similar to: linear regression of verydispersed data

Hi, I am working on a multivariate linear regression of the form y = Ax. I am seeing a great dispersion of y w.r.t x. For example, the correlations between y and x are very small, even after using some typical transformations like log, power. I tried with simple linear regression, robust regression and ace and avas package in R (or splus). I didn't see an improvement in the fit and

I have just put on ftp://sirio.stat.unipd.it/pub/R a new version of R for Win95/NT. The main difference concerns dynamic linking (see below). In addition, the binary distribution contains many of the available packages (all the ones that I have installed on my Unix machine). In the afternoon, I will upload the files on CRAN. g.m. This is the README file: rw0613b.zip is a new binary

I have just put on ftp://sirio.stat.unipd.it/pub/R a new version of R for Win95/NT. The main difference concerns dynamic linking (see below). In addition, the binary distribution contains many of the available packages (all the ones that I have installed on my Unix machine). In the afternoon, I will upload the files on CRAN. g.m. This is the README file: rw0613b.zip is a new binary

(Sorry, I had the previous emails on a different machine.) Thanks to Prof Ripley for diagnosing the problem with inconsistent capitals under Win98. It's 'interesting': * I had ...\acepack\Src\Ace.f Avas.f and Rlsmo.f (tar'ed in a Unix machine and Ws_FTP'ed to the PC) * when the directory name was changed to ...\acepack\src it still didn't work *

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

Readers, Using version 251 I tried the following command: lm(y~a+b,data=datafile) Resulting in, inter alia: ... coefficients (intercept) a 1.2 3.4 Packages installed: acepack ace() and avas() for selecting regression transformations adlift An adaptive lifting scheme algorithm akima Interpolation of irregularly spaced

Dear R developers and users: I have the following data, x is the response vaiable, nsample(individual) nested within trt, and subsample nested within nsample, I want to fit trt as fixed effect, and block, nsample(trt) as random effects using lme, is the following coding correct? dat$vgrp <- getGroups(dat, form = ~ 1|trt/nsample, level = 2) ge.lme1 <- lme(fixed=x~trt, data=dat,

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]]

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

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

Attached is an updated version of the FAQ, to be released right after 0.50 is out. Please have a look, and let me know where improvements are needed. Thanks, -k ******************************************* R FAQ Kurt Hornik v0.1-0, 1997/04/16 This document contains answers to some of the most frequently asked questions about R. Feedback is welcome.

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]]

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]]

Hi everybody, let us assume i have the following matrixX and vectorY matrixX <- runif(100) dim(matrixX) <- c(10,10) vectorY <- as.matrix(as.character(seq(1,10))) if I define: subsample<-c("2") i can extract the rows from matriX based on the elements in vectorY which are listed in subsample matrixX[vectorY==subsample] if I define subsample with more than 1 element, such

I am trying to obtain the Kruskal (1964) secondary least-squares monotonic transformation of a rank variable given 4 categorical variables in order to obtain optimal transformation for regression. The academic problem assigned is to compare R, SPSS (Conjoint Analysis), and SAS' proc transreg in speed and accuracy. Currently, SAS and SPSS are giving similar results, but R's are quite

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'm trying to write a code (see below) to randomly resample measurements of one variable (say here the variable "counts" in the data frame "dat") with different resampled subsample sizes. The code works fine for a single resampled subsample size (in the code below = 10). I then tried to generalize this by writing a function with a loop, where in each loop the function

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

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