similar to: Advice on analyzing a mixed effects survival model?

I have a feature request for rsync. I tried posting it to the FAQ-o-matic, but that system didn't seem to be accepting new questions. I hope this is an okay list for the request--apologies if it's misdirected. Mac OS X provides support for the Mac file system resource forks and mac specific metadata (e.g., creator and file type). Most Unix applications ignore this information, but it

I have a dataset with 25 subjects and 25 items. For each subject-item combination, there's a 0/1 score for two parts, A and B. I'm thinking of this as a set of 2 x 2 tables, 25 x 25 of them. I'd like to fit a log-linear model to this data to test the independence of the A and B scores. If I ignore the subject and item parts, the following works just fine: glm(count ~ A * B,

Dear r-help, With lme, is there a way to specify multiple fixed factors under one level of grouping? For example, for a single fixed factor, I use the following: fm1.lme <- lme(fixed=resp ~ fact1, random=~1|subj/fact1, data=mydata) I would like to have multiple factors under subj, like the following for a two-way design, but I get an error: fm2.lme <- lme(fixed=resp ~ fact1*fact2,

Take a look at lmekin() in the coxme package. The motivating data set for my development of coxme was the Minnesota Family Breast Cancer project: 24050 subjects in 462 families. The random effect is an intercept per subject with sigma^2 K as its variance where K is the kinship matrix (1 for self-self, .5 for parent-child or sib-sib, .25 for uncle-neice, etc). lmekin is a linear models front

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

After an extremely long gestation, a new version of the coxme package is now available on CRAN. This function fits very general mixed-effects Cox models using an lmer-like syntax. For instance here is a model that fits a random treatment effect, using data from a multi-study collaboration. fit <- coxme(Surv(pg.time, pg.stat) ~ stage + trt + (trt|site) + stata(site), data=

Hello, I am trying to run a mixed effects nested ANOVA but none of my codes are giving me any meaningful results and I am not sure what I am doing wrong. I am a new user on R and would appreciate some help. The experimental design is that I have some frogs that have been exposed to three acoustic Treatments and I am measuring neural activity (egr), in 12 brain regions. Some frogs also called

Hi, What R libraries should I use to implement mixed effects models with continuous time and discrete-time survival data? What if I have two crossed random effects? I'd appreciate any help. Regards, Hakan Demirtas [[alternative HTML version deleted]]

Dear list, I have been trying to use coxme in R 2.3.1. When I use coxme in the following data sim.fr1, i get "Warning messages: using 'as.environment(NULL)' is deprecated" Why does it occur? How can I hide such warning message, especially when coxme is under a loop? Mohammad Ehsanul Karim (Institute of Statistical Research and Training, University of Dhaka) >

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Dear all, I have encountered the following problem where coxme seems to allow model with only random effect in R 2.11.1 but not in R 2.13.0. Following is the error message using rat example data. Any comment on this is appreciated. In R2.13 > library(coxme) > rat1 <- coxme(Surv(time, status) ~ rx + (1|litter), rats) > rat0 <- coxme(Surv(time, status) ~ (1|litter), rats)

Assume that we collect below data : - subjects = 20 males + 20 females, every single individual is independence, and difference events = 1, 2, 3... n covariates = 4 blood types A, B, AB, O http://r.789695.n4.nabble.com/file/n4245397/CodeCogsEqn.jpeg ?m = hazards rates for male ?n = hazards rates for female Wm = Wn x ?, frailty for males, where ? is the edge ratio of male compare to female Wn =

Hi, I am running a Cox Mixed Effects Hazard model using the library coxme. I am trying to model time to onset (age_sym1) of thought problems (e.g. hearing voices) (sym1). As I have siblings in my dataset, I have decided to account for this by including a random effect for family (famid). My covariate of interest is Mother's diagnosis where a 0 is bipolar, 1 is control, and 2 is major

In the example I posted previous, there is an obvious gaping security hole, it would be trivial for someone to read the querystring and exploit it to make free phone calls, spoof caller ID (if you allow the CallerID to be set with a QueryString value), etc. You want to make damn sure that the URL is not publicly accessible or somehow obsfucate the querystring, or use POST. In my case, I

Dear all, I am unsure about how to specify a model in R and I thought of asking some advice to the list. I have two groups ("Group"= A, B) of subjects, with each subject undertaking a test before and after a certain treatment ("Time"= pre, post). Additionally, I want to enter the age of the subject as a covariate (the performance on the test is affected by age),

> Message: 24 > Date: Tue, 31 Jan 2006 18:22:52 +0000 > From: "Lutz Ph. Breitling" <lutz.breitling at gmail.com> > Subject: [R] Mixed-effects models / heterogeneous covariances > To: r-help at stat.math.ethz.ch > Message-ID: > <2e38a1c80601311022i2e1be92doa60b80b50b69eb0c at mail.gmail.com> > Content-Type: text/plain; charset=ISO-8859-1 > > Dear

Hi, R experts I am currently using lmekin() function in coxme package to fit a mixed effect model for family based genetic data. How can I extract the p value from a lmekin object? When I print the object in R console, I can see the p value and Z value are just over there. But I can not extract them by the coef() function. kinfit$coefficient$fixed (kinfit is the name of the lmekin object)

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

Dear R users: I have some difficulties analizing data with mixed effects NLME and the last version of R. More concretely, I have a repeated measures design with a single group and 2 experimental factors (say A and B) and my interest is to compare additive and nonadditive models. suj rv A B 1 s1 4 a1 b1 2 s1 5 a1 b2 3 s1 7 a1 b3 4 s1 1 a2

I read the list in digest form which sometimes makes me late to respond. Peter Dalgaard wrote: In all fairness, it should probably be noted that quite a few people swear BY S4 in addition to those who swear AT it. Lest I give the impression of only dislike -- the coxme package (major rewrite nearly done, I'm now testing) depends heavily on the bdsmatrix package which implements a very