similar to: extrating BLUP values from linear mixed models

Dear R-users, Half a year ago we put out the R package "glmmADMB" for fitting overdispersed count data. http://otter-rsch.com/admbre/examples/glmmadmb/glmmADMB.html Several people who used this package have requested additional features. We now have a new version ready. The major new feature is that glmmADMB allows Bernoulli responses with logistic and probit links. In addition there

Dear all I have a model that looks like this: m1 <- lmer(Difference ~ 1+ (1|Examiner) + (1|Item), data=englisho.data) I know it is not possible to estimate random effects but one can obtain BLUPs of the conditional modes with re1 <- ranef(m1, postVar=T) And then dotplot(re1) for the examiner and item levels gives me a nice prediction interval. But I would like to have the prediction

dfhfsdhf at ghghgr.com I ran a simple lme model: modelrandom=lmer(y~ (1|Test) + (1|strain), data=tempsub) Extracted the BLUPs: blups=ranef(modelrandom)[1] Even wrote myself a nice .csv file....: write.csv(ranef(modelrandom)[1],paste(x,"BLUPs.CSV")) This all works great. I end up with a .csv file with the names of my strains in the first column and the BLUP in the second

Hello! If I run this example: library(nlme) fm1 <- lme(distance ~ age+Sex, Orthodont, random = ~ age + Sex| Subject) If I run: summary(fm1) then I can see the fixed effects for age and sex (17.7 for intercept, 0.66 for age, and -1.66 for SexFemale) If I run: ranef(fm1) Then it looks like it's producing the random effects for each subgroup (in this example - each subject). For example,

I would appreciate confirmation that the function vcov(model.nlme) gives the var-cov matrix of the fixed effects in an nlme model. Presumably the random-effects var-cov matrix is given by cov(ranef (model.nlme)? Rob Forsyth

I''m a physicist working on fusion energy and dabble in statistics only occasionally, so please excuse gaps in my statistical knowledge. I''d appreciate any help that a real statistics expert could provide. Most people in my field do only very simple statistics, and I am trying to extend some work on multivariate linear regression to account for significant between-group

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

hello guys, I need to do a BLUP in the simplest model y = Xm + Zg + e however I have missing data in the analysis which I can?t consider as 0(zero). So I need to generate the matrix X'Z, Z'X and Z'Z step by step; I can?t use crossprod(x) #neither X'X <- t(x)%*%x because I should skip the elements with missing data in the matrix I?ll try to be more clear, supposing a matrix x

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 <-

Queridos R-users: Tengo una duda que hace mucho tiempo que estoy intentando resolver, os explico a modo de ejemplo: Tengo estos efectos: Año(5 niveles),Localidad (10 niveles) y genotipo (3 niveles), año y localidad son aleatorios y genotipo es fijo (los he escogido yo). Me gustaría hacer obtener una tabla parecida a la Tabla Anova donde aparezca cada factor y sus interacciones y

Paul: I may have found the issue (which is similar to your conclusion). I checked using egsingle in the mlmRev package as these individuals are strictly nested in this case: library(mlmRev) library(nlme) fm1 <- lme(math ~ year, random=~1|schoolid/childid, egsingle) fm2 <- lmer(math ~ year +(1|schoolid:childid) + (1|schoolid), egsingle) Checking the summary of both models, the output is

Dear list, In a mixed model I selected I find a non positive definite random effects variance-covariance matrix G, where some parameters are estimated close to zero, and related confidence intervals are incredibly large. Since simplification of the random portion is not an option, for both interest in the parameters and significant increase in the model fit, I would like to collect

Using ranef() (package nlme, version 3.1-75) with an 'lme' object I can obtain random effects for intercept and slope of a certain level (say: 1) - this corresponds to (say level 1) "residuals" in MLWin. Maybe I'm mistaken here, but the results are identical. However, if I try to get the standardized random effects adding the paramter "standard=T" to the

Dear all, I'm attempting to use a piecewise regression to model the trajectory of reproductive traits with age in a longitudinal data set using a mixed model framework. The aim is to find three slopes and two points- the slope from low performance in early age to a point of high performance in middle age, the slope (may be 0) of the plateau from the start of high performance to the

I want to compare the fit of a quadratic model to continuous data, with that of a cubic spline fit. Is there a way of computing AIC from for e.g. a GAM with a smoothing spine, and comparing this to AIC from a quadratic model? Cheers ****************************************** Tom Reed PhD Student Institute of Evolutionary Biology 102 Ashworth Laboratories Kings Buildings University of

Hi, I have generated a profile likelihood for a parameter (x) and am trying to get 95% confidence limits by calculating the two points where the log likelihood (LogL) is 2 units less than the maximum LogL. I would like to do this by linear interpolation and so I have been trying to use the function approxfun which allows me to get a function to calculate LogL for any value of x within

On Feb 2, 2014, at 6:18 PM, Andrew Trick <atrick at apple.com> wrote: >> The result of such a system would produce weights for every block in the above CFG as '1.0', or equivalent to the entry block weight. This to me is a really useful metric -- it indicates that no block in the CFG is really more or less likely than any other. Only *biases* in a specific direction would

I am seeking for a method to calculate, given a fitted lme model and some data for a subject, the random effects predictors for this subject. I can only find predictors for the subjects used in creating the fit. Of course I could just add the subject and redo the fit. But I want to avoid just this refitting. Thanks for help wbk

I am fitting models to the responses to a questionnaire that has seven yes/no questions (Item). For each combination of Subject and Item, the variable Response is coded as 0 or 1. I want to include random effects for both Subject and Item. While I understand that the datasets are fairly small, and there are a lot of invariant subjects, I do not understand something that is happening

Hello. I'm running a generalized linear model and am interested in using the random effects that are output for further analysis. My random effect is interacting with two different fixed effects (which which are factors with two levels each). When I retrieve the random effects I get something like this: (Intercept) nutrient (Intercept) light (Intercept) Aa-0 0.59679192