similar to: Question about mixed-effects models example (Pinheiro and Bates)

Hello, I am trying to use lmLists in the lme4 package and copying over very standard code from the nlme package given in 'Mixed-Effects Models in S and S-Plus'. It appears to not accept an 'I(age-11)' in the formula, though it will accept the formula with out the subtraction of 11 from age. This seems like it would be a bug, since this is standard formula syntax, unless

There is a bug in 'anova.gls' in the 'nlme' package (3.1-90). The=20 bug is triggered by calling the function with a single 'gls' object=20 and specifying the 'Terms' argument but not the 'L' argument: > library(nlme) > fm1Orth.gls <- gls(distance ~ Sex * I(age - 11), Orthodont, + correlation =3D corSymm(form =3D ~ 1 |

Hello! If I run this example: library(nlme) fm1 <- lme(distance ~ age+Sex, Orthodont, random = ~ age + Sex| Subject) If I run: summary(fm1) then I can see the fixed effects for age and sex (17.7 for intercept, 0.66 for age, and -1.66 for SexFemale) If I run: ranef(fm1) Then it looks like it's producing the random effects for each subgroup (in this example - each subject). For example,

Dear Mixed Effect Friends, Somehow, R(1021, Windows) seem to run differently from S Plus: The soybean example from Pinheiro/Bates on page 290 fails in R. (Soybean1 is Soybean with the NA and "critical" case removed. Same procedure with full Soybean). > fm1Soy.lis<-nlsList(weight~SSlogis(Time,Asym,xmid,scal),data=Soybean1) > fm1Soy.nlme<-nlme(fm1Soy.lis) Error: Singularity

Hi everyone. I am trying to add a formula to my data using the groupedData function. My experiment consists of randomized block design using fruits, vegetation and time as factors. The idea is to see if fruits, vegetation and time explain the abundance of mice. I am using tree density as a covariate. So I tried to fit the following structure to my data. >

All, Sorry for overly simplistic question, but I can't seem to remember how to create the basic plot shown in Figure 1.1 of Pinheiro & Bates (2004; p.4). The y-axis delineates a factor (Rail) while the x-axis displays the distribution of a continuous variable (time) according to each level of the factor. Didn't see it in archives but perhaps I'm not searching on correct key words,

Dear R users, I would like to exactly reproduce a figure like the 1.5 or 1.9 or 4.13 from the book Mixed effects models in S and S-Plus. Not for the sake of it, but because I have my own data I would like to plot in that fashion (no colors) If I write plot(ergoStool) I can get a good informative plot with colors, but I would like to have a B&W one instead. I've played a little with

Hi, R or S+ users, I want to make a simple transformation for the model, but for the whole design matrix. The model is distance ~ age * Sex, where Sex is a factor. So the design matrix may look like the following: (Intercept) age SexFemale age:SexFemale 1 1 8 0 0 2 1 10 0 0 3 1 12 0 0 4

  Hola,   Por favor, ¿podríais indicarme qué recursos (librerías o ideas) pueden resultar de utilidad para crear un gráfico del estilo del de la figura 3.8 del siguiente link?   http://www.tsc.uvigo.es/BIO/Bioing/ChrLDoc3.html#3.5   Actualmente estoy utilizando funciones muy básicas y la verdad es que no me encuentro muy satisfecha con el resultado.   Muchas gracias.   Eva [[alternative HTML

How does one implement a likelihood-ratio test, to test whether the variances of the random effects differ between two groups of subjects? Suppose your data consist of repeated measures on subjects belonging to two groups, say boys and girls, and you are fitting a linear mixed-effects model for the response as a function of time. The within-subject errors (residuals) have the same variance in

Consider the following plot that shows separate regression lines ~ age for each subject in the Pothoff-Roy Orthodont data, with separate panels by Sex: library(nlme) #plot(Orthodont) xyplot(distance ~ age|Sex, data=Orthodont, type='r', groups=Subject, col=gray(.50), main="Individual linear regressions ~ age") I'd like to also show in each panel the pooled OLS

Dear list members, how can multiple imputation realized for anova tables in R? Concretely, how to combine F-values and R^2, R^2_adjusted from multiple imputations in R? Of course, the point estimates can be averaged, but how to get standarderrors for F-values/R^2 etc. in R? For linear models, lm.mids() works well, but according to Rubins rules, standard errors have to be used together with

Hi, I'm using geeglm function to account for the repeated measure. fit1<- geeglm( binary.outcome ~ age + race + gender + fever.yes.no, data=mydata, id=ID, family=binomial, corstr="exchangeable") summary(fit1)$coef gives too large estimates and standard deviation: Estimate Std.err Wald Pr(>|W|) (Intercept) 3.07e+16

Hi everyone, I have a quick and probably easy question about lme for this list. Say, for instance you want to model growth in pituitary distance as a function of age in the Orthodont dataset. fm1 = lme(distance ~ I(age-8), random = ~ 1 + I(age-8) | Subject, data = Orthodont) You notice that there is substantial variability in the intercepts (initial distance) for people at 8 years, and that

Estimados usuarios: Estoy intentando reproducir el ejemplo 6.4 de Thomas Lumley. Complex Survey. Editorial Wiley. 2010 (ver la página en google:

Hello! could you give me some advice where i can finde out/recieve/download a GLME package, written by Jose Pinheiro. i couldn'ti find it in the package lists (probably becouse it is still in a beta version). thank you for your replay

Jos?? C. Pinheiro Training in UK - Analyzing Mixed-Effects Models with S-PLUS Mixed-effects models provide a powerful tool for analyzing grouped data. This course will overview the application of linear and nonlinear mixed-effects models in the analysis of grouped data, using the NLME software in S-PLUS to illustrate the different stages of model fitting. A new element to the course will cover

I vote to 'fortunize' Doug Bates on Hierarchical data sets: which software to use? "The widespread use of spreadsheets or SPSS data sets or SAS data sets which encourage the "single table with a gargantuan number of columns, most of which are missing data in most cases" approach to organization of longitudinal data is regrettable."

Hello I'm reading up on fitting truncated Weibull distribution to data. There are posts in 2002 that point to this presentation by Prof Bates: http://www.stat.wisc.edu/~bates/JSM2001.pdf but now the file is not there. I can't find it anywhere else, Google doesn't have a cached copy for it. Could someone please give me a copy of this file, if they have it? Thanks and regards,

Kevin Wright wrote: > library(nlme) > m2 <- gnls(conc ~ t1*(1-t2*exp(-k*time)), > data = df.Chloride, > start = list( > t1 = 35, > t2 = 0.91, > k = 0.22)) So my error was to use nls instead that gnls. Thanks a lot, Kevin. > summary(m2) > plot(m2) > lag.plot(resid(m2), do.lines=FALSE) >