similar to: Maximisation of likelihood of a discrete parameter

I've got a colleague who's using a GLMM to analyse her data, and I've told her that she needs to include an offset. However, glmmPQL doesn't seem to allow one to be included. Is there anyway of doing this? Bob -- Bob O'Hara Rolf Nevanlinna Institute P.O. Box 4 (Yliopistonkatu 5) FIN-00014 University of Helsinki Finland Telephone: +358-9-191 23743 Mobile: +358 50 599

Moi! I was helping to teach a course on mixed models this week, and we came across a problem with coding more than one random effect in lme when they aren't nested. As an example, suppose we have an experiment where we sample moths from several populations, and place the moths on different trees, and measure a trait (in this case survival of offspring, but that's less important). We

I want to draw a figure with several panels of unequal size, so i thought I would try using screen(). However, I can't figure out how to define the sizes as a matrix. I've tried this: split.screen(matrix(c(0,0.5,0,0.5, 0.5,1,0.5,1), byrow=F, ncol=4)) and a couple of variants on it, but get the same error: Error in par(.split.screens[[cur.screen]]) : invalid value specified

Moi! I'm trying to add a rather long label to a y-axis, and it's so long that it won't fit into one line. However, things get strange when I try and split it over 2 lines. The problem seems to be the plus/minus symbol, which means I have to use expression(). I'm using R1.6.1 on Windoze 2000. As an example: thing1 <- expression(paste("log odds of survival probability (

I can only assume I'm betraying my ignorance here, but this is not what I would expect. I'm getting the following from a stepwise selection (with both step and stepAIC): > step(lm(sqrt(Grids)~ SE + Edge + NH), scope=~ (Edge + SE + NH)^2) Start: AIC= 593.56 sqrt(Grids) ~ SE + Edge + NH Df Sum of Sq RSS AIC <none> 2147.0 593.6 + Edge:NH 1

Hello from a presently sunny Helsinki! I've bee trying to repeat an analysis I did about 18 months ago (the reviewers of the paper want something adding to it). I'm using R1.5.0, but I couldn't see anything in the list of changes between this and 1.5.1 to suggest it would act any differently. The data is observational, on the changes in the population status of carabid beetles, and

I assume I'm missing something obvious here... The short form of my main question is: how do I extract variance components from an lme object? The longer form (plus optional supplementary question!): I'm looking at some quantitative genetics, and want to estimate two variance components so that I can then calculate a statistic called Qst from them. So I have this: reg1 <- lme(y ~

Moi! A student here has been getting a bit irritated with some side effects of scale() (OS is Windows XP, the behaviour occurs in R 2.0.0, but not 1.7.1). The problem is that she scales a variable in a data frame, then does a regression, and tries to get some predictions for some new data. However, at this point she gets an error (see the example below). This seems to be because the

The background: I'm trying to fit a Poisson-lognormal distrbutuion to some data. This is a way of modelling species abundances: N ~ Pois(lam) log(lam) ~ N(mu, sigma2) The number of individuals are Poisson distributed with an abundance drawn from a log-normal distrbution. To fit this to data, I need to integrate out lam. In principle, I can do it this way: PLN1 <- function(lam, Count,

Dear friends, Over the past weeks, I have been asking a lot of questions about how to use R in Bayesian analysis. I am brand new to R, but I am very pleased with it. I started with winbugs but I found winbugs to be a limited software, not bad but has several limitations. By contrast, R allows the analyst to tackle any problem with a huge set of tools for any kind of analysis. I love R. In

I'm sure I'm missing something obvious here, but I can't find the solution (including in the FAQ etc.). I have a vector of names of variables like this: NRes.x.y. where x and y are numbers. I want to extract these numbers as numbers to use elsewhere. I can extract the numbers as a list of characters using strsplit(), and convert that to a data frame, e.g.:

I have a simple (!) problem. What I want to do is to create a legend with Greek letters, and substituting numbers into the legend. Like this: mu=1:3 Mean=rep(mu, each=20) plot(runif(60), rnorm(60,Mean,0.1), pch=Mean) # create a plot legend(0.6,1.7, paste("mu =", 1:3), pch=mu) but with a Greek letter mu. I think the solution has something to do with substitute() possibly along with

This is slightly off-topic, but there was a discussion about MCMC a couple of weeks ago. OpenBUGS can operate from R, at least in Windows (there are some problems in Linux at the moment). The interface with R is one are that there are plans to work on: it's a bit basic at the moment. Bob -- Bob O'Hara Dept. of Mathematics and Statistics P.O. Box 68 (Gustaf H??llstr??min katu 2b)

Dear all, I didn't find any graphical solution in the package "mva" to plot the canonical scores from a CCA (canonical correlation analysis). Does anybody knows how to plot or has anybody already programmed : - the map of the canonical scores, - the graph of the canonical weights, - the correlation circle i.e. the canonical loadings ? Thank you for help ...

Moi! I've spent a fun few hours trying to get a plot to look exactly the way I want it, and with a lot of help from Venables & Ripley (the book!) and the help pages, I've almost got it perfect. Only one problem now remains.... I'm using R1.2.2 on Windoze NT. I want to draw the graph as a Windows metafile (so at the moment I'm using the windows device, i.e. on the screen and

Anybody know where I can get the Pinheiro/Bates book? I can't find a bookstore w/ stock and the publisher says they don't know when they'll have it again. Thanks. -Frank

Hello, I've recently run into an unknown bit in Tesla shaders, and was hoping you could shed some light on it. I believe they're related to clamping of some sort. Here are 2 examples (from diff shaders): a0000401 cc054780 cvt rpi f32 $r0 f32 $r2 [unknown: 00000000 00010000] a000060d 8c014780 cvt rni s32 $r3 f32 $r3 [unknown: 00000000 00010000] [This is intel-style syntax, cvt =

Hi! I was trying to implement a probit model on a dichotomous outcome variable and found that the predictions were outside the (0,1) interval that one should get. I later tried it with some simulated data with a similar result. Here is a toy program I wrote and I cant figure why I should be getting such odd predictions. x1<-rnorm(1000) x2<-rnorm(1000) x3<-rnorm(1000)

Hi Ilia, sorry for the slow response. This isn't my area of expertise, but as I understand it: * You've correctly decoded both instructions: * The first is a float32-to-float32 conversion, applying ceil() * The second is a float32-to-signed-int32 conversion, rounding to the nearest even integer * For the {float,int}-to-{float,int} operations, bit 48 indicates whether the

Hi guys again, it seems I haven't been doing the maximum likelihood estimation correctly. I quote below, can someone explain to me please what does it mean that the 2nd and 3rd derivatives of the function equals zero and how to compute that in R. "We have our initial estimated, subjective parameters for the gamma mixture and we have our likelihood that is the mixture of negative