similar to: Cook-Weisberg confidence curves

Hi, Suppose I have a matrix with, say 12 columns. I would like to extract out column 2 ~ 8 and 11 ~ 12 then combine them together. I tried with success to extract out the columns by doing: foo <- test[,2:8] goo <- test[,11:12] However then I am having trouble combining foo and goo. Helps are appreciated! Cheers, Kevin ----------------------------------- Ko-Kang Kevin Wang

I am trying to explore nlmer by running some nlme examples from Pinheiro & Bates (2000). I do not seem to find information how to specify fixed effects covariates to nlmer models. Specifically, I tried to run the "Carbon Dioxide Uptake" example from p. 368 onwards in the PB200 book. The model without fixed effects covariates runs well but how to tell nlmer to include Type and

I am new to generalized linear models and studying McCullagh & Nelder (1989). Especially, I have a problem resembling the \"cheese taste\" example (5.3.1. p. 109) of the book. I tried to analyse the cheese example with R but failed to do so because R allowed me to use logit link function only with binary family that supposes 0 <= y <= 1. Do I need to scale the y\'s or

Hi, I wonder if someone could help. I needed to transfer (copy) a workspace file that had been generated in linux (R 2.11) to windows running the same version of R 2.11 (but of course windows binary). Usually, there is no problem in doing this and all objects work as expected. I am often doing this to be able to produce wmf or emf graphic files that I need. This time I had some spectra that I

Hi, I would appreciate of someone could explain how the residual standard error is computed for rlm models (MASS package). Usually, one would expect to get the residual standard error by > sqrt(sum((y-fitted(fm))^2)/(n-2)) where y is the response, fm a linear model with an intercept and slope for x and n the number of observations. This does not seem to work for rlm models and I am wondering

Hi, I wonder if predict.gam is supposed to work with family=negbin() definition? It seems to me that the values returned by type="response" are far off the observed values. Here is an example output from the negbin examples: > set.seed(3) > n<-400 > dat<-gamSim(1,n=n) > g<-exp(dat$f/5) > dat$y<-rnbinom(g,size=3,mu=g) >

Le jeudi 09 f?vrier 2012 ? 15:31 +0200, Kari Ruohonen a ?crit : > Hi, > I am trying to replicate a training of AttributeSelectedClassifier with > CFsSubsetEval, BestFirst and NaiveBayes that I have initially done with > Weka. Now, I am trying to use RWeka in R. > > I have a problem of passing arguments to the CfsSubsetEval, BestFirst > and NaiveBayes. I have first created an

Hi, I have faced a problem with the merge() function when trying to merge two data frames that have a common index but the second one does not have cases for all indexes in the first one. With usual variables R fills in the missing cases with NA if all=T is requested. But if the variable is a matrix R seems to insert NA only to the first column of the matrix and fill in the rest of the columns by

Hi, I wonder if this is something on my machine locally or R in general. When I do the following: > plot(c(0,1),c(0,1),main=expression(paste(symbol("D"),"D",sep=""))) I get a plot with a title having uppercase delta followed by "D". But in the following > pdf(file="deltaTest.pdf") >

Hi - Are there any known bugs or other issues that may cause R to crash when trying to use xyplot()? For example, > x<-1:100 > y<-rnorm(100) > library(lattice) Loading required package: grid > xyplot(y~x) causes this: Process R segmentation fault at Wed Apr 3 16:56:42 2002 I am running linux debian unstable on i386. R says it is R 1.5.0 in the header text when starting but

This is maybe not directly an R problem but I have used R to try to solve it so I think somebody may be able to help. I have a mixture model with three components and a quadratic Scheffe polynomial p1x1+p2x2+p3x3+p12x1x2+p13x1x3+p23x2x3 fitted to the response. Now I'd like to compute the mixture corresponding the maximum response. Model for Y1 has the parameters p1=124.02 p2=60.973 p3=41.479

I have a fresh installation of R from debian unstable packages. The html index found in /usr/lib/R/doc/html/index.html works in Mozilla and under the link of 'packages' on this page I have a list and corresponding links. However, it appears that not all packages I have installed from available deb files via apt-get, will get their link updated to this package index. Specifically, from

Hi, I get the following error message when trying to use the display function on the ARM package: > display(model) Error in .Internal(round(x, digits)) : no internal function "round" Looks like some kind of mismatch between the ARM package and some others? Can I somehow get around it? I have learned to like the display function to print model summaries. Here is my sessionInfo():

hello, all. while i have a mcmc running, i am looking at the frequestist method of my model. i have never done HLM so i am looking for ways to plot them that might yeild something useful like dr. fox's effects plot package. this is my model, where dem is democracy ranked continuous 1:10, trsut is a 3 level categorical variable, cpi is 1:10, etc... > hier.jags2.mod <- lmer(dem ~

Hi, I have a question about the computation of confidence intervals in the zyp package, in particular using the functions zyp.sen and confint.zyp, or zyp.yuepilon. (1) I'm a bit confused about the confidence intervals given by zyp.sen and confint.zyp. When I request a certain confidence interval in the function, the R output seems to deliver another confidence interval, e.g. when I set

I've built a poisson regression model for multiple subjects by using the GLMER function. I've also developed some curves for defining its limits but I did not succeed in developing confidence interval for the model's curve (confint or predict does not work - only for glm). Does anyone know how can I produce confidence interva for a glmer model? I'll appriciate any help... Liat --

where is the cook-distance in R? i can't find it!please help me!

Dear R users this may be a simple question - but i would appreciate any thoughts does anyone know how you would get one lower and one upper confidence interval for a set of data that consists of proportions. i.e. taking a usual confidence interval for normal data would result in the lower confidence interval being negative - which is not possible given the data (which is constrained between

Hi, The question is as follows: has anyone coded panel data unit root tests with R? Even the "first generation" tests (see e.g. Levin & Lin 1993; Pesaran, & Smith & Im 1996; Maddala & Wu 1999) would be sufficient for my needs. To my understanding, these are rather easy to code, but as I have taken just my first steps in coding with R, existing code would save me

Hi, I'm trying to get confidence intervals to slopes from a linear model and I can't figure out how to get at them. As a cut 'n' paste example: ################# # dummy dataset - regression data for 3 treatments, each treatment with different (normal) variance x <- rep(1:10, length=30) y <- 10 - (rep(c(0.2,0.5,0.8), each=10)*x)+c(rnorm(10, sd=0.1), rnorm(10,