similar to: Help on rpart

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

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

We are trying to make a decision tree using rpart and we are continually running into the following error: > fit_rpart=rpart(ENROLL_YN~MINORITY,method="class") > summary(fit_rpart) Call: rpart(formula = ENROLL_YN ~ MINORITY, method = "class") n= 5725 CP nsplit rel error 1 0 0 1 Error in yval[, 1] : incorrect number of dimensions ENROLL_YN is a

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 everyone, I have a problem using rpart (R 2.0.1 under Unix) Indeed, I have a large matrix (9271x7), my response variable is numeric and all my predictor variables are categorical (from 3 to 8 levels). Here is an example : > mydata[1:5,] distance group3 group4 group5 group6 group7 group8 pos_1 0.141836040224967 a c e a g g pos_501

Hello, I am using rpart to derive classification rules for customer segments. I have a few categorical variables in the set of independent variables. For instance, Account Size can be (Very-Small, Small, Medium, Large, V-Large) Rpart seems to encode these categories into: a,b,c,d,e The results are expressed in terms of the encoded values. How do I find out what encoding was used by rpart.

Dear members of R-list, I need to reproduce the rules of a decision tree. For that I need to use the csplit information from the rpart.object. But I cannot uderstand the information because from my example I get: > rpart.tree$csplit [,1] [,2] [,3] [,4] [,5] [,6] [,7] [1,] 1 3 3 1 3 3 3 [2,] 2 3 3 1 2 2 2 [3,] 1 3 3 1 3 3 3

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 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 all, I used rpart to fit a model, where the covariates in the model are categorical variables. Then I plotted the tree (mytree) and used the command "text" to add labels to the tree. In the nodes of the tree, the values of the covariates are represented with a, b or c. Is there a way to show the real value(s) of the variable in the nodes instead of a, b or c ? I found that the

Hi - I'm trying to append simple density histograms of a continuous variable to the leaves of an rpart tree. The splits in the tree are all levels of a factor and I'm hoping to make the histograms out of the subsets of the dataframe corresponding to the splits and for them to be attached to the appropriate leaf of the final tree. Any help would be much appreciated, thanks, Jon Zelner

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()

Hello, I just upgraded my Ubuntu Xenial system to R 3.5.1 (from 3.4.?) by changing the sources.list entry and doing an "apt-get dist-upgrade". Everything works except loading the rpart package in R: > library(rpart) Error: package or namespace load failed for ?rpart?: package ?rpart? was installed by an R version with different internals; it needs to be reinstalled for use with

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

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?

While I'm very pleased with the results I get with rpart and rpart.plot, I would like to change the scientific notation of the dependent variable in the plots into integers. Right now all my 5 or more digit numbers are displayed using scientific notation. I managed to find this: http://tolstoy.newcastle.edu.au/R/e8/help/09/12/8423.html but I do not fully understand what to change, and to

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

Hi, I am currently studying Decision Trees by using rpart package in R. I artificially created a data set which includes the dependant variable (y) and a few independent variables (x1, x2...). The dependant variable y only comprises 0 and 1. 90% of y are 1 and 10% of y are 0. When I apply rpart to it, there is no splitting at all. I am wondering whether this is because of the

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 just updated rpart to the latest version (3.1.0). There are a number of changes between this and previous versions, and some of the code I've been using with earlier versions (e.g. 3.0.2) no longer work. Here is a simple illustration of a problem I'm having with xpred.rpart. iris.test.rpart<-rpart(iris$Species~., data=iris[,1:4], parms=list(prior=c(0.5,0.25, 0.25))) + ) >