similar to: lme fitted correlation of random effects: where is it?

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,

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

I fit the model fit<-lm(thresh~cond*Ne) where thresh is the reponse cond is a factor with levels a, b, and c Ne is a continuous indep var I think of this full model as having three lines: thresh as a function of Ne for each condition. Thus we have slopea, slopeb, slopec, inta, intb, intc. lm output my params ------------------------- (Intercept) inta condb intb - inta condc

Hi, I have a client connected to the ''net through 3 ISP''s. Have set up a Linux box to do routing and load sharing for the 3 connections. A fourth interface is connected to the LAN with private IP addresses. Am using iptables to SNAT traffic to the appropriate IP depending on the interface the packet gets routed onto. The setup looks something like this: Interface IP

Hello all. I was wondering if the logLike.nls() and logLike.nlme() functions are still being used. Neither function seems to be available in the most recent release of R (1.9.1). The following is contained in the help file for logLik(): "classes which already have methods for this function include: 'glm', 'lm', 'nls' and 'gls', 'lme' and others in

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

Hi, I have what I think is an easy question. I have a data frame, called stockdata, of stock prices that looks like this: date ticker close 1 01/02/1998 GE 24.667 2 01/05/1998 GE 25.104 3 01/06/1998 GE 24.771 4 01/07/1998 GE 24.979 5 01/08/1998 GE 24.750 6 01/02/1998 HIT 71.125 7 01/05/1998 HIT 72.313

Hi All! I am interested in testing whether the means for the data I am investigating are equal to a specific value - let's say 0.01. I have already run a one-way ANOVA and know that the differences in the means are not significant, so now I want to know what values the means take on. "otestme" is the data I am working with (it would be hard for me to get into a form that would be

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

Good day R-Users, I have a problem accessing some values in the output from the summary of an lme fit. The structure of my data is as shown below (I have attached a copy of the full data). id trials endp Z.sas ST 1 1 -1 -1 42.42884 1 1 1 -1 48.12007 2 1 -1 -1 43.42878 2 1 1 -1

I am using R 2.0.2 on a WinXP I am trying to get the code of the Kruskal-Wallis test but > kruskal.test function (x, ...) UseMethod("kruskal.test") <environment: namespace:stats> > ls(3) [1] "acf" "acf2AR" "add.scope" .............. [181] "kruskal.test" "ks.test"

I am having reports of PCI passthrough problems with SR-IOV, in particular it looks like we are hitting the warning at msi:635 and msi:648 in xen; see the following log: (XEN) [VT-D]iommu.c:1785: VT-d page table not sharing with EPT table xenbus dev backend/vbd/1/5696 error 6 opening device (XEN) PCI add Virtual Function 04:1a.0 (XEN) HVM1: HVM Loader (XEN) HVM1: Detected Xen v4.1.0.rc2.pre (XEN)

Dear R-Users I have a problem accessing some values in the output from the summary of an lme fit. I fit the model below: ggg <- lme (ST~ -1 + as.factor(endp):Z.sas + as.factor(endp), data=dat4a, random=~-1 + as.factor(endp) + as.factor(endp):Z.sas|as.factor(trials), correlation = corSymm(form=~1|as.factor(trials)/as.factor(id)), weights=varIdent(form=~1|endp)) hh

Hi dear R-listers, I'm trying to fit a 3-level model using lme in R. My sample size is about 2965 and 3 factors: year (5 levels), ssize (4 levels), condition (2 levels). When I issue the following command: > lme(var~year*ssize*condition,random=~ssize+condition|subject,data=smp,method ="ML") I got the following error: Error in logLik.lmeStructInt(lmeSt, lmePars) :

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,

Hi, I am trying to place my own functions in the nlme environment: The following statement works: environment(coef.corSPT) <- environment(getS3method("coef","corSpatial")) but this one returns an error: environment(get("coef<-.corSPT")) <- environment(getS3method("coef<-","corSpatial")) Error in

Dear R-Users, I have a question concerning extraction of parameter estimates of variance function from lme fit. To fit my simulated data, we use varConstPower ( constant plus power variance function). fm<-lme(UPDRS~time,data=data.simula,random=~time,method="ML",weights=varConstPower(fixed=list(power=1))) I extract the results of this function by using the following codes:

When I use a model fit with LME, I get an error if I try to use "predict" with a dataset consisting of a single line. For example, using this data: > simpledata Y t D ID 1 -1.464740870 1 0 1 2 1.222911373 2 0 1 3 -0.605996798 3 0 1 4 0.155692707 4 0 1 5 3.849619772 1 0 2 6 4.289213902 2 0 2 7 2.369407737 3 0 2 8 2.249052533 4 0 2 9 0.920044316 1

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,