search for: blups

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 column....(I am only concerned with th...

Hi R community, I am new bird to R and moved recently from SAS. I am no means expert on either but very curious learner. So your help crucial for me to learn R. I have already got positive expression. I was trying to fit a mixed model in animal experiment but stuck at simple point. The following similar example is from SAS mixed model pp 212. # data genetic_evaluation <-

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

Dear all, I want to put 9 barplots side by side. My code below only print 5 names from 9 names I gave. Problem: how to print all of those 9 names? I use cex=0.8 but did not work, it gave me error message. d<-matrix(rpois(45,3),5,9) barplot(d,beside=T,col=rainbow(5),names=c("CRTL","LSB","ONEMKR",

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

*Dear All,* *I'm trying to do Multivariate BLUP in R. I'd highly appreciate if someone can share R code and data for Multivariate BLUP. Thanks* * * *Regards! * -- * Muhammad Yaseen * [[alternative HTML version deleted]]

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 interval for the individual intercepts, not the conditional modes of the random effects, that is, the fi...

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 <- sqrt(fm1.s.var[1,1,]) fm1.s0.a <- sqrt(fm1...

Package: xen-utils-4.1 Version: 4.1.4-3+deb7u1 Severity: important When an LVM LV that serves as the root disk for a Xen DomU contains a boot loader (or possibly other data) in its volume boot record, pygrub fails to boot it, printing "Error: boot loader didn't return any data" before exiting. I think this is because of the function "is_disk_image" on line 45 of

...t, I?m trying to use the lmer of the lme4 package to fit a linear mixed model of the form Y = Xb + Zu + e and I can?t figure out how to control the covariance structure of u. I want u ~ N(0,sigma^2*I). More precisely I?m trying to smooth a curve through data using the "Penalized Splines as BLUPs" method as described in Ruppert, Wand & Carroll (2003). So I have Z = [Z1 Z2 ... Z11] where Z1,...,Z11 is a linear spline basis and X = [1 t] where t is time column in my case. I have tried various things and read a lot of the online literature but I can?t seem to find anything useful. I...

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, I wish to learn how to extract best linear unbiased predictor values from a linear mixed model of repeated measures on individuals which has a random error structure (random =~ 1|individual). Thanks for your time, Kind regards Matt -- Matt Robinson Institute of Evolutionary Biology University of Edinburgh

On Mon, 2014-04-21 at 15:43 +0200, Sjors Gielen wrote: > Package: xen-utils-4.1 > Version: 4.1.4-3+deb7u1 > Severity: important > > When an LVM LV that serves as the root disk for a Xen DomU contains a boot > loader (or possibly other data) in its volume boot record, pygrub fails to boot > it, printing "Error: boot loader didn't return any data" before exiting.

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

...could remove plot by taking the difference between the measurements and feeding that into the appropriate model. If I had only one Timepoint, this seems like it would be straightforward since in that case the variance of the difference would just be twice the variance of Variety, and from the blups of the differences I could reconstruct the blups of the individual varieties (or would that not be statistically sound?) However, my differences come in different kinds because of the Timepoints, so it's a bit more complicated. Does anyone have any suggestions for whether it is possible to...

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

....20788 VarietyMarvellous 331.5271 18.20788 0.635 VarietyVictory 331.5271 18.20788 0.635 0.635 Residual 165.5585 12.86695 This is a compound symmetry variance-covariance structure. I then tried to find out the standard deviation and correlation matrix of the BLUPs predictors of the random effects and wish all three standard deviations would be close to 18.20788 and the correlation structure would be consistent with a compound symmetry structure. > sd(random.effects(fm4OatsB)) VarietyGolden Rain VarietyMarvellous VarietyVictory 16.01352...

...models each of the fixed effects has an associated random effect (same predictor). What I find is that fixed effects with larger (absolute) standardized parameter estimates have also a higher estimate of the related random effect. In other words, the higher the average of the absolute value of the BLUPs for a given standardized parameter, the higher its variance. Is this a common situation or I am missing some additional normalization factor necessary to compare the different random effects? Thanks a lot, Bruno ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~ Bruno L. Giordano, Ph.D. CIRMMT Schulich Sc...

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

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