similar to: Marginal Means with the lme()

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, recently, while setting me on the straight and narrow about linear contrasts for a linear mixed effect model, Prof Ripley pointed out that my interpertation of the call anova(mymodel.lme) was not correct, because I was meant to add type = "marginal", as in anova(mymodel.lme, type = "marginal") I tried to look deeper in the issue, asking people, checking on the

I am analyzing data from an experiment with two factors: Carbon (+/-) and O3 (+/-), with 4 replicates of each treatment, and 4 harvests over a year. The treatments are assigned in a block design to individual Rings. I have approaches this as a repeated measures design. Fixed factors are Carbon, O3 and Harvest, with Ring assigned as a random variable. I have performed repeated measures analysis

Dear R-Community! The example "oats" in MASS (2nd edition, 10.3, p.309) is calculated for aov and lme without interaction term and the results are the same. But I have problems to reproduce the example aov with interaction in MASS (10.2, p.301) with lme. Here the script: library(MASS) library(nlme) options(contrasts = c("contr.treatment", "contr.poly")) # aov: Y ~

Hello R Users, I am investigating the basic use of the LME function, using the following example; Response is Weight, covariate is Age, random factor is Genotype model.lme <- lme (Weight~Age, random=~ 1|Genotype) After summary(model.lme), I find that the estimate of Age is 0.098 with p=0.758. I am comparing the above model with the AOV function; model.aov <- aov (Weight~Age + Genotype)

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 am trying to understand better an analysis mean RT in various conditions in a within subjects design with the overall mean RT / subject as one of the factors. LME seems to be the right way to do this. using something like m<- lme(rt~ a *b *subjectRT, random= ~1|subject) and then anova(m,type = "marginal"). My understanding is that lme is an easy interface for dummy coding

Why are the results not reliable? ________________________________ From: ESCHEN Rene [mailto:rene.eschen@unifr.ch] Sent: Wednesday, August 23, 2006 3:48 AM To: Spencer Graves; r-help@stat.math.ethz.ch Cc: Doran, Harold Subject: RE: [R] Random structure of nested design in lme The output of the suggested lmer model looks very similar to the output of aov, also when I ran the model

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

Hi Everyone, I’m trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each

(This time with the r-help in the recipients...) Be careful when mixing lme4 and lmerTest together -- lmerTest extends and changes the behavior of various lme4 functions. >From the help page for lme4-anova (?lme4::anova.merMod) > ?anova?: returns the sequential decomposition of the contributions > of fixed-effects terms or, for multiple arguments, model >

Hi, I wonder if there is a function in (some package of) R which computes marginal effects of the variables in a glm, say, for concretness, a probit model. By marginal effects of the covariate x_j I mean d P(y=1 | x), which is approx g(xB)B_j dx_j where g is the pdf of the normal distribution, x is the vector of covariates (at some points, say, the mean values) and B is the estimated

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 Brian, As Duncan mentioned, the terms type-I, II, and III sums of squares originated in SAS. The type-II and III SSs computed by the Anova() function in the car package take a different computational approach than in SAS, but in almost all cases produce the same results. (I slightly regret using the "type-*" terminology for car::Anova() because of the lack of exact

Please reread my point #1: the tests of the (individual) coefficients in the model summary are not the same as the ANOVA tests. There is a certain correspondence between the two (i.e. between the coding of your categorical variables and the type of sum of squares; and for a model with a single predictor, F=t^2), but they are not the same in general. The t-test in the model coefficients is simply

Hello, I've been running some HLMs using the lme function quite happily; it does what I want and I'm pretty sure I understand it. The issue is that I'm currently trying to estimate a model with a 14-level "nusiance" factor as an independent variable...which makes the output quite ugly. All I'm really interested in is the question of whether these factor as a whole

Hi, I have a question about using "lme" and "aov" for the following dataset. If I understand correctly, using "aov" with an Error term in the formula is equivalent to using "lme" with default settings, i.e. both assume compound symmetry correlation structure. And I have found that equivalency in the past. However, with the follwing dataset, I got different

Hi everyone, I'm having a little trouble with working out what formula is better to use for a repeated measures two way anova. I have two factors, L (five levels) and T (two levels). L and T are both crossed factors (all participants do all combinations). So, I do: summary(aov(dat~L*T+Error(participant/(L*T)),data=dat4)) But get different results with:

Hi Bert, Thanks for the suggestions. I tried lme with different control parameters, and also tried using "ML", instaed of "REML", but still got the same answers. Yes, I hope some gurus on this list could give me some hints. Thanks --- "Gunter, Bert" <bert_gunter at merck.com> wrote: > But they are close. This is almost certainly a > numeric issue --

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,