similar to: BLUP with missing data

*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]]

I have a matrix 700x2000 which is sampled in each cycle from another matrix 788x2000 with the numbers 0,1 and 9 There is one specific collumn of this matrix, dart[,1977], that usually, after the samplimg procedure has only 1 and 9 (because the zero frequency in this collumn is low). However, when this happens, I want to include an IF conditional in my code. so basically what i wanted to do was to

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, 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

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

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

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",

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

I have to deal with data frames that contain multiple entries of the same (based on an identifying collumn 'id'). The second collumn is mostly corresponding to the the id collumn which means that double entries can be eliminated with ?unique. a <- unique(data.frame(timestamp=c(3,3,3,5,8), mylabel=c("a","a","a","b","c"))) However

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.

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 <-

was trying to match different matrices of different lengths with in the first collumn date and time info (yearmonthdayhourminute). the routine needs to return NA?s where data of either of the matrices is non existent. have been trying the following: #### x <- c(200112030003, 200112030004, 200112030005, 200112030006) y <- c(0.1, 1, 1.1, 1.5) a <- c(200112030004, 200112030005,

hi all a technical question for those bright statisticians. my question involves ridge regression. definition: n=sample size of a data set X is the matrix of data with , say p variables Y is the y matrix i.e the response variable Z(i,j) = ( X(i,j)- xbar(j) / [ (n-1)^0.5* std(x(j))] Y_new(i)=( Y(i)- ybar(j) ) / [ (n-1)^0.5* std(Y(i))] (note that i have scaled the Y matrix as well) k is

Reply to sender Reply to all Reply to folder Forward Move/Copy Delete Read previous item Read next item Get help information on the current window From: McMurtry, Benjamin G. To: 'r-help-request at stat.math.ethz.ch' Cc: Subject: Table Help Sent: 5/24/2005 4:33 PM Importance: Normal I have a very large table that I want to add some of the certain rows. The table is

Dear all, I have a data.frame like the sample below, and I would like to expand my data.frame using "population" variable. So, for each line of my data.frame I would like that the new data.frame have many rows as the population collumn. place<-c("place1", "place2", "place3", "place4", "place5") population<-c(100,200,300,50,30)

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

Hi experts, I am trying to write a very flexible method that allows me to add a new column to an existing data frame. This is what I have so far: add.column <- function(df, new.col, name) { n.row <- dim(df)[1] length(new.col) <- n.row names(new.col) <- name return(cbind(df, new.col)) } df <- NULL df <- data.frame(a=c(1,2,3)) df # corect: added NA to new collumn df <-

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

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,<?xml:namespace prefix = o ns = "urn:schemas-microsoft-com:office:office" /><o:p></o:p> I would like to create a histogram from a data collumn consisting of 4 classes (0; 0.05;0.5;25;75). Due to the difference in scale the classes 0;0.05 and 0.5 are displayed within one combined bin by default with the code:Hist(x, scale="percent",