similar to: NLME: Prediction intervals for random effects

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Hi Eli, Thanks for your rapid response! Now I have another question. How&nbsp;to&nbsp;get LLVM bc files successfully by compiling test.c and static libraries ? Thanks a lot! ----- 原始邮件 ----- 发件人：Eli&nbsp;Friedman&nbsp;<eli.friedman at gmail.com> 收件人：LLVM&nbsp;Developers&nbsp;Mailing&nbsp;List&nbsp;<llvmdev at cs.uiuc.edu>

Hi,all I want to get a relatively accurate cpu usage of a domain. I have a few questions about virDomainGetInfo: struct virDomainInfo{ unsigned char state : //the running state, one of virDomainState unsigned long maxMem :// the maximum memory in KBytes allowed unsigned long memory :// the memory in KBytes used by the domain unsigned short nrVirtCpu :// the number of virtual CPUs for the

Dear professor, I'm using package nlme, but I can't find a way to specify two uncorrelated random effects. For example, a random intercept and a random slope. In package lme4, we can specify&nbsp;x + (x ll g) to realize, but how in nlme? Thanks! ???????????????????????? Zhen Wang Graduate student, Department of Medical Statistics, School of Public Health, Sun Yat-sen

I am doing Principal Component Analysis (PCA) on assets data for household income prediction. The problem is that the assets data are rank ordered (usually binary ... possess car/don't possess car), so the normal correlation is inappropriate for the calculation of the PCA. Instead one has to use the polychoric correlation coefficient. It uses the "random.polychor.pa" package.

<FONT face="Default Sans Serif, Verdana, Arial, Helvetica, sans-serif" size=2><DIV>Dear list members,</DIV><DIV>&nbsp;</DIV><DIV>I am trying to use "nlm" function to maximize a mixture likelihood of beta densities. There are five unknown parameters in the likelihood. Since I can get the analytic gradient, I attach the "gradient"

in a full-virtualization,this is my config file : name = "rhel51x64f" uuid = "8d33b93f-03e1-b2c2-c18f-82003c6e5b6f" maxmem = 2048 memory = 2048 vcpus = 2 builder = "hvm" kernel = "/usr/lib/xen/boot/hvmloader" boot = "cd" pae = 1 acpi = 1 apic = 1 on_poweroff = "destroy" on_reboot = "restart" on_crash = "restart"

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

Dear all, I have a problem formulating random effects when using the nlme package. ?nlme says that the random effects can be a formula or a list of formulae. But when trying to do so, I run into trouble. (R-1.2.2, nlme-3.1-10, Linux) library(nlme) example(nlme) # works nice ... fm1 # the fitted model ### Trying to update with different random effects

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 R users, I've read the posts on this topic, and had a look at the R documentation for nlme, but I can't seem to make this work. I'd like to be able to fit a mixed effects model with crossed random effects, but also be able to specify the covariance matrix structure for the residuals. Here's the syntax using the lmer function in lme4 (which doesn't currently allow

Hi Lindsey, Yeah, I do realize that this answer gets to you 12 years too late, however, it may be helpful to others in future. I have been struggling with the same issue. When analysing the source code (https://svn.r-project.org/R-packages/trunk/nlme/R/nlme.R) I found out that the E matrix needs rownames referring to the random effect of each group in the dataset, something like: rownames(E)

Hello, I'm having trouble correctly specifying the random effects for a nlme model. The general summary of what I'm trying to do is that I've got a data set that has multiple individuals and multiple machines that took measurements from those individuals. At least one of the machines has drift during the day causing a visually linear decrease in the readout during the day, so I have

Using lmer in the lme4 package, you can compute the conditional variance-covariance matrix of the random effects using the bVar slot: bVar: A list of the diagonal inner blocks (upper triangles only) of the positive-definite matrices on the diagonal of the inverse of ZtZ+Omega. With the appropriate scale factor (and conversion to a symmetric matrix) these are the conditional variance-covariance

Hi all, I've been struggling with trying to specify a diagnoal matrix for linear mixed effects model. I think I've got nearly everything correct, except the following message appears: In lme.formula(fixed = fwave ~ sex + sexXbulbar + visit + age + : Fewer observations than random effects in all level 1 groups Not sure if i've provided enough details, but I'm basically trying

> To sum up, I can't figure out how MLWin calculates the > standardized residuals. But I understand that this is not a > question for the R list. > Nevertheless, it would help if someone could point me to some > arguments why not to use them and stick to the results > obtainable by ranef(). Hi Dirk: Well, it is interesting that mlWin and lmer generate the same exact

Hi all, is there a way to calculate standard error of random effect from the estimated model in lme? Best Marcus [[alternative HTML version deleted]]

How can I get prediction intervals from a mixed-effects model? Consider the following example: library(nlme) fm3 <- lme(distance ~ age*Sex, data = Orthodont, random = ~ 1) df3.1 <- with(Orthodont, data.frame(age=seq(5, 20, 5), Subject=rep(Subject[1], 4), Sex=rep(Sex[1], 4))) predict(fm3, df3.1, interval='prediction') # M01 M01

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