similar to: manova discriminant functions?

Hi, I am trying to reconcile anova table in R (summary(lm)) with individual t.test. datafilename="http://personality-project.org/R/datasets/R.appendix1.data" data.ex1=read.table(datafilename,header=T) #read the data into a table summary(lm(Alertness~Dosage,data=data.ex1)) gives: Call: lm(formula = Alertness ~ Dosage, data = data.ex1) Residuals:    Min     1Q Median     3Q    Max

Hello, I'm new to R and have been following many guides including the two-way anova (http://www.personality-project.org/r/r.anova.html). Using that walkthrough including the supplied data I do str(data.ex2) and receive the appropriate types of data as follows: > str(data.ex2) 'data.frame': 16 obs. of 4 variables: $ Observation: int 1 2 3 4 5 6 7 8 9 10 ... $ Gender :

Hi all, How does R know to regard a variable as a factor and not a character? For example, consider the following table: Observation Gender Dosage Alertness 1 m a 8 2 m a 12 3

Hi there, I checked google for aov. usually one uses summary to see whether the p-value is small. but I want to put aov in my script. how can I get the p-value, (0.1115, 0.6665, 0.6665 in the following example)? thanks YU > datafilename="http://personality-project.org/r/datasets/R.appendix2.data" > data.example2=read.table(datafilename,header=T) > aov.ex2 =

I don't understand which is the problem Could you help me please? Thanks in advance Marta > str(data_indiciN2) 'data.frame': 200 obs. of 36 variables: $ gruppo: Factor w/ 2 levels "0","1": 2 2 2 2 2 2 2 2 2 2 ... $ I001N2: num 19.32 8.22 28.35 7.24 14.7 ... $ I002N2: num 2.92 2.54 0.11 1.6 7.12 ... $ I003N2: num -22.362 -0.222 -19.291

Dear R-users; Previously I posted a question about the problem of rank deficiency in summary.manova. As somebody suggested, I'm attaching a small part of the data set. #*************************************************** "test" <- structure(.Data = list(structure(.Data = c(rep(1,3),rep(2,18),rep(3,10)), levels = c("1", "2", "3"), class =

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,

Dear r-helpers, I have a query regarding use of contrasts in MANOVA. summary(manova(model)) gives me only result of test for overall difference. Would you be so kind and give me a hint how to get the same test statistics (e.g.Pillai's) and P values for the predefined contrasts? Best regards Ondrej Mikula -- Institute of Animal Physiology and Genetics Academy of Sciences of the Czech

Hola: Probablemente está tomando 'Edad_Manova_18a54' y 'Genero2_1a2' como variables numéricas (integer). Deberías convertirlas en factor. Por ejemplo: Edad_Manova_18a54 <- factor(Edad_Manova_18a54) Genero2_1a2 <- factor(Genero2_1a2) Manova.fnc(datosPAS, variables=2:6, fac.inter=c('Edad_Manova_18a54','Genero2_1a2'),poshoc=c('todos')) Saludos,

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)

Muy buenas, He usado muy poco R en mi vida, y ahora estoy intentando hacer una MANOVA, con dos variables independientes (Edad, que tiene 4 niveles y Genero que tiene 2) y 5 variables independientes. Le he puesto también que me haga el poshoc de las dos y la interacción. Decir también que me instalé el ULLRToolbox, que no se si tendrá algo que ver con el error que me da. Este es el código que uso

Dear friends, Sorry for this somewhat generically titled posting but I had a question with using contrasts in a manova context. So here is my question: Suppose I am interested in doing inference on \beta in the case of the model given by: Y = X %*% \beta + e where Y is a n x p matrix of observations, X is a n x m design matrix, \beta is m x p matrix of parameters, and e is a

Dear all, I am quite new to R and I am having trouble writing the polynomial contrasts for an ordinal factor in MANOVA. # I have a model such as this fit<-manova(cbind(Y1,Y2,Y3)~Groups,data=Events) # where groups is an ordinal factor with 4 levels # how to set polynomial contrasts for the "Groups" factor ? Thank you very much in advance for any help! Best regards, Mauro -- Dr.

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

Hi, I would like to conduct a MANOVA. I know that there 's the manova() funciton and the summary.manova() function to get the appropriate summary of test statistics. I just don't manage to specify my model in the manova() call. How to specify a model with multiple responses and one explanatory factor? If I type:

Hello, I had a couple questions about manova modeling in R. I have calculated a manova model, and generated a summary.manova output using both the Wilks test and Pillai test. The output is essentially the same, except that the Wilks lambda = 1 - Pillai. Is this normal? (The output from both is appended below.) My other question is about the use of MANOVA. If I have one variable which has a

Hi all, I have received the following error from summary.manova: Error in summary.manova(manova.test, test = "Pillai") : residuals have rank 36 < 64 The data is simulated data for 64 variables. The design is a 2*2 factorial with 10 replicates per treatment. Looking at the code for summary.manova, the error involves a problem with qr(). Does anyone have a suggestion as to how to

Dear R-helpers, In a data set I got from a medical doctor there are six treatment groups and (about) 5 bivariate responses in each group. Using 'manova', it is easy to see significant differences in treatment effects, but the doctor is more interested in the correlation between the two responses (within groups). I'm willing to assume a common value over groups, and one way of

While I was helping a SAS-using friend with an analysis I noticed some differences in the multivariate test statistics, approximate F statistics, and p-values in the manova function using R and proc GLM using SAS. The univariate coefficients are identical. Is there a reason to expect R and SAS to give different results? Thanks, Bill Kristan.

Dear all, I need to do a Manova but I have an unbalanced design. I have morphological measurements similar to the iris dataset, but I don't have the same number of measurements for all species. Does anyone know a procedure to do Manova with this kind of input in R? Thank you very much, Naiara. -------------------------------------------- Naiara S. Pinto Ecology, Evolution and Behavior 1