search for: pdmatrix

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

...~sex | mrid/set, weights=varPower(fixed=1)) (mydata has over 1000 records, so I won't reproduce it here). When I run this, I get the error: Error: dim<- length of dims do not match the length of object It turns out the error is being thrown by the statement: dim(work$pdFactor) <- dim(pdMatrix(nlmeSt$reStruct[[1]])) indeed: Browse[1]> length(work$pdFactor) [1] 72 Browse[1]> dim(pdMatrix(nlmeSt$reStruct[[1]])) [1] 6 6 It looks like work$pdFactor contains information about both levels of nesting, which is being ignored. Does someone have an idea for a fix (or am I abusing nlme)?...

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 All, I was wondering whether any one knows of a matrix bending function in R that can turn non-positive definite matrices into the nearest positive definite matrix. I was hoping there would be something akin to John Henshall's flbend program (http://agbu.une.edu.au/~kmeyer/pdmatrix.html), which allows the standard errors of the estimated matrix elements to be considered in the bending process. Thanks, Jarrod.

Dear Friends, I am writing to seek any help on "lme" error message. I am using lme to do Mixed-model linear regression. I use my simulated data. However, sometimes, I get the following error message, which I do not understand. "Error in solve.default(pdMatrix(a, fact=TRUE)): system is computationally sigular" I would appreciate any help about it. Thanks a lot Jingyuan Fu Drs, Groningen Bioinformatics Center the Netherlands [[alternative HTML version deleted]]

When trying to fit a generalized linear mixed model using glmmPQL: > fit0 <- glmmPQL(ifelse(response=="A",1,0)~gender,data=set1, random=~1|subject,family=binomial) iteration 1 Error in solve.default(pdMatrix(a, fact = TRUE)) : Lapack routine dgesv: system is exactly singular Could this be occuring because the paired responses for each subject are always the same? If this were the case, what would be the best way to handle this situation? I replaced the response variable with fake data that ar...

...<- glmmPQL(primed ~ log(dist) * role , random = ~ dist | > target.utt / prime.utt , family=binomial(link = "logit"), > data=data.utts, niter=5, verbose = TRUE) > Loading required package: nlme > iteration 1 > iteration 2 > iteration 3 > Error in solve.default(pdMatrix(a, fact = TRUE)) : > system is computationally singular: reciprocal condition > number = 8.65949e-32 > In addition: Warning messages: > 1: Singular precision matrix in level -1, block 4 > 2: Singular precision matrix in level -1, block 4 > 3: Singular precision matrix in...

...=new) summary(rslint)) at which point I obtain the exact same results for each model suggesting that one of the model is not properly specifying the slope or intercept. Or, I receive the following error message when I try to run the random slope/random intercept model. Error in solve.default(pdMatrix(a, fact = TRUE)) : system is computationally singular: reciprocal condition number = 6.77073e-017 (whether I receive an error message or the same results depends on the specific variables in the model). It has been suggested that I may need to change the default starting values in the...

...e-mails to the list. I encountered a problem when running glmmPQL procuedure doing multilevel modeling with a dichotomous outcome. Those are the two error messages I usually get: Error in logLik.reStruct(object, conLin) : NA/NaN/Inf in foreign function call (arg 3) Error in solve.default(pdMatrix(a, fact = TRUE)) : Lapack routine dgesv: system is exactly singular The trick is that the model is a part of a simualtion run, which uses the same starting variance covariance matrix as a source for a mutlivariate normal simulated 2 level dataset. So the variations in the data set are just...

...>mod4<-lme(wthole~poly(nplants,2),data=d3,random=~poly(nplants,2)|field/ subplot,method="ML") But this doesn’t work by either method… > mod4<-lme(wthole~nplants+I(nplants^2),data=d3,random=~nplants+I(nplants^ 2)|field/subplot,method="ML") Error in solve.default(pdMatrix(a, fact = TRUE)) : system is computationally singular: reciprocal condition number = 2.58558e-045 Thanks in advance for clearing up my confusion. The gentler the explanation, the more useful it would be as far as I am concerned as I am not a statistician and have to admit I am not at all...

...king with nlme and I have a question regarding nlme fits that fail because of singularity issues. Specifically, there a way to return an nlme object when the estimation process runs into a singular matrix? For example, can the results up to the point of an error such as "Error in solve.default(pdMatrix(a, fact = TRUE)) : system is computationally singular" or "Error in MEestimate(nlmeSt, grpShrunk) : Singularity in backsolve at level 0, block 1\n" be returned rather than only an error message being returned? Setting the "returnObject" nlme control option to TRUE seems to...

...ure: General positive-definite, Log-Cholesky parametrization StdDev Corr x0 0.5424187 x0 x1 x2 x1 0.4326164 0.739 x2 0.3343281 0.611 0.681 x3 0.3638630 0.569 0.752 0.900 Residual 0.0929820 Number of Observations: 153 Number of Groups: 51 > G = lapply(pdMatrix(a4$modelStruct$reStruct), "*", a4$sigma^2) $teacher x0 x1 x2 x3 x0 0.2942180 0.17330375 0.11089028 0.1123597 x1 0.1733037 0.18715693 0.09847681 0.1183089 x2 0.1108903 0.09847681 0.11177526 0.1094374 x3 0.1123597 0.11830892 0.10943738 0.1323963 Thanks f...

Hi, Does anyone know how to extract fixed effects SE values from generalized linear mixed models estimated using the lmer function in the lme4 library? I searched attributes and structure with no luck. Thanks Frank A. La Sorte, Ph.D. Department of Fisheries and Wildlife Sciences University of Missouri Columbia, MO 65211 USA

Greetings, 1) Is there a nice way of extracting the variance estimates from an lme fit? They don't seem to be part of the lme object. 2) In a series of simulations, I am finding that with ML fitting one of my random effect variances is sometimes being estimated as essentially zero with massive CI instead of the finite value it should have, whilst using REML I get the expected value. I guess