similar to: Enduring LME confusion… or Psychologists and Mixed-Effects

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

The message for aov1 was "Estimated effects <may> be unbalanced". The effects are not unbalanced. The design is 'orthogonal'. The problem is that there are not enough degrees of freedom to estimate all those error terms. If you change the model to: aov1 <- aov(RT~fact1*fact2*fact3+Error(sub/(fact1+fact2+fact3)),data=myData) or to aov2 <-

I am trying to construct a two-way table where, instead of printing the two-way frequencies in the table, I would like to print the values of a third variable that correspond to the frequencies. For example, the following is easily constructed in R > fact1 <- factor(sample(LETTERS[1:3],10,replace=TRUE)) > fact2 <- factor(sample(LETTERS[25:26],10,replace=TRUE)) > fact3

Hello all, I have some trouble in reconstructing a valid expression within a function, here is my question. I am building a function : SUB<-function(DF,subset=TRUE) { #where DF is a data frame, with Var1, Var2, Fact1, Fact2, Fact3 #and subset would be an expression, eg. Fact3 == 1 #in a first time I want to build a subset from DF #I managed to, with an expression like eg. DF$Fact3, # but I

Dear R-users, consider the two following outputs, ## 1 and ## 2 \begin{Scode}{Setup, echo = FALSE, print = FALSE, eval = TRUE} with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2)) ## 1 xtable(with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2))) ## 2 \end{Scode} The first line with(expand.grid(Fact1 = 1:3, Fact2 = 1:40), table(Fact1, Fact2))

I am using R to make two-way ANOVA on a number of variables using g <- aov(var ~ fact1*fact2) where var is a matrix containing the variables. However the outcome seem to be dependent on the order of fact1 and fact2 (i.e. fact2*fact1) gives a slightly (factor of 1.5) different result. Any ideas why this is? Thanks for any help Anders

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,

Hello, I am a new user or R, and am so far very impressed with its capabilities. However, I have no programming experience, and am having some issues in trying to tell the software what I want done. There are basically two issues which I am currently grappling with. The first, I have a data matrix, with two factors and dozens of response variables. I am interested on conducting ANOVAs on

I am trying to put 3 dotcharts side-by-side with minimal space between each. Each chart is for a different variable, but the vertical axes are the same. I want to have vertical axis labels on the lefthand chart but no vertical axis labels on the other two. Plus, I would like very little space between charts 1 & 2 and between charts 2 & 3. I have one approach but am not too happy with

Hi List, Say I have a data.frame "DF" with 6 columns, 3 factors and 3 variables, with different number of repetitions for each combination of factors. I would like to build, for two given factors, a matrix per variable, containing in each cell the mean or sd for a given couple of factors. I have managed to get to the result I wanted step by step, but I would like to have it in a

Hi list, This is a very basic question about lattice: I wish to add some vertical lines in each panel of a xyplot as demonstrated in this example: > library(lattice) > > xx <- seq(1, 10, length=100) > x <- rep(xx, 4) > y <- c(cos(xx), sin(xx), xx, xx^2/10) > fact <- factor(rep(c("cos", "sin", "id", "square"), each=100))

Dear list, I've been trying this for a few hours and I just don't understand how lattice works with groups and subscripts. Consider the following example, > xx <- seq(1, 10, length=100) > x <- rep(xx, 4) > y <- c(cos(xx), sin(xx), xx, xx^2/10) > fact <- factor(rep(c("cos", "sin", "id", "square"), each=100)) > fact2

Dear all, I'm having a few problems trying to reshape a data frame. I tried with reshape{stats} and melt{reshape} but I was missing something. Any help is very welcome. Please find details below: ################################# # data in its original shape: indiv <- rep(c("A","B"),c(10,10)) level.1 <- rpois(20, lambda=3) covar.1 <- rlnorm(20, 3, 1) level.2

I've posted the following to R-help on May 15. It has reproducible R code for real data -- and a real (academic, i.e unpaid) consultion background. I'd be glad for some insight here, mainly not for myself. In the mean time, we've learned that it is to be expected for anova(*, "marginal") to be contrast dependent, but still are glad for advice if you have experience. Thank

Dear R-helpers, I'm trying to selectively swap labels between two factors, depending on an indicator variable i. Can you point me to a solution, and perhaps how I could have found it? labels(fact1) is a character vector of r row numbers levels(fact1) is a character vector of the n < r unique levels How do I then get the character vector of length r of the levels of fact1? Once I have

Dear all, The following example code produces a graph with ggplot2, to which I add several vertical lines of arbitrary colors. I am not satisfied with the legend: it automatically adds some vertical lines which I'd rather not see (they confuse the reader rather than add information in this case). > library(ggplot2) > dfr <- data.frame(values = sin(1:50/10), > fact =

Dear R-users, Sorry for reposting. I put it in another way : I want to test random effects in this random effect model : Rendement ~ Pollinisateur (random) + Lignee (random) + Pollinisateur:Lignee (random) Of course : summary(aov(Rendement ~ Pollinisateur * Lignee, data = mca2)) gives wrong tests for random effects. But : summary(aov1 <- aov(Rendement ~ Error(Pollinisateur * Lignee), data =

Hello to all users and wizards. I am regulary using 'boxplot' function or its analogue - 'bwplot' from the 'lattice' library. But they are, as far as I understand, totally flawed in functionality: they miss ability to select what they would draw 'in the middle' - median, mean. What the box means - standard error, 90% or something else. What the whiskers mean -

I'm starting to understand fixed and random effects, but I'm puzzled a bit. Here is an example from Hays's textbook (which is great at explaining fixed vs. random effects, at least to dummies like me), from the section on mixed models. You need library(nlme) in order to run it. ------ task <- gl(3,2,36) # Three tasks, a fixed effect. subj <- gl(6,6,36) # Six subjects, a random

Dear all, I need an help because I am really not able to find over internet a good example in R to analyze an unbalanced table with Anova with repeated measures. For unbalanced table I mean that the questions are not answered all by the same number of subjects. For a balanced case I would use the command aov1 = aov(response ~ stimulus*condition + Error(subject/(stimulus*condition)), data=scrd)