similar to: R: rulefit error on Linux

Dear List, I would greatly appreciate help on the following matter: The RuleFit program of Professor Friedman uses partial dependence plots to explore the effect of an explanatory variable on the response variable, after accounting for the average effects of the other variables. The plot method [plot(summary(rq(y ~ x1 + x2, t=seq(.1,.9,.05))))] of Professor Koenker's quantreg program

Hi, I'm using R to treat a table (with a lot of missing values) with Rulefit. The matter is when I use the command rfmod. Actually, I don't know how to deal with the error message. I don't know were "true" or "false" is missing. Someone can help me? Thanks The following part is the script I used with the error at the end. platform = "windows" rfhome

Hello LIST, In using Rulefit I;ve bee nabel to fit a model using rulefit without incident. However, when trying to use intnull and interact things go astray. > rf=rulefit(x,"N", cat.vars="H", > not.used=c("G","T"),huber=0.9,path.speed=1); [snip] RuleFit model 2/26/2008 2:17p ave(abs(error)) terms path steps 84.16 110

I have a 64-bit windows box - Intel Xeon CPU E7340 @ 2.4GHz 31.9GB of RAM I have R 2.11.1 (64bit) running on it. My csv data is 3.6 GB (with about 15 million obs, 120 variables.) ------------------------------------------------ I have successfully imported the data above into R. No problem. Now I am trying to run 'rpart' on my data. But I got the following error : Error: cannot

Webinar signup: Advances in Gradient Boosting: the Power of Post-Processing December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: * Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? * Applications Benefiting from

Webinar: Advances in Gradient Boosting: the Power of Post-Processing TOMORROW: December 14, 10-11 a.m., PST Webinar Registration: http://2.salford-systems.com/gradientboosting-and-post-processing/ Course Outline: I. Gradient Boosting and Post-Processing: o What is missing from Gradient Boosting? o Why post-processing techniques are used? II. Applications Benefiting from Post-Processing:

I have a one-variable data set in R. The plot of histogram of my numerical variable suggests an inverse gaussian distribution. How can I obtain best estimation for the two parameters of inverse gaussian based on my data? Thanks. -- View this message in context: http://n4.nabble.com/estimate-inverse-gaussian-in-R-tp949692p949692.html Sent from the R help mailing list archive at Nabble.com.

I have found an existing image on Amazon EC2 including R. But unfortunately, it is 32-bit R on 32-bit Linux. Does anybody know if there exists an mage (R 64-bit on Linux 64-bit) on Amazon EC2? Or how can I install 64-bit R on my own Linux instance there? Thanks. -- View this message in context:

Hi all, sorry for my english, but I don't speak yours language. I'm trying to use bart() and rbf(). The package I'm using now is "BayesTree" and "neural", respectively. I could create the models, but I can't predict my test data. Does anyone have such an experience? Any advice is appreciated! Thank you in advanced!. Andr? -- View this message in

Currently, we have developed an R package for our company's internal use (at least for now). I have successfully built the package and generated zip file which can be easily installed. However, I am not sure how to generate the big HELP file in PDF. Right now, I have about 30+ Rd files for this internal package. I can run 'R CMD Rd2dvi' and WinEdt to create one pdf file

Have anybody used Decision Tree in Python or C++? (or written their own decision tree implementation in Python or C++)? My goal is to run decision tree on 8 million obs as training set and score 7 million in test set. I am testing 'rpart' package on a 64-bit-Linux + 64-bit-R environment. But it seems that rpart is either not stable or running out of memory very quickly. (Is it

Hi there, I have a statistics question on a classification problem: Suppose I have 1000 binary variables and one binary dependent variable. I want to find a way similar to PCA, in which I can find a couple of combinations of those variables to discriminate best according to the dependent variable. It is not only for dimension reduction, but more important, for finding best way to construct

I think that I have successfully installed libreadline.so.6. But still got an error -- libreadline.so.6 needed during R installation on SUSE Linux. help is really appreciated! =============================== # ls -lt /usr/local/lib total 4088 -rw-r--r-- 1 root root 168858 Oct 18 07:15 libhistory.a lrwxrwxrwx 1 root root 15 Oct 18 07:15 libhistory.so -> libhistory.so.6 lrwxrwxrwx 1

Ok is there a way to do it with decision tree? I just need to make the decision rules. Perhaps I can pick one of the trees used with Random Forrest. I am somewhat familiar already with Random Forrest with respective to bagging and feature sampling and getting the mode from the leaf nodes and it being an ensemble technique of many trees. I am just working from the perspective that I need

Hi I'm trying to get the top decision rules from a decision tree. Eventually I will like to do this with R and Random Forrest. There has to be a way to output the decsion rules of each leaf node in an easily readable way. I am looking at the randomforrest and rpart packages and I dont see anything yet. Mike [[alternative HTML version deleted]]

-----BEGIN PGP SIGNED MESSAGE----- [ Sorry for sending this twice; I sent it to the vorbis-dev list by mistake. ] The ALBUM, ARTIST, TITLE, and TRACKNUMBER tags aren't sufficient for classical music. MP3 muffed it, but since Vorbis is so flexible, I'd like to propose that ogg123 recognize and display the following optional tags, instead of giving an error message that it doesn't

-----BEGIN PGP SIGNED MESSAGE----- [ Sorry for sending this twice; I sent it to the vorbis-dev list by mistake. ] The ALBUM, ARTIST, TITLE, and TRACKNUMBER tags aren't sufficient for classical music. MP3 muffed it, but since Vorbis is so flexible, I'd like to propose that ogg123 recognize and display the following optional tags, instead of giving an error message that it doesn't

Nope. Random forests are not decision trees -- they are ensembles (forests) of trees. You need to go back and read up on them so you understand how they work. The Hastie/Tibshirani/Friedman "The Elements of Statistical Learning" has a nice explanation, but I'm sure there are lots of good web resources, too. Cheers, Bert Bert Gunter "The trouble with having an open mind is

Ah yes I will have to use the predict function. But the predict function will not get me there really. If I can take the example that I have a model predicting whether or not I will play golf (this is the dependent value), and there are three independent variables Humidity(High, Medium, Low), Pending_Chores(Taxes, None, Laundry, Car Maintenance) and Wind (High, Low). I would like rules like

Greetings R users! I am working with the ENSEMBLE climate data (10 min resolution daily temperatures images for all of Europe 1950-2006). The data comes packaged in a single netCDF file. I would like to read the data in and export a subset (2002-2006) as geotiffs (one image per day). So far, I can successfully read in the data and view the images within an R display window. However, I have yet to