want to ask is that is it really possible to pass the graphics card to HVM domain running Vista now? That is, have anyone ever successfully

Hello, I saw some patches about PCI-passthrough for HVM(non-IOMMU) and I am interested in it. I want to assign my graphics card(nVidia GeForce 7900GS) to an HVM domain (Vista) in order to run 3D-intensive work(for example, 3D-games). What I want to ask is that is it really possible to pass the graphics card to HVM domain running Vista now? That is, have anyone ever successfully

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

Hi, I am new to R and need help with rpart. I am trying to create a classification tree using rpart. In order to plot the reults I use the plot function and the text function to label the plot of the tree dendrogram with text. The documentation of text.rpart says : "For the "class" method, label="yval" results in the factor levels being used, "yprob" results

Hi all, I am very new to R (only two days of studies). I know a little bit of statistical learning and looking for an implementation of CART and random forest and therefore I am now studying R. I tested with rpart and randomForest package, they are quite good. However, I need a classification tree and for each training data, there is different loss. (i.e. the loss function is not purely 0-1,

Greetings - Is there a way to automatically perform what I believe is called "rule extraction" (by Quinlan and the machine learning community at least) for the leaves of trees generated by rpart? I can use path.rpart() to automatically extract the paths to the leaves, but these can be needlessly cumbersome. For example, one path returned by path.rpart() might be: [1]

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

After fitting and pruning an rpart model, it is often the case that one or more of the original predictors is not used by any of the splits of the final tree. It seems logical, therefore, that values for these "unused" predictors would not be needed for prediction. But when predict() is called on such models, all predictors seem to be required. Why is that, and can it be easily

Hello there. I have fitted a rpart model. > rpartModel <- rpart(y~., data=data.frame(y=y,x=x),method="class", ....) and can use rpart$where to find out the terminal nodes that each observations belongs. Now, I have a set of new data and used predict.rpart which seems to give only the predicted value with no information similar to rpart$where. May I know how

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 release a package to CRAN which uses rpart and relies on that functionality. I have prepared a patch (minor

Hello useR, Could you please tell me how to draw the decision boundaries in a scatterplot of the original data for a LDA or Rpart object. For example: > library(rpart) >fit.rpart <- rpart(as.factor(group.id)~., data=data.frame(Data) ) How can I draw the cutting lines on the orignial Data? Or is there any built in functions that can read the rpart object 'fit.rpart' to do

I have just upgraded from R 1.7.3 to R 1.9.0 and have found that the predict function no longer works for rpart: > predict(hmmm,sim3[1:10,]) Error in predict.rpart(hmmm, sim3[1:10, ]) : couldn't find function "pred.rpart" I have re-installed the rpart package to no avail. Any ideas? Giles Hooker

Dear r-help mailing list, Is there a way to incorporate weights into the minsplit criteria in rpart, when the weights are uneven? I could not find a way for the minsplit threshold to take the weights into account, and when the weights are uneven it becomes an issue, as the following example shows. My current workaround is to expand the data into one in which each row is an observation, but that

I tried using rpart, as below, and got this error message "rpart Error in yval[, 1] : incorrect number of dimensions". Thinking it might somehow be related to the large number of missing values, I tried using complete data, but with the same result. Does anyone know what may be going on, and how to fix it? I have traced two similar error messages in the Archive, but following the

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 All, I have a question regarding predict.rpart. I use rpart to build classification and regression trees and I deal with data with relatively large number of input variables (predictors). For example, I build an rpart model like this rpartModel <- rpart(Y ~ X, method="class", minsplit =1, minbucket=nMinBucket,cp=nCp); and get predictors used in building the model like

Hi all, I'm a newbee using R. I need to do a classification tree using the rpart package. Basically I have a set of birds of known sex and several morphological measurements and we want to predict the sex using the morphology. I read my csv file and it shows up in R no problem, looks fine but when I execute the following rpart command

I've written a small adaptation of the text.rpart function of the rpart package to better suite my tree presentation needs (I basically left the code untouched, only changing some numbers relating to label positioning). When I changed to version 1.7.0 this function stopped working printing the error : couldn't find function "rpartco" As far as I understand this has to do

Hello, I have attempted to email the author of this package without success, just wondering if anybody else has experienced this. I am having an using rpart on 4000 rows of data with 13 attributes. I can run the same test on 300 rows of the same data with no issue. When I run on 4000 rows, Rgui.exe runs consistently at 50% cpu and the UI hangs.... it will stay like this for at least 4-5hours if