similar to: beginner gls (nlme) question

Dear R-users I have problems using lme The model i want to fit can be viewed as a two-level bivariate model Two-level bivariate: bivariate (S coded as -1,T coded as 1) endpoint within trial OR It can equivalently be considered as a three-level model.Three-level: endpoint within patient, patient within trial. My code tries to model the levels through a RANDOM statement and a

Dear people, A somewhat clueless question follows: I just discovered that the lme function in contrib package nlme for R, while similar to the lme function in Splus, does not use the cluster function option. This difference does not appear to be documented in the V&R `R Complements' file. I have data which is divided into 6 groups The lme model is of the form (simplified from the actual

I was reading the help but just did not get how to specify starting values for varIdent() of the lme() function, although I managed to do it for corSymm(). Do I specify the values just as they are printed out in an output, like c(1, 1.3473, 1.0195). Or do I need to take the residual and multiply it with these like c(0.2235, 0.2235*1.3473, 0.2235*1.0195) or any other form that I dont know of?

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)+

Hello, I have a simulated data structure in which students are nested within teachers, and with each student are associated two test scores. There are 20 classrooms and 25 students per classroom, for a total of 500 students and two scores per student. Here are the first 10 lines of my dataframe "d": studid tchid Y time 1 1 1 -1.0833222 0 2 1 1

Dear all, I am trying to define my own corStruct which is different from the classical one available in nlme. The structure of this correlation is given below. I am wondering to know how to continue with this structure by using specific functions (corMatrix, getCovariate, Initialize,...) in order to get a structure like corAR1, corSymm which will be working for my data. Thanks in advance.

Hi, I usually use lme from the nlme library. Now I have read an article about lmer in Rnews and lmer seemed to me more comfortable to use. Unfortunately, I didn't find out how to use covariance structures (e. g. corSymm(), corAR1()). Is there a way to use them similarly as in lme ? Is it implemented ? If somebody knows, please let me know. Thank you very much in advance, Stephan

Hello, I have used nlme to fit a model, the R syntax is like fmla0<-as.formula(paste("~",paste(colnames(ldata[,9:13]),collapse="+"),"-1")) > fmla1<-as.formula(paste("~",paste(colnames(ldata[,14:18]),collapse="+"),"-1")) >

Hi, You should look at Pinheiro and Bates (2000) Mixed-effects models in S and S-Plus. It describes how to format the correlation matrix to pass to functions lme and gls. Basically, the correlation matrix has to be one of the corStruct classes, probably corSymm for your example. So in the call to lme (or gls if you really have no random effects), use something like:

I've come into some code that produces different results under R 2.1.1 and R 2.2.1. I'm really unfamiliar with the libraries in question (MASS and nlme), so I don't know if this is a bug in my code, or a regression in R. If it's a bug on my end, I'd appreciate any advice on potential causes and relevant documentation. The code:

Hi, I'm fitting a relatively simple growth model to some forest plot data. Two species of trees were planted in different mixtures in 10 (nearly-adjacent) plots and measured on four occasions over 10 years. The model is constructed in terms of the diameter increments (per year; DI) in the 3 intervals, in which DI is modelled as a function of mid-interval D and DSQ. The details of the

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

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

Hi All How can I fit a repeated measures analysis using gls? I want to start with a unstructured correlation structure, as if the the measures at the occations are not longitudinal (no AR) but plainly multivariate (corSymm). My data (ignore the prox_pup and gender, occ means occasion): > head(dta,12) teacher occ prox_self prox_pup gender 1 1 0 0.76 0.41 1 2

Dear List: My question is more statistical than R oriented (although it originates from my work with nlme). I know statistical questions are occasionally posted, so I hope my question is relevant to the list as I cannot turn up a solution anywhere else. I will frame it in the context of an R related issue. To illustrate the problem, consider student achievement test score data with multiple

Hi I am trying to convert SAS codes to R, but some of the result are quite different from SAS. When I ran proc mixed, I have an option ddfm=bw followed by the model. How can I show this method in R?(I am thinking that this maybe the reason that I can't get the similar results) below is my SAS codes: proc mixed data=test covtest empirical; class pair grade team school; model score = trt

Hello, I'm fitting linear mixed models to gene-expression data from microarrays, in a data set where 4608 genes are studied. For a sample of 5 subjects and for each gene we observe the expression level (Intensity) in four different tissues: N, Tp, Tx and M. I want to test whether the expression level is different accross tissues. Between-subject variability is modeled with a random

I am trying to make predictions with se's using a nlme (kew11.nlme below). I get an error indicating levels for a factor are not allowed. I have searched and read Rnews, MEMSS, MASS, R-Help, and other lists in Spanish where I found questions similar to mine but not solution. I do not really care about the method used. Any suggestions to obtain predictions with se's from an nlme

Dear R - Users I have some problems fitting a linear mixed effects model using the lme function (nlme library). A sample data is as shown at the bottom of this mail. I fit my linear mixed model using the following R code: bmr <-lme (outcome~ -1 + as.factor(endpoint)+ as.factor(endpoint):trt, data=datt, random=~-1 + as.factor(endpoint) + as.factor(endpoint):trt|as.factor(Trial),

Hi, I got a computational problem with lme (nlme library R 1.9.1) using corSymm(). Here is the data: [,1] [,2] [,3] [,4] [,5] [,6] [1,] 0.19639793 0.09127954 0.11733288 0.07598273 0.06545106 0.06211532 [2,] 0.22773467 0.10981912 0.16052847 0.38101187 0.18353474 0.24072918 [3,] 0.46743388 0.45733836 0.32191178 0.43356107 0.39159746 0.53984221 [4,]