similar to: missing value replacement for test data in random forest

A few days ago I asked whether there were options other than na.action=na.fail for the R port of Breiman?s randomForest; the function?s help page did not say anything about other options. I have since discovered that a pdf document called ?The randomForest Package? and made available by Andy Liaw (who made the tool available in R---thank you) does discuss an option. It is an implementation of

Does anyone know a reason why, in principle, a call to randomForest cannot accept a data frame with missing predictor values? If each individual tree is built using CART, then it seems like this should be possible. (I understand that one may impute missing values using rfImpute or some other method, but I would like to avoid doing that.) If this functionality were available, then when the trees

I am having trouble with the rfImpute function in the randomForest package. Here is a sample... clunk.roughfix<-na.roughfix(clunk) > > clunk.impute<-rfImpute(CONVERT~.,data=clunk) ntree OOB 1 2 300: 26.80% 3.83% 85.37% ntree OOB 1 2 300: 18.56% 5.74% 51.22% Error in randomForest.default(xf, y, ntree = ntree, ..., do.trace = ntree, : NA not

In trying to execute this line in R (Version 1.7.1 (2003-06-16), under windows XP pro), with the randomForest library (about two weeks old) loaded, the program crashed: bost4rf <- rfImpute(TargetDensity~.,data=bost4rf0) Specifically, an XP dialog box popped up, saying ?R for windows GUI front-end has encountered a problem and needs to close.? That was the dialog saying asking whether I

Dear Administrator, I am using linux (suse 10.2). While attempting rfImpute, I am getting the following error message: > Members <- rfImpute(Status ~ ., data = Members) Error in .C("classRF", x = x, xdim = as.integer(c(p, n)), y = as.integer(y), : C symbol name "classRF" not in DLL for package "randomForest". I need the help to sort out above error.

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I tried rfImpute (randomForest) and as far as I understood should it replace NA`s using a proximity matrix: > set.seed(100000) > Subset5Imputed<-rfImpute(Sex~., data=Subset5) ntree OOB 1 2 300: 11.78% 12.36% 11.21% ntree OOB 1 2 300: 12.07% 12.64%

Hi, I'm looking to perform a classification analysis on an imbalanced data set using random Forest and I'd like to reproduce the weighted random forest analysis proposed in the Chen, Liaw & Breiman paper "Using Random Forest to Learn Imbalanced Data"; can I use the R package randomForest to perform such analysis? What is the easiest way to accomplish this task? Thanks,

Hi! I try to explain the efffect of (1) forest where i took samples's soils (* Lugar*: categorical variable with three levels), (2) nitrogen addition treatments (*Tra*: categorical variable with two levels) on total carbon concentration's soil samples (*C: *continue* *variable) during four months of sampling (*Time:* categorical and ordered variable with four levels). I fitted the

Dear List, I find Effects package very useful, but I believe I have found a bug in allEffects function. Please consider the following code: test <- data.frame(tries= round(runif(40, 5, 300)), tra = gl(4, 10, labels = c("V", "D", "C", "L")), prop= runif(40, 0, 1)) test$success <- round(with(test, tries*prop)) test$prop <- with(test,

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi all, There is now a package available on CRAN that provides an R interface to Leo Breiman's random forest classifier. Basically, random forest does the following: 1. Select ntree, the number of trees to grow, and mtry, a number no larger than number of variables. 2. For i = 1 to ntree: 3. Draw a bootstrap sample from the data. Call those not in the bootstrap sample the

Hi, Here is my data, the first 10 rows > u=regCond_all[1:10,] > dput(u) structure(c(999, 999, 999, 999, 999, 999, 999, 999, 999, 999, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 99, 1.9, 2, 1.97, 1.99, 1.83, 1.78, 1.6, 1.52, 1.52, 1.36, 10.53, 9.88, 9.88, 10.53, 10.53, 10.53, 5.26, 9.88, 10.53, 10.53, 5.4, 5.57, 5.46, 5.34, 5.5, 5.59, 5.62, 5.76, 6.23, 6.19,

I have been using Mandriva 2008.0 regularly since last Jan so I'm not very good. I have Tomb Raider Anniversary (TRA )and although i had to remove all the graphic options everytime it loads i.e. vsync shadow reflection and water effects, i could play it and i was happy. After some time i had to stop because i had a problem with video codecs and i had to install/remove a lot of things to get

Hello, I would like to convert date as a factor to represent time in a repeated measure situation in the following code. How would I do that? > d <- read.csv(file.path(dataDir,"data.csv"), as.is=T,stringsAsFactors = FALSE) > d[1:2,] id date a b c y 1 1 8/6/2008 Red 15 B 22 2 1 8/6/2008 Green 15 B

Hello group, I have an OSX 10.3.9 Server that I'm trying to back up with rsync 3.0.2. Since I didn't have any luck with the 10.3.9 patch and I do have 10.4 clients, I decided to try running the backup over AFP (Apple's file sharing protocol). I found a way to mount the AFP volume with root permissions and rsync 3.0.2 works very nicely using this on the 10.4 clients, preserving

Dear all , I would like to use the function randomForest to predict the probability of relocation failure of a GPS collar as a function of several environmental variables x (both factor and numeric: slope, vegetation, etc.) on a given area. The response variable y is thus success (0)/failure(1) of the relocation, and the sampling unit is the pixel of a raster map. My aim is to build a map

Hi, I've just switch from procmail to pigeonhole, and I'm seeing an issue (twice in six hours). I have spamassisn running via postfix milter, and dropping off via main.cf "mailbox_transport = lmtp:unix:/var/lib/imap/socket/lmtp" The issue is that I get a sequence of these log messages: Aug 21 19:43:59 lmtp(toppk): Debug: Junk: Mailbox opened because: lib-lda delivery Aug 21

Hi, I had two questions regarding random forests for regression. 1) I have read the original paper by Breiman as well as a paper dicussing an application of random forests and it appears that the one of the nice features of this technique is good predictive ability. However I have some data with which I have generated a linear model using lm(). I can get an RMS error of 0.43 and an R^2 of

Hi all, I am using the package "random forest" for random forest predictions. I like the package. However, I have fairly large data sets, and it can often take *hours* just to go through the "na.roughfix" call, which simply goes through and cleans up any NA values to either the median (numerical data) or the most frequent occurrence (factors). I am going to start

This buildbot appears to have been failing for several weeks now ( http://lab.llvm.org:8011/builders/clang-ppc64-elf-linux2/builds/19490 ). Does anyone know/own/care about it? ---------- Forwarded message ---------- From: <llvm.buildmaster at lab.llvm.org> Date: Mon, Sep 28, 2015 at 11:17 PM Subject: buildbot failure in LLVM on clang-ppc64-elf-linux2 To: Aaron Ballman <aaron at