similar to: Three level linear mixed models

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 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

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 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, 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

Dear R-users Can anyone inform me of a library or more specifically functions that can maximise (or calculate) a Pairwsie likelihood from a data. Better still, i would like to know if there is a function (library) that fits regression models based on pairwise likelihoods. Thanks 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]]

I have three columns of data, Xc, Trt and fish. This was a repeated measures design with 6 measurements taken from each of 5 fish. Xc is the actual measurement, Trt is the treatment, and fish is the fish number. Data can be seen below (hopefully it is in the column format). I would like to look for differences between treatments in a repeated measures format. I used the following code

I'm fitting a regression model for censored data with three categorical predictors, say A, B, C. My final model based on the survreg function is Surv(..) ~ A*(B+C). I know the three-way group mean estimates can be computed using the predict function. But is there any way to obtain two-way group mean estimates, say estimated group mean for (A1, B1)-group? The sample group means don't

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),

Hey, I was using lme and proc mixed in SAS to run a empirical bayesian model. I used the same method for both lme and proc mixed (the default REML). I got very similar, but not identical results. I am just wondering if anyone knows what the differneces may be between proc mixed and lme. Any thoughts would be appreciated! Thanks, Liz

Dear R users I have built several glmm models using glmmPQL in the following structure: m1<-glmmPQL(dev~env*har*treat+dens, random = ~1|pop/rep, family = Gamma) (full script below, data attached) I have tried all the methods I can find to obtain some sort of model fit score or to compare between models using following the deletion of terms (i.e. AIC, logLik, anova.lme(m1,m2)), but I

Dear community, can anyone provide a snippet of code to force the lm() to fit a model with terms in the formula in an arbitrary order? I am interested in something like: lm(y ~ A * B + C, data=data) where the interaction of A and B should be in the formula before C. My goal is to simplify my presentation of models using the anova() statement. I have found that this should be possible using

Dear friends, I found the thread on this subject this summer but wonder whether it has been taken any further. I have an important medical problem where X is computed from a three independent and complicated measurements (exchangeable sodium and potassium and total body water - i.e. X = (Nae+Ke)/TBW ) and Y is serum sodium concentration (all data in Edelman, JCI 1958). I have the individual

Hello! I want to calculate the intra- and interrater reliability of my study. The design is very simple, 5 raters rated a diagnostic score 3 times for 19 patients. Are there methods/funtions in R? I only found packages to calculate interrater variability and intraclass correlation coefficients for matrices of n*m (n subjects, m raters) - I have n subjects, m raters and r repetitions. Can

Dear All, How can I sort a data frame (using one of the column)? Thanks for your support. Regards. Abd. Rahman Kassim (PhD) Head Forest Ecology Branch Forest Management & Ecology Program Forestry and Conservation Division Forest Research Institute Malaysia Kepong 52109 Selangor, Malaysia ***************************************** Checked by TrendMicro Interscan Messaging Security. For any