similar to: manova/tukey test

Hi all, This is both a general and r specific question. I am analyzing some morphological data and have performed a MANOVA on the 5 groups with 5 measured morphological traits. The results provide me with some pairwise comparisons, but I would like to run a multiple comparison test on the results so that I could look at all pairwise comparisons, but have been unable to find a way to do it. I

I am having what I think is a strange problem with applying TukeyHSD to an aov fit with error strata. TukeyHSD is supposed to take "A fitted model object, usually an 'aov' fit." aov (with error strata) is supposed to generate an object of type aovlist, which is a list of objects of type aov. But I can't seem to feed components of my aovlist to TukeyHSD. I guess I

Hello, I am trying to determine the most appropriate way to run post-hoc comparisons on my lme model. I had originally planned to use Tukey HSD method as I am interested in all possible comparisons between my treatment levels. TukeyHSD, however, does not work with lme. The only other code that I was able to find, and which also seems to be widely used, is glht specified with Tukey:

Hi, I'm trying to make sense of the options for multiple comparisons options in R. I've found the following options: pairwise.t.test, which provides standard t-tests, with options for choosing an appropriate correction for multiple comparisons TukeyHSD, which provides the usual Tukey test glht(package multcomp), which provides a variety of options >From the help list, it appears

Hey Folks: New to R and learning as I go, but really, I know just enough to get myself into trouble. I've waded through everything up till now, and don't see anything in the search that is directly helpful for the POS that I've created. The GOAL: All I want in the world is a program that performs 1-way ANOVA's on every column in a data set (taking the first column as the

Hello, I am a little out of date and am still using S-Plus instead of R. I haven't been able to find the right place to ask this question, so I thought I would ask it here and hope that someone can help. I am unable to located the TukeyHSD() function in S-Plus. I have been able to use the GUI to run Tukey's test, but the results don't provide a p-value. Does anyone know how I can

Hi there, ============================= x=c(145,40,40,120,180, 140,155,90,160,95, 195,150,205,110,160, 45,40,195,65,145, 195,230,115,235,225, 120,55,50,80,45 ) y2=c( rep(as.character(1),5), rep(as.character(2),5), rep(as.character(3),5), rep(as.character(4),5), rep(as.character(5),5), rep(as.character(6),5) ) crd2=data.frame(x,y2) model1=aov(x~y2,data=crd2) TukeyHSD(model1)

I'm using SAS for my statistical purpose and I'm triyng to jump in the R' world. How can i implement Tukey linear test in R? In sas-code my lines are : PROC GLM; BY PARCELLA; CLASS TESI; MODEL PROD=TESI; MEANS TESI/TUKEY lines; IN R: ab<-type.convert(a$tesi) a<-cbind(a,ab) cc<-anova(lm(prod~ab,data=a)) cc2<-aov(prod~ab,data=a) tt<-TukeyHSD(cc2,ordered=TRUE) but how can

Dear R community, I compare mean monthly body temperature between two age classes of turtles overwintering underground. lm(body_tem ~ Month*Year*Age_Class) TukeyHSD(aov(body_tem ~ Month*Year*Age_Class, a)) The Tukey HSD as well as the planned contrasts method showed significant differences between the two age classes, but insignificant differences between the two age classes at the same

Hello, I am really new to R and it's still a challenge to me. Currently I'm working on my Master's Thesis. My supervisor works with SAS and is not familiar with R at all. I want to run an Anova, a tukey-test and as a result I want to have the tukey-grouping ( something like A - AB - B) I came across the HSD.test in the agricolae-package, but... unfortunately I do not get an output

Hallo, zur Analyse von Daten zum Artenreichtum von Pflanzen, habe ich ein Glm (glm) und anschlie?end eine Anova (anova) durchgef??hrt. Nun m??chte ich f??r die signifikanten Einflussfaktoren einen Post Hoc Tukey Test durchf??hren, um zu ermitteln in wie weit die einzelnen Faktorstufen sich signifikant voneinander unterscheiden. Mit dem Befehl (TukeyHSD) komme ich nicht

Dear All, I am trying to perform MANOVA. I have table with 504 columns(species) and 36 rows) with two grouping (season and location) Zx <- Z[c(4:504)] Zxm <- as.matrix(Z) m<- manova(Zxm~Season*location, data=Z) when I do summary.aov, I get respond for each species but summary.manova summary.manova(m) :" residuals have rank" 24<501. What can it be the reason for this error

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

I'm new using R im trying to do a tukey test, but when i see the results the p value results in NA im guessing its because i have missing values but im not sure how to fix it AnovaModel.2 <- aov(area ~ trat, data=apilados) > summary(AnovaModel.2) Df Sum Sq Mean Sq F value Pr(>F) trat 4 11847 2961.76 9.9905 1.500e-06 *** Residuals 76 22531 296.46

Dear colleagues, I had a question wrt the car package. How do I evaluate whether a simpler multivariate regression model is adequate? For instance, I do the following: ami <- read.table(file = "http://www.public.iastate.edu/~maitra/stat501/datasets/amitriptyline.dat", col.names=c("TCAD", "drug", "gender", "antidepressant","PR",

Hello all, I have a question on manova in R: I'm using the function "manova()" from the stats package. Is there anything like a stepwise (backward or forward) manova in R (like there is for regression and anova). When I enter: step(Model1, data=Mydata) R returns the message: Error in drop1.mlm(fit, scope$drop, scale = scale, trace = trace, k = k, : no 'drop1'

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 all; working with a 'fat' data set (700 variables / 50 samples) and trying to run a manova test on it (I'm aware that it's not the best option for this kind of data set) I got the error in the summary.manova function about the rank of the residuals (rank < # variables). Ok. The thing that I don't understand is why I don't get the same type of error in SAS. There