similar to: question about Rpvm, SNOW, etc.

At office, using the internal LAN at my disposal, I'm having a go at parallel computing - to begin with - with pvm, rpvm & snow. The two boxes are as follows Remote machine uffbsd: CPU: Intel(R) Pentium(R) 4 CPU 2.00GHz (1994.13-MHz 686-class CPU) Origin = "GenuineIntel" Id = 0xf24 Stepping = 4 real memory = 260046848 (248 MB) This machine NbBSD: CPU: Mobile Intel(R)

Hi, does anybody know how to use the dual processors in the machines of a cluster? I am using R with rpvm and snow packages. I usually start pvm daemon and add host machines first, and then run R to start my computing work. But I find that only one processor in each machine is used in this way and the other one always stays idle. Is there any simple way to tell pvm to use the two processors at

Gracias Carlos y Javier, ntrees es el nº de árboles y treesize sus respectivos tamaños (nº de nodos) ntree: Number of trees to grow. This should not be set to too small ...... treesize: Size of trees (number of nodes) in and ensemble. Puse 1000 árboles (ntree=1000), si, pero la función treesize te da el nº de nodos: treesize(RFfit, terminal=TRUE) me da un vector de 1000 elementos (uno

Caveat: I've only played with this a couple of years ago... I believe you can just add each host _twice_ (or as many times as the number of CPUs at that host) to get both CPUs to work. Andy From: Paul Y. Peng > > Hi, > > does anybody know how to use the dual processors in the > machines of a cluster? I am using R with rpvm and snow > packages. I usually start pvm

Hi, I am using randomForests for a classification problem. The predict function in the randomForest library, when asked to return the probabilities, has precision of two digits after the decimal. I need at least four digits of precision for the predicted probabilities. How do I achieve this? Thank you, Nagu

Dear mailinglist members, I was wondering if there was a way to re-evaluate the instances of a tree (in the forest) again after I have manually changed a splitpoint (or split variable) of a decision node. Here's an illustration: library("randomForest") forest.rf <- randomForest(formula = Species ~ ., data = iris, do.trace = TRUE, ntree = 3, mtry = 2, norm.votes = FALSE) # I am

Si, Carlos. Yo hago lo mismo, pero esos mismos numeritos salen enormes. > treesize(RFfit) [1] 4304 4302 4311 4319 4343 4298 4298 4311 4349 4327 4331 4317 4294 4321 4283 4362 [17] 4300 4330 4266 4331 4308 4352 4294 4315 4372 4349 4331 4347 4329 4348 4298 4335 [33] 4346 4396 4345 4313 4293 4276 4353 4272 4304 4325 4317 4336 4308 4351 4374 4324 [49] 4386 4359 4311 4346 4300

Dear Friends, I'm trying to learn to use parallel computation using snow & rpvm Now, I have two boxes: 1) Pentium 4, pvm 3.4.5, R 2.2.0 with FreeBSD 5.4 (box uffbsd); 2) PowerPC, pvm 3.4.5, R 2.1.1 with Debian Linux latest stable (box powerpclinux); I'm using the instructions in (found in snow.pdf) http://www.stat.uiowa. edu/~luke/R/cluster/cluster.html. Now ** from the box

Muchas gracias Carlos, como siempre. Es raro que se me pasase. En su momento miré todos los argumentos del RF, como hago siempre, pero ese lo había olvidado. La verdad es que funcionaba estupendamente, pero me parecía extraño. Aunque dado que los RF no sobreajustan, no hay problema con que sus árboles sean todo lo grandes que quieras. Lo he testado con una base de datos externa y explica

Buenas Tengo en un string guardado lo siguiente: > parametros [1] "ntree=10" "ntree=30" "ntree=50" "ntree=100" "ntree=200" Con un bucle for quiero ir metiendolo en el modelo, pero no se muy bien como hacerlo, ya que con deparse no me funciona, con get tampoco (obvio, no es un objeto), y no se muy bien como hacerlo de manera dinamica

Hi, Since I've had no replies on my previous post about my problem I am posting it again in the hope someone notice it. The problem is that the randomForest function doesn't take datasets which has instances only containing a subset of all the classes. So the dataset with instances that either belong to class "a" or "b" from the levels "a", "b" and

Hi, I need help with the randomForest prediction. i run the folowing code: > iris.rf <- randomForest(Species ~ ., data=iris, > importance=TRUE,keep.forest=TRUE, proximity=TRUE) > pr<-predict(iris.rf,iris,predict.all=T) > iris.rf$votes[53,] setosa versicolor virginica 0.0000000 0.8074866 0.1925134 > table(pr$individual[53,])/500 versicolor virginica 0.928

I've been training randomForest models on 7 million rows of data (41 features). Here's an example call: myModel <- randomForest(RESPONSE~., data=mydata, ntree=50, maxnodes=30) I thought surely with only 50 trees and 30 terminal nodes that the memory footprint of "myModel" would be small. But it's 65 megs in a dump file. The object seems to be holding all sorts of

Hello, When I plot or look at the error rate vector for a random forest (rf$err.rate) it looks like a descending function except for a few first points of the vector with error rates values lower(sometimes much lower) than the general level of error rates for a forest with such number of trees when the error rates stop descending. Does it mean that there is a tree(s) (that is built the first in

Dear R Users, I'm using the randomForest package and would like to generate partial dependence plots, one after another, for a variety of variables: m <- randomForest( s, ... ) varnames <- c( "var1", "var2", "var3", "var4" ) # var1..4 are all in data frame s for( v in varnames ) { partialPlot( x=m, pred.data=s, x.var=v ) } ...but this

Hi, I'm trying to extract the rules from every tree in the random forest model that I've created. I've used randomForest2Rules function from "rattle" package however I get an error "Error in if (var.class == "character" | var.class == "factor") { : argument is of length zero" Sample code: library(rattle) library(randomForest) iris.10tree

Has anyone sucessfully used the maxnodes feature in randomForest? I tried setting it, but when it is non-NULL I always get back a forest in which all trees have size 1. I am using a continuous response (regression). Any help would be appreciated. Thanks. [[alternative HTML version deleted]]

I've trying to fix the check error in rpvm package. Right now on my Mac, I use darwinport to install PVM and I can get rpvm to pass the check using 'R -arch x86_64 CMD check'. However the default 'R CMD check' uses i386 which fails since PVM is built for x86_64 only. So is there a way to ensure proper architecture is used that matches that of the external library like PVM?

Hi, In R, how to do missing value replacement for test data in randome forest in the way Breiman decribed. thanks in advance iris

Guys, I used Random Forest with a couple of data sets I had to predict for binary response. In all the cases, the AUC of the training set is coming to be 1. Is this always the case with random forests? Can someone please clarify this? I have given a simple example, first using logistic regression and then using random forests to explain the problem. AUC of the random forest is coming out to be