similar to: multiple numerical variables in aov

Hi, I'm doing anova on a matrix of multivariate data where I want to assess the effect of each column (element). My matrix is 86 rows x 31 columns. I've created a grouping factor of length 86 containing group assignments of 6 types. Then I run: x<- aov(matrix~grouping.factor) summary(aov.fit.raw, test="Wilks") This is working fine enough, but I'm getting different

Hello Again Gurus and Lurkers: I?m trying to build a very user-friendly function which does aov without having the user type in a formula (which would be tedious in this case). The idea is to take the response from a PCA score matrix, and the factors from a list. A simple example is the function given below, along with test data and a sample call to the function. I'm certainly having

Hi All, I am currently trying to use R to perform some statistical discriminant analysis on a data set using ANOVA. I'm new to anova and R as well. As I understand it, ANOVA can be used to perform discriminant analysis by measuring the value of the f-test when given a data set with a grouping variable and the dependent variable. What I would like to do in R is perform ANOVA and extract the

Dear all, I'm testing the effect of species and sex in my sample by using the principal component scores of a PCA analysis. I have 30 PCs and I tried to see if there is any significant difference from males to females, given that there is a significant effect of phylogeny (factor with several species). I didi it like this: Y<-PCA$pc.scores[,1:30] fit <- manova(Y ~ sp*sex)

Hi R help, Hi R help, Which is the easiest (most elegant) way to force "aov" to treat numerical variables as categorical ? Sincerely, Andrea Bernasconi DG PROBLEM EXAMPLE I consider the latin squares example described at page 157 of the book: Statistics for Experimenters: Design, Innovation, and Discovery by George E. P. Box, J. Stuart Hunter, William G. Hunter. This example use

Hi Sergio Doing those tests 30 times is going to give you a huge Type I error rate, even if there was a function that did that. There is a reason why TukeyHSD doesn't make it easy. In general, if there are useful comparisons among the species, you are better off setting up and testing contrasts than doing all-pairwise Tukey tests. Also, the PCA scores are ordered in terms of variance

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

Hi all, I have a question regarding the boxplot function. The data I am working on has 1 grouping variable (G) and it has many numerical variables (V1, V2, V3, V4, Vx, etc). What I would like to do is create a boxplot where the Y-axis represents the numerical values of variable V1...Vx (all the variables have the same range). The X-axis needs to represent the G-V combination. So suppose the

Hi.? There appears to be a bug in R function manova.? My friend and I both ran it the same way as shown below (his run) with the shown data set. His results are shown below. we both got the same results.? I was running with R 2.3.1. I'm not sure what version he used. Thanks very much, David Booth Kent State University -----Original Message----- From: dvdbooth at cs.com To: kberk at

Hi, Can someone tell me how to normalize a data set so that the mean of the set is 0 and the variance is 1. As I understand, when you calculate the principle components of a data set through correlation as < princomp( dataset, cor=T ) > then a similar calculation is performed. I would like to know how I can perform such a calulation directly. Any help would be greatly appreciated. Many

Hi all, I am experimenting the function "manova" in R. I tried it on a few data sets, but I did not understand the result: I used "summary(manova_result)" and "summary(manova_result, test='Wilks')" and they gave a bunch of numbers... But I need the Sum-of-Squares of BETWEEN and WITHIN matrices... How do I read off from the R's manova results? Any

I'm trying to learn to use manova(), and don't understand why none of the following work: > data(iris) > fit <- manova(~ Species, data=iris) Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : incompatible dimensions > fit <- manova(iris[,1:4] ~ Species, data=iris) Error in model.frame(formula, rownames, variables, varnames, extras,

Ronaldo, Further to my previous posting on your Glycogen nested aov model. Having read Douglas Bates' response and Reflected on his lmer analysis output of your aov nested model example as given.The Glycogen treatment has to be a Fixed Effect.If a 'treatment' isn't a Fixed Effect what is ? If Douglas Bates' lmer model is modified to treat Glycogen Treatment as a purely

Hello, I'm a new user to R so apologies if this is a basic question, but after scouring the web on information for summary.formula, I still am searching for an answer. I made a function to analyze my data - I have a categorical variable and three continuous variables. I am analyzing my continuous variables on the basis of my categorical variables. radioanal <- function(a) { #Educational

Let's say I have a within-subject experiment with 2 observables, obs1 and ob2 and 2 independent factors, fac1 and fac2. I can do summary( aov( obs1~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) summary( aov( obs2~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) to test the 2 observables separately. > summary( fit<-manova( cbind(obs1,obs2)~fac1*fac2 + Error(Subject/(fac1*fac2)) ) ) gives

Hi everyone, Could somebody explain to me what is the package/function for classification analysis. I am performing analysis of music files in the form of MIDI files. I end up with about 750 dependent variables from the analysis, I also have a number of independent/grouping variables that I set manually. What I would like is to be able to predict which group a particular MIDI files belongs to

Hi everybody ... I have to perform Hotteling's T^2 Test - more generally a MANOVA - on a set of data. Is there a (simple?) possibility to do it in R? The somewhat obvious way to do it would be > summary(aov(Y ~ x1 + x2 ... + xn)) where Y would be a two-column matrix. But this does not work in any possible combination of matrix or factor dimensions! Is it principally not possible

Hello . I have a little question .. I work with Anova (aov) and Manova (manova) and want to print some result in a text file. I see the results with the summary function or the summary.aov function but i do not know how to save the differents probabilities of error in a value. Sorry for my poor English Thanks a lot. -- Lamoral Julien

Hi everyone, I'm fairly new to R (and this forum!), so first of all apologies if this question has already been answered (I've tried searching the forum but haven't come across anything which has solved the problem I'm having). I have a data frame containing an "independent variable" and a series of potential "dependent variables", and I'd like to find

Dear all, I am trying to create eps files of R plots (in Linux) so that I can import them into Word (obviously in MS Windows). What I would like is for the files to be cropped so that there is no margin around the actual plot, because I have no way of editing the files after they have been created. I have tried using par(mai=c(.75,.75,0,0)) in order to reduce the margin; it works fine when I