similar to: Pasting data into scan() - oops!

The file TENSILE.DAT from the Hand et al "Handbook of Small Data Sets" looks like this: 0.023 0.032 0.054 0.069 0.081 0.094 0.105 0.127 0.148 0.169 0.188 0.216 0.255 0.277 0.311 0.361 0.376 0.395 0.432 0.463 0.481 0.519 0.529 0.567 0.642 0.674 0.752 0.823 0.887 0.926 except that my mail client has replaced the tab separators by blanks. If I paste this data into R 2.2.1 what I get is

Here is the file minimal.Snw: \documentclass[a4paper]{article} \title{R tips and tricks} \author{Murray Jorgensen} \usepackage{Sweave} \begin{document} \maketitle \section*{Entering data from a single variable} The following data are transformed tensile strength measurements on polyester fibres. They may be found on the file \texttt{TENSILE.DAT}. We may enter this data into R using the

Bother! This cold has made me accident-prone. I meant to hit Reply-all. Clarification below. -------- Original Message -------- Subject: Re: [R] Parameterization puzzle Date: Fri, 21 Jul 2006 19:10:03 +1200 From: Murray Jorgensen <maj at waikato.ac.nz> To: Prof Brian Ripley <ripley at stats.ox.ac.uk> References: <44C063E5.3020703 at waikato.ac.nz>

I can ask my question using and example from Chapter 1 of Pinheiro & Bates. > # 1.4 An Analysis of Covariance Model > > OrthoFem <- Orthodont[ Orthodont$Sex == "Female", ] > fm1OrthF <- + lme( distance ~ age, data = OrthoFem, random = ~ 1 | Subject ) > summary( fm1OrthF ) Linear mixed-effects model fit by REML Data: OrthoFem AIC BIC

Having just update to R 2.6.2 on my old Windows laptop I notice that the number of packages is growing exponentially and my usual approach of get-em-all may not be viable much longer. Has any thought been given to dividing "contributed" binaries into a recommended set, perhaps a couple of hundred, and the remained. That way one could install the recommended ones routinely and add in

I am exploring the result of clustering a large multivariate data set into a number of groups, represented, say, by a factor G. I wrote a function to see how categorical variables vary between groups: > ddisp <- function(dvar) { + csqt <- chisq.test(G,dvar) + print(csqt$statistic) + print(csqt$observed) + print(round(csqt$expected)) + round(csqt$residuals) + } > > x

The birth weight example from ?stepAIC in package MASS runs well as indeed it should. However when I change stepAIC() calls to step() calls I get warning messages that I don't understand, although the output is similar. Warning messages: 1: In model.response(m, "numeric") : using type="numeric" with a factor response will be ignored (and three more the same.) Checked

Consider the following: > A <- 1:10 > A [1] 1 2 3 4 5 6 7 8 9 10 > dim(A) NULL > dim(A) <- c(2,5) > A [,1] [,2] [,3] [,4] [,5] [1,] 1 3 5 7 9 [2,] 2 4 6 8 10 > dim(A) [1] 2 5 > dim(A) <- 10 > A [1] 1 2 3 4 5 6 7 8 9 10 > dim(A) [1] 10 Would it not make sense to have dim(A) = length(A) for all vectors?

I am introducing the scan() function to my class. Consider the following file (Scanexamp.Rnw ) \documentclass[12pt]{article} \begin{document} <<>>= height = scan() 64 62 66 65 62 69 72 72 70 part = scan(what = character(0)) "Soprano" "Soprano" "Soprano" "Alto" "Alto" "Tenor" "Tenor" "Bass"

I found myself wanting to average a vector [vec] within each level of a factor [Fac], returning a vector of the same length as vec. After a while I realised that lm1 <- lm(vec ~ Fac) fitted(lm1) did what I want. But there must be another way to do this, and it would be good to be able to apply other functions than mean() in this way. Cheers, Murray -- Dr Murray Jorgensen

It is not uncommon to want to coarsen a factor by grouping levels together. I have found one way to do this in R: > sites [1] F A A D A A B F C F A D E E D C F A E D F C E D E F F D B C Levels: A B C D E F > regions <- list(I = c("A","B","C"), II = "D", III = c("E","F")) > library(Epi) > region <-

I assigned a class the first problem in Pinheiro & Bates, which uses the data set PBIB from the SASmixed package. I have recently downloaded 2.0.1 and its associated packages. On trying library(SASmixed) data(PBIB) library(nlme) plot(PBIB) I get a warning message Warning message: replacing previous import: coef in: namespaceImportFrom(self, asNamespace(ns)) after library(nlme) and a

I am wondering if stepAIC in the MASS library may be used for model selection in an overdispersed Poisson situation. What I thought of doing was to get an estimate of the overdispersion parameter phi from fitting a model with all or most of the available predictors (we have a large number of observations so this should not be problematical) and then use stepAIC with scale = phi. Should this

We have a choice when calculating the Huber location estimate: > set.seed(221205) > y <- 7 + 3*rt(30,1) > library(MASS) > huber(y)$mu [1] 5.9117 > coefficients(rlm(y~1)) (Intercept) 5.9204 I was surprised to get two different results. The function huber() works directly with the definition whereas rlm() uses iteratively reweighted least squares. My surprise is

Consider the following output [R2.2.0; Windows XP] > set.seed(160706) > X <- matrix(rnorm(40),nrow=10,ncol=4) > Xpc <- princomp(X,cor=FALSE) > summary(Xpc,loadings=TRUE, cutoff=0) Importance of components: Comp.1 Comp.2 Comp.3 Comp.4 Standard deviation 1.2268300 0.9690865 0.7918504 0.55295970 Proportion of Variance 0.4456907 0.2780929

I am in a discriminant analysis situation with a frame containing several variables and a grouping factor, if you like: set.seed(200906) exampledf <- as.data.frame(matrix(rnorm(50,5,2),nrow=10,ncol=5)) exampledf$Group <- factor(rep(c(1,2,3),c(3,3,4))) exampledf I'm sure there must be a simple way to get the within group pooled covariance matrix but I haven't found it yet. I

This is somewhat off-topic but I think that an answer may help other users of R-help. Thunderbird trys to help in the display of messages by "greying out" quoted text. However when reading r-help in digest form it gets thoroughly confused and usually ends up greying out the fresh text in a message. [I'm using Windows XP]. Does anyone know how to turn off this Thunderbird

Greetings all I promised a summary of the responses that I got to my question: "Next year I will be teaching a third year course in applied statistics about 1/3 of which is multivariate statistics. I would be interested in hearing experiences from those who have taught multivariate statistics using R. Especially I am interested in the textbook that you used or recommended." There

I would like a function to strip quotes off character strings. I should work like this: > A <- matrix(1:6, nrow = 2, ncol=3) > AF <- as.data.frame(A) > names(AF) <- c("First","Second","Third") > AF First Second Third 1 1 3 5 2 2 4 6 > names(AF)[2] [1] "Second" > attach(AF) >

[Using R 2.2.0 on Windows XP; OK, OK, I will update soon!] I have noticed some undesirable behaviour when applying ifelse to a data frame. Here is my code: A <- scan() 1.000000 0.000000 0.000000 0 0.00000 0.027702 0.972045 0.000253 0 0.00000 A <- matrix(A,nrow=2,ncol=5,byrow=T) A == 0 ifelse(A==0,0,-A*log(A)) A <- as.data.frame(A) ifelse(A==0,0,-A*log(A)) and this is the output: