search for: blup

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 t...

...*************************88 Error: length(f1) == length(f2) is not TRUE In addition: Warning messages: 1: In sire:dam : numerical expression has 20 elements: only the first used 2: In sire:dam : numerical expression has 20 elements: only the first used **********************how can I estimate the BLUP effects?************* #equavalent code in SAS proc mixed data=genetic_evaluation; class sire dam; model adg= / ddfm=kr; random sire dam(sire); estimate 'sire 1 BLUP "broad" ' intercept 1 | sire 1 0; estimate 'sire 1 BLUP "narrow" '...

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...

...nly 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", "TWOMKR","BLUP","BLUPQ","BLUP1M","BLUP2M","GAS")) # with cex=0.8 (but not working) barplot(d,beside=T,col=rainbow(5),names=c("CRTL","LSB","ONEMKR", "TWOMKR","BLUP","BLUPQ","BLUP1M","BLU...

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 f...

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(fm...

...but it returns true because of certain bytes in the first block of 512 bytes being set. This causes get_partition_offsets to seek fruitlessly for a partition instead of simply returning [0], causing the bootloader not to find the right partition. Detailed steps to reproduce: We have a domU called blup.kassala.de, it is running Jessie, its root disk is a LVM LV at /dev/vpsdisks/blup.kassala.de-disk. The first 512 bytes are null: # dd if=/dev/vpsdisks/blup.kassala.de-disk bs=1 count=512 | hexdump -C 00000000 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 |................| * 00000200 "x...

...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 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

...ub from a newer Xen? I can see that much has changed around this area since 4.1.x. If you can reproduce with something newer then I think your best bet is to report this upstream: http://wiki.xen.org/wiki/Reporting_Bugs_against_Xen Ian. > Detailed steps to reproduce: > We have a domU called blup.kassala.de, it is running Jessie, its root disk is > a LVM LV at /dev/vpsdisks/blup.kassala.de-disk. The first 512 bytes are null: > > # dd if=/dev/vpsdisks/blup.kassala.de-disk bs=1 count=512 | hexdump -C > 00000000 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 00 |................|...

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

...ties are random samples from a population of plots and varieties, so they are random effects. The timepoints are fixed effects. I am particularly interested in the variance components for variety and timepoint within variety, in the estimate for the fixed timepoint effect and in the predictions (BLUP) for variety and timepoint within variety. So the mixed model looks like: Measurement ~ Timepoint + Plot + Variety + Variety/Timepoint Here is the question: I am not interested in Plot, so it would be great if I could avoid estimating it. Since I take two measurements from each plot, I could...

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 16.0135...

...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 S...

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

...9 -0.280839171 -1.558589679 Amel-1 0.46083213 -0.02787653 0.885634543 -0.632460142 0.469048098 So...3 intercepts and two values for the interaction terms. My questions are: Which one is the straight-up intercept for the random term itself? How do I get the predictor/estimate (basically, the BLUP) for the different interaction levels? Thanks, Ginnie [[alternative HTML version deleted]]