similar to: Pairewise Likelihood

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]]

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

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, 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

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

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

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 R-users I intend to create a function which calls some smaller other functions in return. Some of these smaller functions all call some functions. I do not know a good way to do this. I tried using the source() function to include the smaller functions within the main functions before they are called. This does not work, or maybe i am not doing the right thing. For example: the

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)+

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),

Dear R users, I have a data generated as the following, dat <- data.frame(matrix(sample(24), nrow=4)) dimnames(dat) <-list(rownames=c('g1','g2','g3','g4'), colnames=c("A_CH1","A_CH2","B_CH1","B_CH2","C_CH3","C_CH3")) » dat A_CH1 A_CH2 B_CH1 B_CH2 C_CH3 C_CH3 g1 16 24 7 9 14

--- Dimitris Rizopoulos <dimitris.rizopoulos at med.kuleuven.ac.be> wrote: > you could try something like this: > > dat <- array(sample(24), dim=c(4,2,3)) > par(mfrow=c(3,1)) > apply(dat, 3, function(x) plot(rowSums(x), > x[,2]-x[,1])) ## Thank you all for the inputs. It's great of help. The above solution also opens my mind that I could convert my data to an

I have this assignment to do and after ten hours of constant trying my eyes ache and i give up.. all i'm able to get is this plot please help me these are the commands i have used till now read.table(file.choose(), sep=";", header=T) read.table(file.choose(), sep=";", header=T)->areas melt(areas,id=c("Year","State"),m=c("Rice"))->

Hi, I have a quick question regarding estimation of a truncation regression model (truncated above at 1) using MLE in R. I will be most grateful to you if you can help me out. The model is linear and the relationship is "dhat = bhat0+Z*bhat+e", where dhat is the dependent variable >0 and upper truncated at 1; bhat0 is the intercept; Z is the independent variable and is a uniform

Hi I am trying to carry out some maximum likelihood estimation and I'm not making much headway, and I'm hoping that someone will be able to point me in the right direction. I am modelling mortality statistics. One way to do this is to model the mortality rate (or, more accurately, log of the mortality rate, log_m) as (say) a constant plus a proportion of age, plus time, so: r_1 <-

Hi Experts, As i am a new in this forum, let me introduce myself:- i am Debaranjan, from India, Assam. I have heard many things about R and its statistical Magic feature. i am really have nothing knowledge about R language nor have ever used before. i am only a nursery in R. please help me in learning R. I have gone through many study material for R but have gain nothing

I'm havinf a problem with a simple file i have the following data State 1960 1970 1980 1990 1 All India 35988.70 37346.00 39707.30 42321.00 2 Andhra Pradesh 3431.03 3163.27 3687.23 3695.63 3 Assam 1902.93 2001.60 2278.47 2525.33 4 Bihar 5277.07 5133.80 5138.70 4662.57 5 Gujarat 538.13 456.10 484.23 590.47 6

Hello I need to define complex likelihoods in BRUGS-> mixed multinomial probit & random coefficient tobit for panel data can I use the likelihood that i`ve written in R in the BRUGS model? if not, how can I deal with complex likelihoods in BRUGS, there is no integrate function in BRUGS? is there any possibility to this without writing my own sampler in R? Thanx, Johannes