similar to: Multivariate linear regression

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

Hi, I am trying to run randomForests on a datasets of size 500000X650 and R pops up memory allocation error. Are there any better ways to deal with large datasets in R, for example, Splus had something like bigData library. Thank you, Nagu

Hi, I get the following error when I try to predict the probabilities of a test sample: Error in predict.randomForest(fit.EBA.OM.rf.50, x.OM, type = "prob") : New factor levels not present in the training data I have about 630 predictor variables in the dataset x.OM (25 factor variables and the remaining are continuous variables). Any ideas on how to trace it? Thank you, Nagu

To whom should bug reports of the R port of acepack be directed? On a SPARC/Solaris 2.6 or 2.7 (SunOS 5.6 or 5.7) system running R-1.0.0 the avas example fails > library(acepack) > example(avas) avas> TWOPI <- 8 * atan(1) avas> x <- runif(200, 0, TWOPI) avas> y <- exp(sin(x) + rnorm(200)/2) avas> a <- avas(x, y) Process R bus error (core dumped) at Mon

Hi, Does any one know how we can decide on the correct transformation in (avas and ace) after having drawn the graphs y,g(y) x ,s(x) and g(y) ,s(x) . Is it possible by only looking at patterns the graphs follow for example when y ,g(y) shows a logaritmic pattern can we say that log transform on y is suitable? Thanks for your help. Regards [[alternative HTML version deleted]]

Hi, I am trying to model a dataset with the response variable Y, which has 6 levels { Great, Greater, Greatest, Weak, Weaker, Weakest}, and predictor variables X, with continuous and factor variables using random forests in R. The variable Y acts like an ordinal variable, but I recoded it as factor variable. I ran a simulation and got OOB estimate of error rate 60%. I validated against some

I need to get more digits in predicting a test sample with a random forests object. Format or options(digits=) do nothing. Any ideas? Thank you, Nagu

Hi, I wanted to make a transformation with "avas" and "ace" but saw a message " couldn't find function "avas" " what are the possibble reasons of this case? It may be a basic question but unfortunately I am very new in R. Thanks for your helps Regards [[alternative HTML version deleted]]

> On Jun 14, 2017, at 5:52 PM, Jeff Newmiller <jdnewmil at dcn.davis.ca.us> wrote: > > Sigh. I never load packages in .Rprofile to avoid the irreproducibility trap. Might seem drastic to some, but I don't feel much pain because I almost always edit my code in a file rather than on the fly at the console, and re-run it frequently from a fresh R process to check my progress.

In platform i386-pc-linux-gnu arch i386 os linux-gnu system i386, linux-gnu status major 1 minor 7.1 year 2003 month 06 day 16 language R using debian testing (latest Knoppix) I get an error when installing acepack: gcc

Got at suggestion from CarrierAccess to use the Adit 600 as an VoIP router using MGCP IP protocol, instead of controlling it through an E1. Have anyone tried this configuration? How does MGCP works? I've tried to search for it on Google, but I only find the protocol specification for it. Is Asterisk fully capable of this? I can't find any documentatin covering the use of MGCP in Asterisk.

I've put all ``current'' add-on packages into CRAN's source/contrib tree and created an INDEX file (attached below). As you can see, currently we have acepack bootstrap ctest date e1071 fracdiff gee jpn snns splines survival4 (Yes, e1071 and jpn are new ... more on the latter in a later mail.) In the near future, I am hoping for the following: oz (Bill

I am using ACE/AVAS to fit a non-parametric regression model to a multivariate data set. Is there a way to add confidence intervals to the fit and predictions by using bootstrap? Thanks for any suggestions. [[alternative HTML version deleted]]

Dear all, I have a problem installing a package required by Hmisc on MacOS 10.3.5 with R 2.00. g77 -fno-common -g -O2 -c avas.f -o avas.o g77 -fno-common -g -O2 -c rlsmo.f -o rlsmo.o gcc -bundle -flat_namespace -undefined suppress -L/usr/local/lib -o acepack.so ace.o avas.o rlsmo.o -L/usr/local/lib -L/usr/local/lib/gcc/powerpc-apple-darwin6.8/3.4.2

Hello, I would like to perform a monotone transformation of y (my response variable) to achieve additivity. My model is as follows y~X+items I do not want to transform X or items (both of these variables are factors) > X [1] 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 3 3 3 3 3 3 3 3 3 4 4 4 4 4 4 4 4 4 5 5 5 5 5 5 5 5 5 6 6 6 6 6 6 6 6 6 7 7 7 7 7 7 [61] 7 7 7 8 8 8 8 8 8 8 8 8 Levels: 1 2 3 4 5

Hi, I recently provoked some discussion over on the BioConductor developer list about testing and R CMD check. In brief, the salient points that arose from the discussion are: [1] Because of the need for a nightly build of BioConductor, the tests (in the ./tests directory) of a package that run routinely as part of "R CMD check" must complete in five minutes. [2] Nontrivial

Sigh. I never load packages in .Rprofile to avoid the irreproducibility trap. Might seem drastic to some, but I don't feel much pain because I almost always edit my code in a file rather than on the fly at the console, and re-run it frequently from a fresh R process to check my progress. -- Sent from my phone. Please excuse my brevity. On June 14, 2017 3:27:15 PM PDT, David Winsemius

Hello R helpers! I'm using the avas function form package acepack (called from areg.boot package Hmisc) to estimate automatically transformations of predictors (in this case monotonous) and response. Well, it seems to work quite well, but I have 3 basic questions: - which set of basis functions is used in this procedure? - how do I back transform my estimate (y hat ) to the originasl scale?

Dear colleagues: I've been storing up this question for a long time and apologize for the length and verbosity of it. I am having trouble in consulting with graduate students on their research projects. They are using surveys to investigate the sources of voter behavior or attitudes. They have predictors that are factors, some ordered, but I am never confident in telling them what

>I am trying to use the Hmisc function transace to transform predictors > > test<-cbind(flowstress,pressres,alloy) > xtrans<-transace(x,binary=pressres',monotonic='flowstress', categorical='alloy') > > >and I am getting the following message?? >Error in ace(x[, -i], x[, i], monotone = im, categorical = ic) : > unused argument(s) (monotone ...)