similar to: anyone know why package "RandomForest" na.roughfix is so slow??

Hello, This is mostly to developers, but in case I missed something in my literature search, I am sending this to the broader audience. - Are there any plans in the works to make "time" classes a bit more friendly to the rest of the "R" world? I am not suggesting to allow for fancy functions to manipulate times, per se, or to figure out how to properly

Hello, This is mostly to developers, but in case I missed something in my literature search, I am sending this to the broader audience. - Are there any plans in the works to make "time" classes a bit more friendly to the rest of the "R" world? I am not suggesting to allow for fancy functions to manipulate times, per se, or to figure out how to properly

Hi everyone, I have another "Random Forest" package question: - my (presumably incorrect) understanding of the varImpPlot is that it should plot the "% increase in MSE" and "IncNodePurity" exactly as can be found from the "importance" section of the model results. - However, the plot does not, in fact, match the "importance"

Hey everyone, I know that I can call 'R' from other scripts, and that I can make command calls from 'R' (e.g., using system() ). But how can I get 'R' to RETURN values to the script that called it. E.g., I would like to be able to do something like the following (as a simpler example) from a bash script: #!/bin/bash myTest=echo /usr/local/bin/R --no-restore

Hi All, First let me state that I did search for a while on r-help, google, and using the "sos" package inside of 'R', without much luck. I want to know how to create a univariate time series from a set of data that will have huge time gaps in it. For instance, here is a snapshot of a piece of data that I would like to analyze: *Row queued_time

Hello all, First of all, thanks so those of you who helped me a week or so ago managing a time series with varying gaps between the data series in 'R'. (My final preferred solution was to use "its" function & then forecast(Arima( ) ). ) My next question is a general statistical question where I'd like some advice, for those willing / able to proffer any wisdom:

Hello All, Using the standard "summary" function in 'R', I ran across some odd behavior that I cannot understand. Easy to reproduce: Typing: summary(c(6,207936)) Yields:: Min. *1st Qu. Median Mean 3rd Qu. Max.* 6 *51990 104000 104000 156000 207900* None of these values are correct except for the minimum. If I perform "quantile(c(6,

Hello, A hopefully simple question. I use 'R' through emacs, but I suspect the following would occur with any manner of text editor: - my editor has a normally quite handy feature where it will automatically indent to the appropriate level when I start a new line. However, this occasionally creates cases where there is no friendly way to break a long line of code into

I have a question regarding NA in randomForest (in R). I have a dataset which include both numerical and non-numerical variables, and the data includes some NA. I tried to use na.roughfix but then i get an error message "na.roughfix only works for numeric or factor". I also tried rfImpute but this does not work either because I have some NA in my response variable. Does anyone have som

Hey everyone, I've been using 'R' long enough that I should have some idea of what the heck either expression() or eval() are really ever useful for. I come across another instance where I WISH they would be useful, but I cannot get them to work. Here is the crux of what I would like to do: presume df looks like this A B C === === === M 45 0 M

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

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

So I have a very big matrix of about 900 by 400 and there are a couple of NA in the list. I have used the following functions to impute the missing data data(pc) pc.na<-pc pc.roughfix <- na.roughfix(pc.na) pc.narf <- randomForest(pc.na, na.action=na.roughfix) yet it does not replace the NA in the list. Presently I want to replace the NA with maybe the mean of the rows or columns or

Hello all, I have a strange / interesting problem that might be 'R' settings themselves, or it might be something with the OS. I am using the RODBC library. I have a script that goes out and, before making a query for a big data set, will first query for the column names of the data set. The column names could sometimes be quite long (e.g., "Time Background Estimation

Hello all, I have a peculiar and particular bug that I stumbled across with ggplot2. I cannot seem to replicate it with anything other than my specific data set. Here is the problem: - when I try to plot a histogram, allowing for ggplot2 to decide the binwidths itself, I get the following error: - stat_bin: binwidth defaulted to range/30. Use 'binwidth = x' to

Hello developers, I noticed that if I am running 'R', type "rm(list=objects())" and "gc()", 'R' will still be consuming (a lot) more memory than when I then close 'R' and re-open it. In my ignorance, I'm presuming this is something in 'R' where it doesn't really do a great job of garbage collection... at least not nearly as well as

Hello, I have a large data set 320.000 rows and 1000 columns. All the data has the values 0,1,2. I wrote a script to remove all the rows with more than 46 missing values. This works perfect on a smaller dataset. But the problem arises when I try to run it on the larger data set I get an error “cannot allocate vector size 1240 kb”. I’ve searched through previous posts and found out that it might

I've built two RF objects (RF1 and RF2) and have tried to combine them, but I get the following error: Error in rf$votes + ifelse(is.na(rflist[[i]]$votes), 0, rflist[[i]]$votes) : non-conformable arrays In addition: Warning message: In rf$oob.times + rflist[[i]]$oob.times : longer object length is not a multiple of shorter object length Both RF models use the same variables, although

Hi I listened to all your advise and ran my data on a computer with a 64 bits procesor but i still get the same error saying "it cannot allocate a vector of that size 1240 kb" . I don't want to cut my data in smaller pieces because we are looking at interaction. So are there any other options for me to try out or should i wait for the development of more advanced computers!

Hello R-user! I am running R 2.7.0 on a Power Book (Tiger). (I am still R and statistics beginner) I try to find the most important variables to divide my dataset as given in a categorical variable. code: Test.rf4<-randomForest(Sex~.,na.action=na.roughfix, data=Subset4, importance=TRUE, proximity=TRUE, ntree=10000, do.trace=1000, keep.forest=FALSE) My dataset contains also ordered