similar to: Linear mixed model with correlated residual

Dear R Users I have difficulties accessing the variance components for an lme fit when the variance covariance matrix of the random effects is not positive definite. Can anyone inform me on how to get by this ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

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),

Hello R-users Is it possible to fit a three level linear mixed effect model in R? If anyone has an idea or sample code, i will appreciate it very much if i can receive it. I am reading the book by Pinheiro and Bates but have not come across that yet! Kind regards Pryseley --------------------------------- [[alternative HTML version deleted]]

Dear R-Users, Is it possible to get the covariance matrix from an lme model that did not converge ? I am doing a simulation which entails fitting linear mixed models, using a "for loop". Within each loop, i generate a new data set and analyze it using a mixed model. The loop stops When the "lme function" does not converge for a simulated dataset. I want to

Dear R-Users I used the nlme library to fit a linear mixed model (lme). The random effect standard errors and correlation reported are based on a Log-Cholesky parametrization. Can anyone tell me how to get the Covariance matrix of the random effects, given the above mentioned parameters based on the Log-Cholesky parametrization?? Thanks in advance Pryseley

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 have been asked to analyse some questionnaire data- which is not data I'm that used to dealing with. I'm hoping that I can make use of the nabble expertise (again). The questionnaire has a section which contains a particular issue and then questions which are related to this issue (and potentially to each other): 1) importance of the issue (7 ordinal categories from -3 to +3) 2) impact

Hi, There are 20 subjects grouped by Gender, each subject has 2 tissues (normal vs. cancer). In fact, it is a 2-way anova (factors: Gender and tissue) with tissue nested in subject. I've tried the following: Model 1: lme(response ~ tissue*Gender, random = ~1|subject) Model 2: response ~ tissue*Gender + subject Model 3: response ~ tissue*Gender It seems like Model 1 is the correct one

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

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 R users, Can someone inform me about a library/function in R that fits a Plackett-Dale model ? Thanks in advance Pryseley --------------------------------- [[alternative HTML version deleted]]

Hello, Sorry to be a bit longwinded, but I've struggled quite a bit with the following over the last few days. I've read all entries related to spatial autocorrelation in R help and haven't found what I'm after. If it's okay, I'm going to first describe my general understanding of the process by which a mixed model can account for correlated errors. If possible, please

Dear Experts, I am new to R and unfortunately cannot start with a simply statistical analysis: I manually determined the volume of the right and left hippocampus in a group of meditators and in a group of controls. My data-sheet looks as follows: observation subject group age gender hemisphere volume 1 am04 m 25 f left 3.637 2 am04 m 25 f right 3.713 3 ao08 m 47 m left 3.715 4 ao08 m 47

(apologies if this looks like a re-post, I just sent a similar message to the r-help mail list. This version is via Nabble.) My intended application is error propagation using the ISO GUM Supplement 1 approach (propagation of distributions using Monte Carlo strategies). To automate uncertainty analysis I typically have the following data: (1) a measurement function y(x1,x2,...xn) (2) 'n'

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 R-users I am new to R and trying to write some functions. I have problems writing functions that takes a data set as an arguement and uses variables in the data. I illustrate my problem with a small example below: sample data #------------------ visual24<-rnorm(30,3,5) visual52<-rt(30,7) dats<- data.frame(cbind(visual24,visual52)) remove(visual24, visual52)

Dear R-users: On page 155 of "Mixed-effects Models in S and S-Plus", the degree of freedoms of the anova comparison of lme and lm are 8 and 5. But when I use the following SAS code: proc glm data=ortho2; class gender; model distance = age|gender / solution ; run; The df is 3. Could you please explain this to me? Thanks Joe

Hi all, I don't know if anyone has any thoughts on this. I have been trying to move from SAS Proc Mixed to R nlme and have an unusual result. I have several subjects measured at four timepoints. I want to model the within-subject correlation using an autoregressive structure. I've attached the R and SAS code I'm using along with the results from SAS. With R lme I get an estimate of

Lets say I have a linear model and I want to find the average expented value of the dependent variable. So let's assume that I'm studying the price I pay for coffee. Price = B0 + B1(weather) + B2(gender) + ... What I'm trying to find is the predicted price for every possible combination of values in the independent variables. So Expected price when: weather=1, gender=male weather=1,

Dear all, I would like to know how to run an analysis of covariance in R. For example, I have a data frame ("data") consisting of two second-degree categorical variables ("diagnosis" and "gender"), one continous independent variable ("age") and one continous dependent variable ("response"). I ran a simple anova to see the effects of diagnosis