similar to: [SPAM] - Re: How to assess significance of random effect in lme4 - Bayesian Filter detected spam

Dear All, With kind help from several friends on the list, I am getting close. Now here are something interesting I just realized: for random effects, lmer reports standard deviation instead of standard error! Is there a hidden option that tells lmer to report standard error of random effects, like most other multilevel or mixed modeling software, so that we can say something like "randome

Dear All, After playing with lmer for couple of days, I have to say that I am amazed! I've been using quite some multilevel/mixed modeling packages, lme4 is a strong candidate for the overall winner, especially for multilevel generzlized linear models. Now go back to my two-level poisson model with cross-classified model. I've been testing various different model specificatios for the

Hello, I am looking for a way to obtain standard errors for emprirical Bayes estimates of a model fitted with lmer (like the ones plotted on page 14 of the document available at http://www.eric.ed.gov/ERICDocs/data/ericdocs2/content_storage_01/0000000b/80/2b/b3/94.pdf). Harold Doran mentioned (http://tolstoy.newcastle.edu.au/~rking/R/help/05/08/10638.html) that the posterior modes' variances

Can you provide the summary(m2) results? > -----Original Message----- > From: Simon Pickett [mailto:S.Pickett at exeter.ac.uk] > Sent: Wednesday, August 16, 2006 7:14 AM > To: Doran, Harold > Cc: r-help at stat.math.ethz.ch > Subject: [SPAM] - RE: [R] REML with random slopes and random > intercepts giving strange results - Bayesian Filter detected spam > > Hi again,

oops, Forgot to cc to the list. Regards, Mike -----Original Message----- From: dr mike [mailto:dr.mike at ntlworld.com] Sent: 24 February 2005 19:21 To: 'Spencer Graves' Subject: RE: [R] Bayesian stepwise (was: Forward Stepwise regression based onpartial F test) Spencer, Obviously the problem is one of supersaturation. In view of that, are you aware of the following? A Two-Stage

I am glad to help. The pp program is the main tool to use to create the executable. -----Original Message----- From: "Duncan Murdoch" <murdoch at stats.uwo.ca> To: "Greg Snow" <Greg.Snow at intermountainmail.org> Cc: "Gabor Grothendieck" <ggrothendieck at gmail.com>; "Doran, Harold" <HDoran at air.org>; "r-help at

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a

Version 1.1 of the MiscPsycho package had been uploaded to CRAN. The package has been updated to include the following: 1) The irt.ability() function that estimates examinee ability given a set of item parameters. The function is very general and can be used to estimate ability when there are only dichotomous items (1-, 2-, or 3PL), only polytomous items (generalized partial credit model), or a

Hi all, I would like to start to use R's MCMC abilities to compute answers in Bayesian statistics. I don't have any specific problems in mind yet, but I would like to be able to compute/sample posterior probabilities for low-dimensional custom models, as well as handle "standard" Bayesian cases like linear regression and hierarchical models. R clearly has a lot of abilities in

Dear Friends, According to Gelman et al (2003), "...Bayesian P-values are defined as the probability that the replicated data could be more extreme than the observed data, as measured by the test quantity p=pr[T(y_rep,tetha) >= T(y,tetha)|y]..." where p=Bayesian P-value, T=test statistics, y_rep=data from replicated experiment, y=data from original experiment, tetha=the function

Hi all, To follow up on an older thread, it was suggested that the following would produce confidence intervals for the estimated BLUPs from a linear mixed effect model: OrthoFem<-Orthodont[Orthodont$Sex=="Female",] fm1OrthF. <- lmer(distance~age+(age|Subject), data=OrthoFem) fm1.s <- coef(fm1OrthF.)$Subject fm1.s.var <- fm1OrthF. at bVar$Subject fm1.s0.s <-

Hello, for a frequency modelling problem I want to combine expert knowledge with incoming real-life data (which is not available up to now). The frequency has to be modelled with a poisson distribution. The parameter lambda has to be normal distributed (for certain reasons we did not NOT choose gamma althoug it would make everything easier). I've started with the subsequent two functions to

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

I want to compute confidence intervals for the random effect estimates for each subject. From checking on postings, this is what I cobbled together using Orthodont data.frame as an example. There was some discussion of how to properly access lmer slots and bVar, but I'm not sure I understood. Is the approach shown below correct? Rick B. # Orthodont is from nlme (can't have both nlme and

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

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R users, I am pleased to announce the release of a new packaged called `bcrm? (version 0.1), now available on CRAN. The package implements a wide range of Bayesian continuous reassessment method (CRM) designs to be used in Phase I dose-escalation trials. The package is fully documented and highlights include ? A choice of 1-parameter working models or the 2-parameter logistic model.

Dear R-users, I'd appreciate your statistical opinion on the following problem. I'm fitting the four parameter logistic model [f(x) = a + (b - a)/(1 + exp((c - x)*d))] to assay data. We have a lot of samples to fit and my aim is to classify these samples into following groups: 1. no interrelation all results about =~ 0 too low concentration 2. only full

I have a function in R^2, say f <- function(x,y) { ...skipped } I want to plot this function using contour, persp. wireframe, etc. I know that the function has a global minimum at (x0, y0) The naive approach is to evaluate the function on the outer product of two arrays, like this: sx <- c(seq(-3, x0, len = 100), seq(x0, 3, len = 100)[-1]) sy <- c(seq(-3, y0, len = 100), seq(y0, 3,