similar to: lmer - BLUP prediction intervals

Hello, I need some advice how to debug this issue ... Recently I got a new mainboard for a router, it's a Gigabyte GA-H77N-WIFI with two onboard re(4) NICs. The problem is that re0 works fine and re1 doesn't: It doesn't receive any packets. Tcpdump displays all outgoing packets, but no incoming ones on re1. Ifconfig shows the link correctly (100 or 1000 Mbit, depending on where I

I have an Asus P5Q-WS running FreeBSD 7 and the onboard Realtek NIC's are not being detected. No Kernel modifications as this is a fresh install. pciconf -lv : re0@pci0:3:0:0: class=0x020000 card=0x82c61043 chip=0x816810ec rev=0x02 hdr=0x00 vendor = 'Realtek Semiconductor' device = 'RTL8168/8111 PCI-E Gigabit Ethernet NIC' class = network

Dear R-users, I would like to fit a glmm with cross-classified random effects with the function MCMCglmm. Something along the lines: model1<-MCMCglmm(response~pred1, random=~re1+re2, data=data) where re1 and re2 should be crossed random effects. I was wondering whether you could tell me specifying cross-classified random effects in MCMCglmm requires a particular syntax? Are there any

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 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'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]]

Hello! I wonder how can one get upper and lower limits of a prediction interval -- exact values? They are shown on caterpillar plot using ranef() with argument postVar=TRUE, but I would like to know them. A while ago, some discussions were opened on "Confidence Intervals for Random Effect BLUP's", but the answer was never clear: http://www.mail-archive.com/r-help at

Hi all: I get the following error message that I am not able to resolve. Error in if (const(t, min(1e-08, mean(t)/1e+06))) { : missing value where TRUE/FALSE needed It appears right before the last data.frame statement. Below is the program that simulates data from one way random effects model and then computes normality and bootstrap confidence interval for

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 <-

I have the following dataset: 96 plots 12 varieties 2 time points The experiment is arranged as follows: A single plot has two varieties tested on it. With respect to time points, plots come in 3 kinds: (1) varietyA, timepoint#1 vs. variety B, timepoint#1 (2) varietyA timepoint #2 vs. varietyB timepoint #2 (3) varietyA timepoint #1 vs. variety A timepoint#2 - there are 36 of each kind

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

Hello. I'm in need of some help to shed some light on a strange problem. The box is running Samba 4.1.7 on FreeBSD 9.2/amd64 and I suspect (though I'm not sure) all troubles started after the upgrade from 4.1.6. The setup is quite simple: no AD, no LDAP, only one server. A command as simple as "smbclient -U user //SERVER/SHARE", if fired up several times in a row on the

R-users E-mail: r-help@r-project.org Hi! R-users. http://finzi.psych.upenn.edu/R/library/mvpart/html/xpred.rpart.html says: data(car.test.frame) fit <- rpart(Mileage ~ Weight, car.test.frame) xmat <- xpred.rpart(fit) xerr <- (xmat - car.test.frame$Mileage)^2 apply(xerr, 2, sum) # cross-validated error estimate # approx same result as rel. error from printcp(fit) apply(xerr, 2,

Hello all, Sorry if this question has been answered in the past, but I could not find an answer. I am trying to print quotes within a cat output. The arguments are: file= "Data labels" directory= "/home/mylaptop/" The function returns: cat("The file", file, "is located in directory", directory, sep=" ") The output R prints is The file Data

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 R-list, 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

Dear all, I am trying to get the emperical bayes estimates together with their standard errors out of lme4. Up to now I have used MLwiN to get these estimates. I have fitted the following - very simple - model, just to find out how this works. test<-lmer(y~(1|subject),data,REML=F) ranef(test,postVar=T) str(ranef(test,postVar=T) If I use the formulation of the emperical bayes estimates and

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

Hi R users, I am a newbie and therefore the naive question. Sorry but I was unable to find an answer online. I would like to read 47 data sets (country1.raw, country2.raw, ... country47.raw) and save into a matrix each time (data1, data2, ....data47). for (i in 1:47){ data(?) <- read.csv(file.path("country", i, ".raw", fsep = ""), header = TRUE) } The read.cvs

hi all, I want to move some routing tasks out of the router and into the controller. The goal is to make Merb feel less like mod_rewrite and give the user more control at the controller. The new Router is simple: it takes the path_info (not the whole request) then outputs a controller class and some parameters from the path matching. The rest of the routing would be done at the controller level.