similar to: rpart question

Hello: I am using rpart and would like more background on how the splits are made and how to interpret results - also how to properly use text(.rpart). I have looked through Venables and Ripley and through the rpart help and still have some questions. If there is a source (say, Breiman et al) on decision trees that would clear this all up, please let me know. The questions below pertain to a

Hello all, I'm was going through the help for ?rpart.object And noticed some inconsistencies, Some might be a mistake in the help file and some might be my misunderstanding. The help in the section: value -> frame (first paragraph), states that: > yval, the fitted value of the response at each node, *and splits, a two > column matrix of left and right split labels for each node. *

I have created a multivariate regression tree using mvpart, with 3-4 responses. Though the plot shows bargraphs for each response, I would like to have the VALUES of the responses printed or indicated (via a scale or something) alongside the bargraph. Is this possible ?? Thanks, Manjunath [[alternative HTML version deleted]]

Hi Prof. Williams, thanks for your suggestion. The updated code is below. It turns out it was a matter of displaying the second column in yval to get the number of N and subtracting it from the n column in the frame to get the number of Y remaining in a binary example. once this is added now the function returns the rules along with Y and N count affected by the resulting rule. I am ccing

A few weeks back I posted that the subset feature of rpart was not working when predicting a categorical variable. I was able to figure out a simple solution to the problem that I hope can be included in future editions of rpart. I also include a fix for another related problem. The basic problem is that when predicting a categorical using a subset, the subset may not have all the categories

(Apparently, I posted this to the wrong place. I am hopefully posting this is the correct place now. If not, please advise.) A few weeks back I posted that the subset feature of rpart was not working when predicting a categorical variable. I was able to figure out a simple solution to the problem that I hope can be included in future editions of rpart. I also include a fix for another related

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

Dear R-helpers, Can you help me to see why "code 1" gives error while "code 2" runs fine? The only difference in the data is the distribution of age categories. I am attaching the session after the code. Many thanks. XL library(survival) library(rpart) # code 1 n <- 20 age <- rep(1:3, c(2, 3, 15)) eg<- data.frame(rexp(n), rbinom(n,1,prob=.3), age=age)

Full_Name: Bill Wheeler Version: 2.0.1 OS: Windows 2000 Submission from: (NULL) (67.130.36.229) The program to reproduce the error is below. I am calling rpart with a user-defined split function for a binary response variable and one continuous independent variable. The split function works for some datasets but not others. The error is: Error in "$<-.data.frame"(`*tmp*`,

Dear R Users: I have race-ethnicity groups identified in the factor variable Ethnic_G. I need to collapse Ethnic_G into a new variable with only two factors, 1 (White, non-Hispanic) and 2 (Minority). As seen in the code and output below, the recoded race-ethnicity variable is put into logical format, not factor format. I've used library(car) and the package was updated. Any ideas on

Hello all, I am trying to run a simulation. the simulation presented below. > rep=5 > sr=.10 # selection ratio > pmin=.10 # minority ratio > nap=1000 # total number of applicant > nsle=sr*nap # number of ee selected > nb=nap*pmin # number of minority > nw=nap-nb # number of majority > mb=100 # mean minority > sb=15 # sd minority > mw=100 # mean majority > sw=15 #

Dear all R users, I got a strange problem while working with SPSS data : I wrote following : library(foreign) data.original = as.data.frame(read.spss(file="c:/Program Files/SPSS/Employee data.sav")) data = as.data.frame(cbind(data.original$MINORITY, data.original$EDUC, data.original$PREVEXP, data.original$JOBCAT, data.original$GENDER)) colnames(data) = c('MINORITY',

Am 05.05.2016 um 23:19 schrieb Tanya Lattner: > Having a code of conduct like this is just as bad as having no code > of conduct at all. It trivializes the importance of a code of conduct > and its pretty much impossible to enforce. Regardless of what kind CoC you have: if it comes to having to enforce it, the community has stopped being open and welcoming. So I think this approach is

> On 19 Jul 2016, at 04:18, Mehdi Amini via llvm-dev <llvm-dev at lists.llvm.org> wrote: > > We had a long thread about that a few weeks (months?) ago: the conclusion (as I remember) was roughly a guideline to “always start a new revision to have a proper mailing-list thread starting with context (i.e. patch description)” > (and my dissident minority opinion that it is only

On 06/07/2017 11:24 AM, James Hogarth wrote: > > Mark stop with the flame baiting please. > > This is nothing systemd specific - and keep in mind /var/tmp is a > persistent temp area unlike /tmp which as it's tmpfs by default is of > course emptie don boot. I would wholeheartedly disagree. This IS something systemd specific. I have never seen init.d blow itself up over

In our recent workshop on "Multilevel Modeling in R" we discussed handling data for multilevel modeling. An classic example of such data are test scores of students grouped into schools. We may wish to model the scores as functions of both student-level covariates and school-level covariates. Such data are often organized in a multi-table format with a separate table for each level of

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

> You get the code you want with > > @foo = external hidden global i8 > > or > > @foo = external protected global i8 Sorry, not quiet. What you get is leaq foo(%rip), %rax Since it is still assuming foo is a position in the file. So yes, we need a way to declare an absolute value. If we can declare it, we may as well use the same construct to define it. I guess the

Hi all What is the best strategy to add another storage to an existing virtual mail system ? Move some domains to the new storage and create symlinks ? Switch to dovecot hashing ? But in this case what is the easy-east way to migrate ? Thanks for any suggestions or tips !

On Tue, Oct 25, 2016 at 6:52 PM, Peter Collingbourne <peter at pcc.me.uk> wrote: > So, to summarise the overall consensus on this thread: > - We should introduce a GlobalConstant class which supports definitions > and declarations > - It should derive from GlobalValue > - No type changes for GlobalValue (i.e. still required to be pointer type) > - To communicate the range