similar to: R-help Digest, Vol 118, Issue 20

Hi, I've searched R-help and haven't found an answer. I have a set of data from which I can create a classification tree using rpart. However, what I'd like to do is predefine the blank structure of the binary tree (i.e., which nodes to include) and then use a package like rpart to fit for the optimal splitting criteria at each of the predefined nodes. Does such a package exist?

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

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

The package mvpart is now available. mvpart includes partitioning based on (1) multivariate numeric responses and (2) dissimilarity matrices. The package mvpart is a modification of rpart -- -- authors of original: Terry M Therneau and Beth Atkinson <atkinson at mayo.edu>, and R port of rpart Brian Ripley <ripley at stats.ox.ac.uk>. Includes some modified routines from vegan --

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

Dear R's I'm trying to use specific rather than random cross-validation groups in mvpart. The man page says: xval Number of cross-validations or vector defining cross-validation groups. And I found this reply to the list by Terry Therneau from 2006 The rpart function allows one to give the cross-validation groups explicitly. So if the number of observations was 10, you could use

When I run R CMD check on the survival package I invariably get a note: ... * checking for file ?survival/DESCRIPTION? ... OK * this is package ?survival? version ?3.2-6? * checking CRAN incoming feasibility ... NOTE Maintainer: ?Terry M Therneau <therneau.terry at mayo.edu>? ... This is sufficient for the auto-check process to return the following failure message: Dear maintainer,

I'm updating some (very) old code, and one particular option of its plot method depends on a once-was-true trick polygon(x, y, border=TRUE, col=0) polygon(x, y, border=TRUE, density=0) would draw the polygon AND erase whatever was underneath it back to background color. Is there a reliable way to do this with the current R (standard graphics)? Terry Therneau PS For the

I am trying to clear up all the "NOTE"s before a CRAN submission, but am a bit confused about this one.?? What is it complaining about -- that it doesn't like my name? ... * checking for file ?deming/DESCRIPTION? ... OK * this is package ?deming? version ?1.4-1? * checking CRAN incoming feasibility ... [7s/18s] NOTE Maintainer: ?Terry Therneau <therneau.terry at mayo.edu>?

-------- Original Message -------- Subject: Re: How to obtain nonparametric baseline hazard estimates in the gamma frailty model? Date: Mon, 04 Nov 2013 17:27:04 -0600 From: Terry Therneau <therneau.terry at mayo.edu> To: Y <yuhanusa at gmail.com> The cumulative hazard is just -log(sfit$surv). The hazard is essentially a density estimate, and that is much harder. You'll notice

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

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

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).

I cloned therneau/survival and the installation failed since there is no definition for exported function survfit(). A file seems to be missing - there is survfit0() and survfit0.R but, compared to CRAN, no survfit.R. Georgi Boshnakov ---------------------------------------------------------------------- Message: 1 Date: Thu, 05 Sep 2019 12:53:11 -0500 From: "Therneau, Terry M.,

Dear Terry, Does fitting group + age:group instead of age*group solves your problem? Best regards, ir. Thierry Onkelinx Instituut voor natuur- en bosonderzoek / Research Institute for Nature and Forest team Biometrie & Kwaliteitszorg / team Biometrics & Quality Assurance Kliniekstraat 25 1070 Anderlecht Belgium To call in the statistician after the experiment is done may be no more

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 Terry, On 03/16/2016 08:03 AM, Therneau, Terry M., Ph.D. wrote: > Is the phrase "index <- match(x, sort(unique(x)))" reliable, in the > sense that it will never return NA? This is assuming that match() and unique() will never disagree on equality between 2 floating point values. I believe they share some code internally (same hashing routine?), so maybe it's reliable.

You can legally cast a function pointer to another function pointer, where the signatures differ. (It is not legal to cast between data and function pointers.) I would make typedefs for the various signatures, as the casting syntax is more readable then. Bill Dunlap TIBCO Software wdunlap tibco.com On Fri, Dec 29, 2017 at 10:13 AM, Therneau, Terry M., Ph.D. < therneau at mayo.edu> wrote:

I've created some Splus code for a microarray problem that - needed to be in C, to take advantage of some sparse matrix properties - uses a cholesky decompostion as part of the computation For the cholesky, I used the cholesky2 routine, which is a part of the survival library. It does just what I want and I'm familiar with it (after all, I wrote it). In Splus, this all works

--- included message ---- Thus, my question is: *What common measures exists for ranking/measuring variable importance of participating variables in a CART model? And how can this be computed using R (for example, when using the rpart package)* ---end ---- Consider the following printout from rpart summary(rpart(time ~ age + ph.ecog + pat.karno, data=lung)) Node number 1: 228 observations,