similar to: energy 1.1-0 with dcov

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

R Users, We would like to announce that Version 1.0.1 of the energy package is now available on CRAN. The energy package introduces a new class of statistical tests based on the concept of Newton's potential energy. Included in the package are * mvnorm.etest (test) and mvnorm.e (statistic) A rotation invariant multivariate goodness-of-fit test, implemented for testing

I pushed version 0.1 of my Google Summer of Code project -- dcov -- to RubyForge yesterday, and I hope you guys can take advantage of it (and let me know if it breaks! :)). dcov is a tool to help you analyze the documentation of your Ruby code. At the present, it only checks for the presence of documentation and generates a report based on that, but in the future it will check for

Szekely Denes wrote: > > Three days ago I had a mad ideea to install Win 98 on my PC. Until this > date I cannot map or access from WINDOWS my user directory and any other > shared resurces on the SCO, because my password is rejected. > And some others answered... At first, I wanted to post a response to Szekely only, not CC'd to the list, as this is _not_ a new topic for the

Hi, how to estimate a the following model in R: y(t)=beta0+beta1*x1(t)+beta2*x2(t)+...+beta5*x5(t)+beta6*y(t-1)+beta7*y(t-2)+beta8*y(t-3) 1) using "lm" : dates <- as.Date(data.df[,1]) selection<-which(dates>=as.Date("1986-1-1") & dates<=as.Date("2007-12-31")) dep <- ts(data.df[selection,c("dep")]) indep.ret1

I commend you to (a) the recent article by Doug Bates on "Fitting nonlinear mixed models in R" pp. 27-30 in the latest issue of "R News" available from "www.r-project.org" -> Newsletter and (b) Doug's book with Pinheiro (2000) Mixed-Effects Models in S and S-PLUS (Springer). I suggest you try the same analysis using in "lmer", library(lme4), and

Hi useRs, I'm experiencing something quite weird with glm() and weights, and maybe someone can explain what I'm doing wrong. I have a dataset where each row represents a single case, and I run glm(...,family="binomial") and get my coefficients. However, some of my cases have the exact same values for predictor variables, so I should be able to aggregate up my data frame and

Hi, I'm working with a numeric matrix with 55columns and 581012 rows and I'm having problems in allocation of memory using some of the functions in R: for example lda, rda (library MASS), princomp(package mva) and mvnorm.etest (energy package). I've read tips to use less memory in help(read.table) and managed to use some of this functions, but haven't been able to work with

Hello, Is there a better way to use paste such as: a = paste(colnames(list.indep)[1],colnames(list.indep)[2],colnames(list.indep)[3],colnames(list.indep)[4],colnames(list.indep)[5],sep="+") > a [1] "aa+dummy1+dummy2+bb+cc" I tried a = paste(colnames(list.indep)[1:5],sep="+") > a [1] "aa" "dummy1" "dummy2"

Hi again. I'm putting together a little project with R, python, and a website. So I have an HTML file, a py file, an R file. Here is the HTML file: <form action="/cgi-bin/radio4.py" method="post" target="_blank"> <input type="radio" name="subject" value="Integrate" /> Integrate <input type="radio"

Hello R community, I have a question about the logistic regression function. Specifically, when the predictor variable has not just 0's and 1's, but also fractional values (between zero and one). I get a warning when I use the "glm(formula = ... , family = binomial(link = "logit"))" which says: "In eval(expr, envir, enclos) : non-integer #successes in a binomial

Hello all, I use arima to fit the model with fit <- arima(y, order = c(1,0,1), xreg = list.indep, include.mean = TRUE) and would like to use predict() to forecast: chn.forecast <- rep(0,times=num.record) chn.forecast[1] <- y[1] for (j in 2:num.record){ indep <- c(aa=chn.forecast[j-1], list.indep[j,2:num.indep]) # this is the newxreg in the

Hello R users, I am new to R and am wondering if anyone can help me out with the following issue: I wrote a function to build ts models using different inputs, but when R displays the call for a model, I cannot tell which variables it is using because it shows the arguments instead of the real variables passed to the function. (e.g Call: lm(formula = dyn(dep ~ lag(dep, -1) + indep)) --->

Dear R community, I am a newbie to R and I am using lme() to analyzed a two way repeated measures ANCOVA on some data I have gathered. In producing some graphs based on the fixed effects I noticed that I get vary different results depending on how I order my levels in my factor statement (see code below). Now, I have read that different models treat data from the factor class

Hi, I have adviced my students to install R and an editor on a USB drive for working in the computer class. With R everything works fine following these instructions: http://personal.bgsu.edu/~mrizzo/Rmisc/usbR.htm. But several editors (e.g., Tinn-R and WinEdt) require administrator rights. I have found out that Emacs (Vincent Goulet's Emacs for Windows Modified

Hello all, I tried several experiments with the mshapiro.test package in R and compared it with the energy package to test for multivariate normality and find that the mshapiro.test is not consistent which is a bit concerning and has suspicious behavior. On the other hand the energy test seems to be a more appropriate test for testing multivariate normality in any dimension. I looked for the

I am using R on a Windows XP professional platform. The following code is part of a bigger one CODE press=function(y,x){ library(qpcR) models.press=numeric(0) cat("\n") dep=y print(dep) indep=log(x) print(indep) yfit=dep-PRESS(lm(dep~indep))[[2]] cat("\n yfit\n") print(yfit) yfit.orig=yfit presid=y-yfit.orig press=sum(presid^2)

> Date: Fri, 16 Oct 1998 18:07:45 -0700 > From: RICK_@t-online.de =?ISO-8859-1?Q?(J=DCRGEN?= ANZER) > To: dszekely@csoft.ro > Subject: Re: WIN 98 password problem > Message-ID: <3627EDE1.38A@t-online.de> > > Szekely Denes wrote: > > > > Three days ago I had a mad ideea to install Win 98 on my PC. Until this > > date I cannot map or access from WINDOWS

I'm trying to develop some graphic methods for glm objects, but they only apply for models where all predictors are discrete factors. How can I test for this in a function, given the glm model object? That is, I want something that will serve as an equivalent of is.discrete.glm() in the following context: myplot.glm <- function(model, ...) { if (!inherits(model,"glm"))

All, I am having trouble with a "read.table()" function that is inside of another function. But if I call the function by itself, it works fine. Moreover, if I run the script on a Mac OS X (with the default Mac OS X version of R installed, rev 2.8), it works fine. But it does not work if I run it on windows vista (also default Windows version of R, rev. 2.8). Again, both