similar to: rsurv in ipred

The package ipred is uploaded to CRAN. The main focus of the package is the calculation of improved predictors in classification tasks. Misclassification error can be improved by bootstrap aggregated classification trees and/or the framework of indirect classification. Furthermore, a unified interface for the estimation of misclassification error completes the features of ipred. We try to make

The package ipred is uploaded to CRAN. The main focus of the package is the calculation of improved predictors in classification tasks. Misclassification error can be improved by bootstrap aggregated classification trees and/or the framework of indirect classification. Furthermore, a unified interface for the estimation of misclassification error completes the features of ipred. We try to make

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Dear useRs, a new package for tree-structured regression is available on CRAN. This package implements a unified framework for recursive partitioning which embeds tree-structured regression models into a well defined theory of conditional inference procedures. Stopping criteria based on multiple test procedures are implemented. The methodology is applicable to all kinds of regression problems,

Dear List, I have a question regarding "ipred" package. Under 10-fold cv, for different knn ( = 1,3,...25), I am getting same misclassification errors: ############################################# library(ipred) data(iris) cv.k = 10 ## 10-fold cross-validation bwpredict.knn <- function(object, newdata) predict.ipredknn(object, newdata, type="class") for (i in

Dear R users, I have been using the rpart procedure to predict the occurrence of depression in a large data file. Since the prevalence is very low (5%), I have been using classification trees with a loss matrix that penalized false negatives more than false positives. I have become interesested in bagging these (successful!) classification trees, and have been using the ipred package for

I wanted to report this issue here so others may not find themselves alone and as the author is apparently active on the list. I havent done an exhaustive test by any means, cause I dont have time. But here's a small example. Apparently the "ns" argument is the one that is killing it. I've gotten several different segfault messages, the only other one I remember said "out

Hi All, I'm a newcomer to the R community, just started learning R. I need to use partykit for developing CART. But I'm not getting any document/help for using partykit/rpart. http://www.agrocampus-ouest.fr/math/useR-2009/slides/Hothorn+Zeileis.pdfgives some example, but it doesn't tell how my own data should be formatted so that it can be accepted by partykit. Example,

Hello! I'm Taeho, a graduate student in South Korea. In order to get .632+ bootstrap error using random forest, I have tried to use 'ipred' package; more specifically the function 'errorest' has been used. Following the guidelines, I made a simple command line like below: error<-errorest(class ~ ., data=data, model=randomForest, estimator = "632plus")$err

Dear all, Does anybody can explain this: different results got when all the same parameters are used in the errorest() in library ipred, as the following? errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv", est.para=control.errorest(k=3), mtry=2)$err [1] 0.03333333 > errorest(Species ~ ., data=iris, model=randomForest, estimator = "cv",

Hello List, I`m trying to make prediction using a bagged tree with the package ipred. I tried to follow the manual but I`m getting an error message. Also browsing through the list-archive I didn`t find any hint. Maybe someone can help me? selbag <- bagging(SOIL_UNIT ~., data=traindat.bin, coob=TRUE) Error in dimnames(X) <- list(dn[[1L]], unlist(collabs, use.names = FALSE)) :

Dear all, can anybody help me with the program below? The function predict.lda seems to be defined but cannot be used by errortest. The R version is 1.7.1 Thanks in advance, Stefan ---------------- library("MASS"); library("ipred"); data(iris3); tr <- sample(1:50, 25); train <- rbind(iris3[tr,,1], iris3[tr,,2], iris3[tr,,3]); test <- rbind(iris3[-tr,,1],

Hi Manuel, find our SoC proposal below. Best wishes, Torsten & Achim _______________________________________________________________________ Party On! New Recursive Partytioning Tools. Mentor: Torsten Hothorn & Achim Zeileis Short Description: The aim of the project is the implementation of recursive partitioning methods ("trees") which aren't available in R at the

Dear R-users, If you use the exact Wilcoxon test in the coin package, I would like make you aware of that SPSS/StatXact MAY perform a round-off before doing their exact Wilcoxon-Mann-Whitney test (if you ever are unlucky enough not to use R). I have data from two treatments and was surprised to find that SPSS (15 under Windows) and R differed in their p-values (0.167 resp. 0.172). It turns out

Last week Giovanni Parrinello posted a message asking why various packages were loaded when he loaded an .Rdata file. Brian Ripley replied saying he thought it was because the saved workspace contained a reference to the namespace of ipred. (Correspondence copied below). This begs the question: how did the reference to the namespace of ipred come to be in the .Rdata file? Brian did say it is

Last week Giovanni Parrinello posted a message asking why various packages were loaded when he loaded an .Rdata file. Brian Ripley replied saying he thought it was because the saved workspace contained a reference to the namespace of ipred. (Correspondence copied below). This begs the question: how did the reference to the namespace of ipred come to be in the .Rdata file? Brian did say it is

Hello all. I've been attempting to utilize the "survival" pkg ( http://cran.r-project.org/web/packages/survival/index.html), while reading through this guide (http://www.ms.uky.edu/~mai/Rsurv.pdf). I figured working through the guide would be the best way to go, before attempting my own data. I tried to utilize the Kaplain-Meier estimator as shown in the guide:

Since we use APIs added in Jansson 2.7 (e.g. json_string_length), then raise the minimum version required to that version. Fixes commit bd1c5c9f4dcf38458099db8a0bf4659a07ef055d. --- docs/guestfs-building.pod | 2 +- m4/guestfs-libraries.m4 | 2 +- 2 files changed, 2 insertions(+), 2 deletions(-) diff --git a/docs/guestfs-building.pod b/docs/guestfs-building.pod index bf80984e4..db429f1a9

Hi All, I'm using the party package to grow conditional inference trees for survival analysis. When I used party version party_0.9-9991 everything worked well, but when I update to party_1.0-2 (due to using 64bit R), I get an error. For simplicity I will show the error I get for the example in the party documentation: ### survival analysis if (require("ipred")) {

On 08/17/2018 10:48 AM, Richard W.M. Jones wrote: > On Fri, Aug 17, 2018 at 10:30:35AM -0500, Eric Blake wrote: >> On 08/17/2018 10:16 AM, Richard W.M. Jones wrote: >>> Commit bd1c5c9f4dcf38458099db8a0bf4659a07ef055d changed all the code >>> to use Jansson instead of yajl. However it didn't change the OCaml >>> API name (which was still Yajl). >>>