similar to: How to extract R{i} from lme object?

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

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

Dear all, Here is the question: For example, using the "petrol" data offered with R. pet3.lme<-lme(Y~SG+VP+V10+EP,random=~1|No,data=petrol) pet3.lme$sigma gives the residual StdDev. But I can't figure out how to extract the "(intercept) StdDev", although it is in the print out if I do "summary(pet3.lme)". In

I'm getting an error from nlme that has me stymied. I have a data set ,'mydata', with variables: AChE, Dose, sex, set, and mrid; 'set' and 'mrid' indicate two levels of nesting, with 'set' nested within 'mrid'. I want to fit the model: mod <- nlme(AChE ~ Cexp(Dose, A, B, m), data=mydata, fixed = A+B+M~sex, random=A+B+m~sex | mrid/set,

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Trying to use contrast to look at differences within an lme lme.fnl.REML <- lme(Max ~ S + Tr + Yr + Tr:Yr, random = ~1 |TID, method = "REML") I have three levels of Tr I'm trying to contrast among different years (R, T97, T98), years = 1997-1999, so I'm interested in contrasts of the interaction term. > anova(lme.fnl.REML) numDF denDF F-value

Dear R-users Coming from a proc mixed (SAS) background I am trying to get into the use of (n)lme. In this connection, I have some (presumably stupid) questions which I am sure someone out there can answer: 1) With proc mixed it is easy to get a hold on the estimated variance parameters as they can be put out into a SAS data set. How do I do the same with lme-objects? For example, I can see the

Sorry if this is obvious, but my S skills aren't great and I haven't been able to find it documented anywhere. I want to write a new function for use with lme objects; the function will simply calculate an ICC (aka "rho") for each level of a mixed-effects model. What I need for this is pretty simple: (c(var1..varn, residual)) / sum(c(var1..varn, residual)) where var1..varn

Hi, R or S+ users, I want to make a simple transformation for the model, but for the whole design matrix. The model is distance ~ age * Sex, where Sex is a factor. So the design matrix may look like the following: (Intercept) age SexFemale age:SexFemale 1 1 8 0 0 2 1 10 0 0 3 1 12 0 0 4

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

Dear listers, First let me appologize if the same mail arrives multiple times. Recently I had some probelms sending my 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)

Hi all! I am working on a nested lme model with one fixed effect ("treatment", which 3 levels) and two random effects for "Individuals" (four of them) within "treatment" and "replicate -2 levels-" within "individual" within "treatment". For doing so, I´ve been trying to create a factor for Individual%in%Treatment, say IT by

R-helpers: I would like to measure the correlation coefficient between the repeated measures of a single variable that is measured over time and is unbalanced. As an example, consider the Orthodont dataset from package nlme, where the model is: fit <- lmer(distance ~ age + (1 | Subject), data=Orthodont) I would like to measure the correlation b/t the variable "distance" at

Here is a piece of code fitting a model to a (part) of a dataset, just for illustration. I can extract the random interaction and the residual variance in group meth==1 using VarCorr, but how do I get the other residual variance? Is there any way to get the other variances in numerical form directly - it seems a litte contraintuitive to use "as.numeric" when extracting estimates,

Hi, nlme users, I am fitting a model for repeated measures, using gls(). The var-cov structure is UNSTRUCTURED in lower triangle and identical for the diagonal, for which I still have no idea how to fit using PROC MIXED. The error message is as follows: Error in "coef<-.corNatural"(*tmp*, value = log((cStNatPar + 1)/(1 - cStNatPar))) : NA/NaN/Inf in foreign function call

Dear all, I am estimating a mixed-model in Ubuntu Raring (13.04¸ amd64), with the code: fm0 <- lme(rt ~ run + group * stim * cond, random=list( subj=pdSymm(~ 1 + run), subj=pdSymm(~ 0 + stim)), data=mydat1) When I check the approximate variance-covariance matrix, I get: > fm0$apVar [1] "Non-positive definite

Hello, I saw that parallel-readdir was an experimental feature in GlusterFS version 3.10.0, became stable in version 3.11.0, and is now recommended for small file workloads in the Red Hat Gluster Storage Server documentation[2]. I've successfully enabled this on one of my volumes but I notice the following in the client mount log: [2018-01-23 10:24:24.048055] W [MSGID: 101174]

----- Original Message ----- > From: "Pranith Kumar Karampuri" <pkarampu at redhat.com> > To: "Alan Orth" <alan.orth at gmail.com> > Cc: "gluster-users" <gluster-users at gluster.org> > Sent: Saturday, January 27, 2018 7:31:30 AM > Subject: Re: [Gluster-users] parallel-readdir is not recognized in GlusterFS 3.12.4 > > Adding

I have been using gnls with the weights argument (and varConstPower) to specify a variance structure for curve fits. In attempting to extract the parameters for the variance model I am seeing results I don't understand. When I simply display the model (or use "summary" on the model), I get what seem like reasonable values for both "power" and "const". When I

Adding devs who work on it On 23 Jan 2018 10:40 pm, "Alan Orth" <alan.orth at gmail.com> wrote: > Hello, > > I saw that parallel-readdir was an experimental feature in GlusterFS > version 3.10.0, became stable in version 3.11.0, and is now recommended for > small file workloads in the Red Hat Gluster Storage Server > documentation[2]. I've successfully