similar to: Issue with dataset inclusion in CRAN packages

********************************************************************** This email and any files transmitted with it are confidential and intended solely for the use of the individual or entity to whom they are addressed. If you have received this email in error please notify the system manager. ********************************************************************** Hi Just installing R and some

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?

? data=read.table("http://statcourse.com/research/boston.csv", , sep=",", header = TRUE) ? library(rpart) ? fit=rpart (MV~ CRIM+ZN+INDUS+CHAS+NOX+RM+AGE+DIS+RAD+TAX+ PT+B+LSTAT) Please: Show me the tree. Mark -------- Original Message -------- Subject: Re: [R] help with rpart From: "Stephen Milborrow" <[1]milbo at sonic.net>

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

Hello All, I've problems to interpret the contourplot for a binary response dependent variable. For example, I ploted the survived probability for the titanic3 dataset, http://www.geocities.com/useebi/data/CondenContour.jpeg Trellis displayed two panels of contour, one for dead and one for alive. However, the contours between the two panels didn't conform to the rule of

(Sorry for the slightly off topic post) I'm giving a talk (on data mining) to some non-statisticians (who're all postgrad students, but a mixture of Science and Commerce majors). My intention is to show them the importance of statistics when doing data mining. What I'm thinking of doing is using, hopefully, two datasets. One from scientific area and another that is

Not exactly an R question but.......... Does anyone have a dataset with a nice story behind it that would be suitable for regression analysis but is missing data in one or two covariates ? I am playing around with different missing data routines and need a nice dataset to play with. Thanks, Steve __________________________________________________ Yahoo! News - Today's headlines

In R, 0 ^ NaN yields Inf. I would have expected NaN or perhaps 0. Is this behaviour intended? >sessionInfo() R version 2.8.0 (2008-10-20) i386-pc-mingw32 locale: LC_COLLATE=English_United States.1252;LC_CTYPE=English_United States.1252;LC_MONETARY=English_United States.1252;LC_NUMERIC=C;LC_TIME=English_United States.1252 attached base packages: [1] stats graphics grDevices utils

Dear helpers, I am a Dutch student from the Erasmus University. For my Bachelor thesis I have written a script in R using boosting by means of classification and regression trees. This script uses the function the predefined function rpart. My input file consists of about 4000 vectors each having 2210 dimensions. In the third iteration R complains of a lack of memory, although in each iteration

Stephen, thanks for your effort. The more appropriate list for this discussion is probably R-devel (as far as I understand it) so I've moved the discussion there. Related topics have already been discussed in the past. Specifically, I remember contributions by Paul Johnson ("rockchalk" package) and John Fox ("effects" and "car" package) as their packages

Dear All: I tried to replicate a case study described by Prof. Harrell in Chapter 7 of his Regression Modeling Strategies book, but failed on using anova.Design to reproduce his table 7.1, Following is the code: rm(list=ls()) library(Hmisc) library(Design) getHdata(counties) counties$older <- counties$age6574 + counties$age75 label(counties$older) <- '% age >= 65, 1990'

Dear R-users: I can not reproduce figure 4 of *Statistical Tables and Plots using S and LATEX* by Prof. Frank Harrell with the following code: rm(list=ls()) library(Hmisc) getHdata(pbc) attach(pbc) age.groups <- cut2(age, c(45,60)) g <- function(y) apply(y, 2, quantile, c(.25,.5,.75)) y <- with(pbc, cbind(Chol=chol,Bili=bili)) # You can give new column names that are not legal S names

Full_Name: Stephen Milborrow Version: R 2.8.0 OS: Windows XP Submission from: (NULL) (81.159.65.46) Under certain conditions, R 2.8.0 incorrectly echoes integer literals without the "L" suffix. R 2.7.2 is ok. To reproduce, create two files as follows: a.R containing: source("b.R", echo=TRUE) b.R containing: x <- 1L Now execute: Rterm --vanilla --silent <a.R This

Dear R-users: I am trying to use the following code to reproduce the figures on page 140 of Prof. Frank Harrell's book 'Regression Modeling Strategies': rm(list=ls()) options(width=128) library(Hmisc) library(Design) getHdata(counties) counties$older <- counties$age6574 + counties$age75 label(counties$older) <- '% age >= 65, 1990' counties$pdensity <-

Hi, It sometimes happens that some packages get included to R like for example the parallel package. I was wondering if there is a process to decide whether or not to include a package in the core implementation of R? For example, why not include the Rcpp package, which became for a lot of user the main tool to extend R? What is our view on the (not so well known) dotCall64 package which is an

Hi, I would like to step-through a non-visible function. but apparently I don't know enough about namespaces to get that to work: > methods(predict) ... deleted lines ... [27] predict.rpart* predict.smooth.spline* [31] predict.survreg.penal* Non-visible functions are asterisked > debug(predict.rpart) Error: Object "predict.rpart" not found >

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

Hi, I am using rpart decision trees to analyze customer churn. I am finding that the decision trees created are not effective because they are not able to recognize factors that influence churn. I have created an example situation below. What do I need to do to for rpart to build a tree with the variable experience? My guess is that this would happen if rpart used the loss matrix while creating

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, 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