similar to: aov problem

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 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 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,

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 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

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 <-

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

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

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 Rafael, What about multivariate logistic regression? ----- Forwarded Message ---- From: Rafael Duarte <rduarte@ipimar.pt> To: Anders Malmendal <anders@chem.au.dk> Cc: r-help@stat.math.ethz.ch Sent: Tuesday, May 29, 2007 3:21:11 PM Subject: Re: [R] hierarhical cluster analysis of groups of vectors It seems that you have already groups defined. Discriminant analysis would probably

I want to do hierarchical cluster analysis to compare 10 groups of vectors with five vectors in each group (i.e. I want to make a dendogram showing the clustering of the different groups). I've looked into using dist and hclust, but cannot see how to compare the different groups instead of the individual vectors. I am thankful for any help. Anders

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

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

Hi all, So I think I have seen some similar questions to mine when I searched the archives, but have not seen any concrete answers and was wondering if any one could help. I have been trying to use R's aov() function to analyze my data. I have a 3 x 4 x 2 repeated measures design. All of the IVs are within subjects. I do also have missing values (unequal N), as I have to remove

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

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 =

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 -

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