similar to: residuals(lme) error message

I have some questions about the use of lme(). Below, I constructed a minimal dataset to explain what difficulties I experience: # two participants subj <- factor(c(1, 1, 1, 1, 2, 2, 2, 2)) # within-subjects factor Word Type wtype <- factor(c("nw", "w", "nw", "w", "nw", "w", "nw", "w")) # within-subjects factor

Dear R-Users, I'm trying to set up a repeated measures anova with two within subjects factors. I tried it by 3 different anova functions: aov, Anova (from car package) and lme (from nlme package). I managed to get the same results with aov and Anova, but the results that I get from lme are slightly different and I don't figure out why. I guess I did not set up the error structure

In my undertstanding of the problem, the model lme1 <- lme(resp~fact1*fact2, random=~1|subj) should be ok, providing that variances are homogenous both between & within subjects. The function will sort out which factors & interactions are to be compared within subjects, & which between subjects. The problem with df's arises (for lme() in nlme, but not in lme4), when

Dear all, In lme, models in which a factor is fully "contained" in another lead to an error. This is not the case when using lm/aov. I understand that these factors are aliased, but believe that such models make sense when the factors are fitted sequentially. For example, I sometimes fit a factor first as linear term (continuous variable with discrete levels, e.g. 1,2,4,6), and

Dear List, This is my first post, and I'm a relatively new R user trying to work out a mixed effects model using lme() with random effects, and a correlation structure, and have looked over the archives, & R help on lme, corClasses, & etc extensively for clues. My programming experience is minimal (1 semester of C). My mentor, who has much more programming experience, but a comparable

Sorry for reposting, but even after extensive search I still did not find any answers. using: summary(aov(pointErrorAbs~noOfSegments*turnAngle+Error(subj/(noOfSegments+turnAngle)), data=anovaAllData )) with subj being a random factor and noOfSegments and turnAngle being fixed factors, I get the following results: ---------------------------------------------- Error: subj Df Sum

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

This is a continuing issue with the one on the list a long time ago (I couldn't find a solution to it from the web): -------------------------------------------------------------------------- > Using a formula converted with as.formula with lme leads > to an error message. Same works ok with lm, and with > lme and a fixed formula. > > # demonstrates problems with lme and

Dear r-help, With lme, is there a way to specify multiple fixed factors under one level of grouping? For example, for a single fixed factor, I use the following: fm1.lme <- lme(fixed=resp ~ fact1, random=~1|subj/fact1, data=mydata) I would like to have multiple factors under subj, like the following for a two-way design, but I get an error: fm2.lme <- lme(fixed=resp ~ fact1*fact2,

Hi, I'm trying to set up AR(1) as a correlation structure in modeling some data (attached file data.txt in text format) with lme, but have trouble getting it to work. Incent, Correctness, and Oppor are 3 categorical variables, Beta is a response variable, and Time is an equally-spaced variable with 6 time points (treated as a categorical variable as well). Basically I want to model the

Hi, I posted the message below last week, but no answers, so I'm giving it another attempt in case somebody who would be able to help might have missed it and it has now dropped off the end of the list of mails. I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no

Using a formula converted with as.formula with lme leads to an error message. Same works ok with lm, and with lme and a fixed formula. # demonstrates problems with lme and as.formula demo<-data.frame(x=1:20,y=(1:20)+rnorm(20),subj=as.factor(rep(1:2,10))) demo.lm1<-lme(y~x,data=demo,random=~1|subj) print(summary(demo.lm1)) newframe<-data.frame(x=1:5,subj=rep(1,5))

Dear ExpeRts, Suppose I have a typical psychological experiment that is a within-subjects design with multiple crossed variables and a continuous response variable. Subjects are considered a random effect. So I could model > aov1 <- aov(resp~fact1*fact2+Error(subj/(fact1*fact2)) However, this only holds for orthogonal designs with equal numbers of observation and no missing values.

In Baron and Li's "Notes on the use of R for psychology experiments and questionnaires" http://cran.r-project.org/doc/contrib/rpsych.htm they describe a balanced data set for a drug experiment: "... a test of drug treatment effect by one between-subject factor: group (two groups of 8 subjects each) and two within-subject factors: drug (2 levels) and dose (3 levels). "

I don't think, this has been answered: > I'm trying to run a 3-way within-subject anova in lme with 3 > fixed factors (Trust, Sex, and Freq), but get stuck with handling > the random effects. As I want to include all the possible random > effects in the model, it would be something more or less > equivalent to using aov > > > fit.aov <- aov(Beta ~ >

Dear All, Comparing linear mixed effect models in SAS and R, I found the following discrepancy: SAS R random statement random subj(program); random = ~ 1 | Subj -2*loglik 1420.8 1439.363 random effects variance(Intercept) 9.6033 9.604662

Dear R experts, I would like to get an R^2 - like value for a multilevel regression using lme. I followed an archived suggestion by José Pinheiro to use the squared correlation between fitted and observed values, i.e., (cor(fitted(model.lme), getResponse(model.lme))^2 but getResponse returns the error message Error in val[, level] : incorrect number of dimensions The same happens with

Dear R users, I fitted a repeated measure model without random effects by using lme. I will use the estimates from that model as an initial estimates to do multiple imputation for missing values of the response variable in the model. I am trying to extract the within group correlation matrix or covariance matrix. here is my code: f = lme(y ~x0+x1+trt+tim+x1:tim +tim:trt,random=~-1|subj,

Dear R-Help, I am trying to perform forward selection on the following coxph model: >my.bpfs <- Surv(bcox$pfsdays, bcox$pfscensor) > b.cox <- coxph(my.bpfs ~ Cbase + Abase + Cbave + CbSD + KPS + gender + as.factor(eor) + Age)>stepAIC(b.cox, scope=list(upper =~ Cbase + Abase + Cbave + CbSD + KPS + gender + as.factor(eor) + Age, lower=~1), direction= c("forward")) However

Hi list I am just beginning to understand the complexities of linear mixed effects models. Maybe someone can give advise concerning the following problem: I have two groups of surgical patients in which repeated laboratory measurements were taken over time after surgery. I decided that lme would be the best model to fit the data. I already fitted the model lme(logratio ~ gr*I(pod-10) +