similar to: Extracting SD of random effects from lme object

LS, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Dear helpers, I'm estimating multilevel regression models, using the lme-function from the nlme-package. Let's say that I estimated a model and stored it inside the object named 'model'. The summary of that model is shown below: Using summary(model)$tTable , I receive the following output: > summary(model)$tTable Value Std.Error DF t-value

Hello R-users, I am using gls function in R to fit a model with certain correlation structure. The medol as: fit.a<-gls(y~1,data=test.data,correlation=corAR1(form=~1|aa),method="ML") mu<-summary(fit.a)$coefficient With the toy data I made to test, the estimate of mu is exactly equal to the overall mean of y which can not be true. But, if I make a toy data with y more than two

Howdy, I am trying to invert a matrix for the purposes of least squares. I have tried a number of things, and the variety of results has me confused. 1. When I try solve() I get the following: >Error in solve.default(t(X) %*% X) : system is computationally singular: reciprocal condition number = 3.76391e-20 2. When I try qr.solve(), I get: >Error in qr.solve(t(X) %*% X) : singular matrix

Hello, I am trying to upgrade to 2.11 from 2.10 in the hopes of gaining TCL/TK capabilities (let's pretend for the moment that this should work). I've added "deb http://cran.stat.ucla.edu/bin/linux/ubuntu lucid/" to /etc/apt/sources.list as described in the guide and have run apt-get update apt-get install r-base r-base-dev Things seem to work. When I look at the installed

Hello, I am using R2.5 under Windows. Looks like the following statement vars <- (obj$sigma^2)*vw in getVarCov.gls method (nlme package) needs to be replaced with: vars <- (obj$sigma*vw)^2 With best regards Andrzej Galecki Douglas Bates wrote: >I'm not sure when the getVarCov.gls method was written or by whom. To >tell the truth I'm not really sure what

HI, I would like to extract the variance components estimation in lme function like a.fit<-lme(distance~age, data=aaa, random=~day/subject) There should be three variances \sigma_day, \sigma_{day %in% subject } and \sigma_e. I can extract the \sigma_e using something like a.fit$var. However, I cannot manage to extract the first two variance components. I can only see the results in

Hi, I' trying to learn how to use lme for Linear Mixed Models and I have a problem when I have to include more than one random effect in my model. I know that this could be a stupid question to ask, but I'm not able to solve it by myself... One example: if my model is response = operator + block + day with operator and block as fixed effects and day as random effect, I use res.lme

Dear List: I am estimating a gls model and am having to make some rather unconventional modifications to handle a particular problem I have identified. My aim is to fit a GLS with an AR1 structure, obtain the variance-covariance matrix (V), modify it as needed given my research problem, and then reestimate the GLS by brute force using matrix operations. All seems to be working almost perfectly,

Dear R-users I am relatively new to R, i hope my many novice questions are welcome. I have problems accessing some objects (specifically the random effects, correlation structure and variance function) from an object of class gls and lme. I used the following models: yah <- gls (outcome~ -1 + as.factor(Trial):as.factor(endpoint)+

Howdy, In SPSS, there are 2 ways to weight a least squares regression: 1. You can do it from the regression menu. 2. You can set a global weight switch from the data menu. These two options have no, in my experience, been equivalent. Now, when I run lm in R with the weights= switch set accordingly, I get the same set of results you would see with option #1 in SPSS. Does anybody know how to

Howdy, Referencing the below exchange: https://stat.ethz.ch/pipermail/r-help/2006-April/103862.html I am still getting the same warning ("non-integer #successes in a binomial glm!") when using svyglm:::survey. Using the API data: library(survey) data(api) #stratified sample dstrat<-svydesign(id=~1,strata=~stype, weights=~pw, data=apistrat, fpc=~fpc)

Dear R-helpers I am running a simple mixed effects model using lme(). The call looks like this: fit <- lme(Analyte~Sample, data=Data, random=~1 | Run) I am particularly interested in the estimated random effects. When I print the 'fit' object, it looks something like example below: (...) Random effects: Formula: ~1 | Run (Intercept) Residual StdDev: 3.483794 3.637523

Greetings- I need to extract, programatically, the standard deviations of the intercept and residuals from an lme model. These are presented by print.lme as: ... (Intercept) Residual StdDev: 1.410635 0.7800512 ... (data taken from ?lme's examples section) I can get the residuals with x$sigma where x is the fitted lme object. I can't find the intercept, though. The closest

I am trying to use lme to fit a mixed effects model to get the same results as when using the following SAS code: proc mixed; class refseqid probeid probeno end; model expression=end logpgc / ddfm=satterth; random probeno probeid / subject=refseqid type=cs; lsmeans end / diff cl; run; There are 3 genes (refseqid) which is the large grouping factor, with 2 probeids nested within each refseqid,

Dear List: I am working to construct a matrix of a particular form. For the most part, developing the matrix is simple and is built as follows: vl.mat<-matrix(c(0,0,0,0,0,64,0,0,0,0,64,0,0,0,0,64),nc=4) Now to expand this matrix to be block-diagonal, I do the following: sample.size <- 100 # number of individual students I<- diag(sample.size) bd.mat<-kronecker(I,vl.mat) This

Hi, lme() users, Can some one tell me how to do this. I model Orthodont with the same G for random variables, but different R{i}'s for boys and girls, so that I can get sigma1_square_hat for boys and sigma2_square_hat for girls. The model is Y{i}=X{i}beta + Z{i}b + e{i} b ~ iid N(0,G) and e{i} ~ iid N(0,R{i}) i=1,2 orth.lme <- lme(distance ~ Sex * age, data=Orthodont, random=~age|Subject,

Dear R-help list readers, I am fitting mixed models with the lme function of the nlme package. If I get convergence depends on how the method (ML/REM) and which (and how much) parameters will depend randomly on the cluster-variable. How get the bist fit without convergence? I set the parameters msVerbose and returnObject to TRUE: lmeControl(maxIter=50000, msMaxIter=200, tolerance=1e-4,

The print method for lme *prints out* the fitted correlation matrix for the random effects. Is there any way to get these values as an object in R? I have examined the components of the lme object (called "junk" in the example below) and the components of summary(junk) without finding these numbers. (How I did this: I dumped the entire lme object to a text file and then used egrep to

Hello friends, I come to ask for a hand. I have an AD server with Samba 4.1 and added a Member Server 4.4 without problems. The only problem I'm having is that the UID of users in the Member Server are different from the AD server. Ad Server KENNEDY\florenciaelmone:*:3000679:100:Florencia Elmone Domingues:/home/KENNEDY/florenciaelmone:/bin/false Member Server