Hi Philipp,
On 13 June 2007 at 15:36, Philipp Benner wrote:
| Package: r-cran-rpart
| Version: 3.1.35-1
| Severity: normal
| Tags: patch
|
|
| While implementing an algorithm using rpart() I discovered the following
| problem:
|
| (sample script rpart.bug.R)
|
--------------------------------------------------------------------------------
| library(rpart)
|
| pima <- read.table("pima-indians-diabetes.r.data")
| n <- nrow(pima)
So n is 'local'. Try 'n <<- nrow(pima)', or as pima is known, assign n in the
adaboost function.
| formula <- class ~ pregnant + glucose + blood.pressure + triceps +
| insulin + bmi + pedigree + age
|
| adaboost <- function() {
| w <- abs(rnorm(n))
| hyp <- rpart(formula, data=pima, weights=w, method="class")
| prev <- hyp
| }
|
--------------------------------------------------------------------------------
|
--------------------------------------------------------------------------------
| 01 > source("rpart.bug.R")
| 02 > adaboost()
| 03 Error in eval(expr, envir, enclos) : object "w" not found
| 04 > w <- abs(rnorm(n)) # global definition of w (with random values)
Not a global. Globals use '<<-'. You simply assign in the 'next environment'.
| 05 > adaboost() # now it works
| 06 > remove(w) # removing w again
| 07 > adaboost()
| 08 Error in eval(expr, envir, enclos) : object "w" not found
| 09 > debug(rpart)
| 10 > c <- adaboost()
| 11 debugging in: rpart(formula, frame, weight, method = "class")
| 12 debug: {
| 13 [...]
| 14 debug: m <- eval(m, parent.frame())
| 15 Browse[1]>
| 16 Error in eval(expr, envir, enclos) : object "weight" not found
|
--------------------------------------------------------------------------------
|
| If rpart() is used in a function (so not at the toplevel environment) it
| will use the wrong weight vector and even fail if it is not defined in
| the top environment. I fixed that bug and attached the patch to this
| mail. However, I'm not completely sure whether the patch is correct
| although it seems to work.
I think this relates to lexical scopting -- see the R FAQ. I tend to avoid
these torny questions by explicitly passing variables so I'd try
adaboost <- function(mydata) {
w <- abs(rnorm(nrow(mydata)))
hyp <- rpart(formula, data=mydata, weights=w, method="class")
prev <- hyp
}
I may have missed something though. Can you provide you data file, or a
similar one, so that I could try with a complete example?
Dirk, making a guess that this may be for r-help rather than debian-bugs :)
|
| Regards
|
|
| -- System Information:
| Debian Release: lenny/sid
| APT prefers testing
| APT policy: (990, 'testing'), (500, 'unstable'), (500, 'stable')
| Architecture: powerpc (ppc)
|
| Kernel: Linux 2.6.18
| Locale: [EMAIL PROTECTED], [EMAIL PROTECTED] (charmap=ISO-8859-15)
| Shell: /bin/sh linked to /bin/bash
|
| Versions of packages r-cran-rpart depends on:
| ii libc6 2.3.6.ds1-13 GNU C Library: Shared libraries
| ii r-base-core 2.4.1-2 GNU R core of statistical
computin
| ii r-cran-survival 2.31-1 GNU R package for survival
analysi
|
| r-cran-rpart recommends no packages.
|
| -- no debconf information
|
| --
| Philipp Benner
| --- rpart/R/rpart.old.s 2007-06-09 12:59:13.000000000 +0200
| +++ rpart/R/rpart.s 2007-06-09 13:23:35.000000000 +0200
| @@ -2,7 +2,7 @@
| #
| # The recursive partitioning function, for S
| #
| -rpart <- function(formula, data, weights, subset,
| +rpart <- function(formula, data, subset, weights = NULL,
| na.action=na.rpart, method, model=FALSE, x=FALSE, y=TRUE,
| parms, control, cost, ...)
| {
| @@ -17,6 +17,7 @@
| m$x <- m$y <- m$parms <- m$... <- NULL
| m$cost <- NULL
| m$na.action <- na.action
| + m$weights <- weights
| m[[1]] <- as.name("model.frame")
| m <- eval(m, parent.frame())
| }
--
Hell, there are no rules here - we're trying to accomplish something.
-- Thomas A. Edison
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