similar to: OSX-specific Bug in randomForest

I've cc'd this to the package maintainer, Andy Liaw <andy_liaw at merck.com>. I'm not sure he reads this list. Duncan Murdoch On 2024-10-23 1:26 a.m., Stevie Pederson wrote: > Hi, > > It appears there is an OSX-specific bug in the function > `randomForest.default()` Going by the source code at >

Hi, I have a randomforest object "cost.rf" that was created in splus 8.0, now I need to use this trained RF model in R. So in Splus, I dump the RF file as below data.dump("cost.rf", file="cost.rf.txt", oldStyle=T) then in R, restore the dumped file, library(foreign) data.restore("cost.rf.txt") it works fine and able to restore the

Hello, I'm using the random forest package. One of my factors in the data set contains 41 levels (I can't code this as a numeric value - in terms of linear models this would be a random factor). The randomForest call comes back with an error telling me that the limit is 32 categories. Is there any reason for this particular limit? Maybe it's possible to recompile the module with a

Hi Dave, That's rather frustrating. I've found vroom (from the package vroom) to be helpful with large files like this. Does the following give you any better luck? vroom(file_name, delim = ",", skip = 2459465, n_max = 5) Of course, when you know you've got errors & the files are big like that it can take a bit of work resolving things. The command line tools awk

data.table's fread is also fast. Not sure about error handling. But I can merge 300 csvs with a total of 0.5m lines and 50 columns in a couple of minutes versus a lifetime with read.csv or readr::read_csv On Mon, 8 Apr 2024, 16:19 Stevie Pederson, <stephen.pederson.au at gmail.com> wrote: > Hi Dave, > > That's rather frustrating. I've found vroom (from the package

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

Could not find it in ?randomForest. Thank you for your help! -- Dimitri Liakhovitski Ninah.com Dimitri.Liakhovitski at ninah.com

All, I'm trying again with a slightly more generic version of my first question. I can extract the plotted values from hist(), boxplot(), and even plot.randomForest(). Observe: # get some data dat <- rnorm(100) # grab histogram data hdat <- hist(dat) hdat #provides details of the hist output #grab boxplot data bdat <- boxplot(dat) bdat #provides details of the boxplot

> From: Weiwei Shi > > it works. > thanks, > > but: (just curious) > why i tried previously and i got > > > is.vector(sample.size) > [1] TRUE Because a list is also a vector: > a <- c(list(1), list(2)) > a [[1]] [1] 1 [[2]] [1] 2 > is.vector(a) [1] TRUE > is.numeric(a) [1] FALSE Actually, the way I initialize a list of known length is by

Hi, I've a question about the RandomForest package. The package allows the extraction of a variable importance measure. As far as I could see from the documentation, the computation is based on the Gini index. Do you know if this extraction can be also based on other criteria? In particular, I'm interested in the info gain criterion. Best regards, Chris --

Hello there, I am working on an extremely unbalanced two class classification problems. I wanna use "classwt" with "down sampling" together. By checking the rfNews() in R, it looks that classwt is not working yet. Then I looked at the software from Salford. I did not find the down sampling option. I am wondering if you have any experience to deal with this problem. Do you

Hi, is it correct that i need ~ 2GB RAM that it's possible to work with the default setting ntree=500 and a data.frame with 100.000 rows and max. 10 columns for training and testing? P.S. It's possible calculate approximate the memory demand for different settings with RF? Many thanks & regards, Christian

I solved the mystery, but not the problem. The problem is that there's an unclosed quote somewhere in those 5 additional records I'm trying to access. So read.csv is reading million-character fields. It's slow at that. That mystery solved. However, the the problem persists: how to fix what is obvious to the naked eye - a quote not adjacent to a comma - but that read.csv can't

Hello everybody, I'm testing the randomForest package in order to do some simulations and I get some trouble with the prediction of new values. The random forest computation is fine but each time I try to predict values with the newly created object, I get an error message. I thought I was because NA values in the dataframe, but I cleaned them and still got the same error. What am I

Hi All, I am a PhD student in forestry science and I am working with LiDAR data set (huge data set). I am a brand-new in R and geostatistic (SORRY, my background it?s in forestry) but I wish improve my skill for improve myself. I wish to develop a methodology to processing a large data-set of points (typical in LiDAR) but there is a problem with memory. I had created a subsample data-base but

Dear R-users, While making a prediction using the randomForest function (package randomForest) I'm getting the following error message: "Error in predict.randomForest(model, newdata = CV) : No forest component in the object" Here's my complete code. For reproducing this task, please find my 2 data sets attached ( http://www.nabble.com/file/p17855119/data.rar data.rar ).

Hello I'd like to try out some functions in the package randomForest. Therefore, I did install this package. However, it is not possible to load the library, although I have R-Version 2.1.1 (i.e. later than 2.0.0). The commands I used and the Answers/Error from R is as follows: > install.packages("C://Programme//R//rw2011//library//randomForest_4.5-16.zip",

I am new to R and I'd like to use the randomForest package for my thesis (identifying important variables for more detailed analysis with other software). I have found extremely well written and helpful information on the usage of R. Unfortunately it seems to be very difficult to find similarly detailed tutorials for randomForest, and I just can't get it work with the information on

Hello, I've tried to install randomForest on a Ubuntu 8.04 Hardy Heron system. I've repeatedly rec'd the error: > install.packages("randomForest", dependencies = TRUE) ERROR: compiliation failed for package 'randomForest' ** Removing '/home/admuser/R/i486-pc-linux-gnu-library/2.6/randomForest' The downloaded packages are in

Buenos días: Otra opción es escribir directamente el nombre de la función en la consola de R: > randomForest function (x, ...) UseMethod("randomForest") En este caso, la función randomForest() llama a UseMethod() para seleccionar el método adecuado. Podemos ver los métodos para randomForest con la función methods(): > methods(randomForest) [1] randomForest.default*