similar to: TukeyHSD for multiple response

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

Dear Help List, Thanks in advance for reading...I hope my questions are not too ignorant. I have an experiment looking at evolution of wing size [centroid] in fruitflies and the effect of 6 different experimental treatments [treatment]. I have five replicate populations [replic] in each treatment and have reared the flies in two different temperatures [cond] to assay the wing size, making

Hi, I am doing canonical discriminant analysis using candisc function from the candisc package. My input is a table of species distribution (columns = abundance of each species in each sample) in samples that are split by categories (rows), and I want to know whether each category is associated with a particular set of species and their abundances. I have 20 rows (samples) split into 6

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

Hello everybody After doing a MANOVA on a bunch of data, I want to be able to make some comment on the amount of variation in the data that is explained by the factor of interest. I want to say this in the following way: XX% of the data is explained by A. I can acheive something like what I want by doing the following: X <- structure(c(9, 6, 9, 3, 2, 7), .Dim = as.integer(c(3,

Hello R People: Here's the Saturday night goofy question. I would like to see the code for TukeyHSD function and I tried the following: > getAnywhere("TukeyHSD") A single object matching ?TukeyHSD? was found It was found in the following places package:stats namespace:stats with value function (x, which, ordered = FALSE, conf.level = 0.95, ...)

hi all. I have a matrix of data with 5 different groups and 20 individual response per group, and about 12 variables collected for each. I want to represent the result in a 2D plot. PCA is not so good because the difference between the groups is not obvious. I have seen, in a recent paper, people doing a MANOVA and representing it in a centroid plot (they used Matlab to do it). I would like

I can prove I've done this before, but I recently installed Rexcel (and it was easiest to reinstall R and some other bits to make it work) and now its no longer working. Before I would do an ANOVA and a tukey post-hoc like this: >data1.aov=aov(result~factor1*factor2, data=data1) then... >TukeyHSD(summary(data1.aov)) and it would give me a nice tukey table of all the pairwise

I would like to do a one way anova and then a tukeyHSD. I have three vectors A,B and C. In a previous help message, I was told to do the following for the anova: y = c(A,B,C) group = factor(rep(a:3,c(7,9,13))) #provided there a 7 elements in A,9 in B and 13 in C and then anova(lm(y~group)) Looking at the tukeyHSD method it looks like it wants the aov method which I don't understand.

I've tried to run a Tukey post-hoc but keep getting this weird error, whether the aov was significant or not. treat_code is a dummy variable, but that shouldn't matter. Any suggestions? Thanks Amy > summary(aov(EtoH~treat_code, mydata)) Df Sum Sq Mean Sq F value Pr(>F) treat_code 1 16.44 16.44 11.027 0.001014 ** Residuals 287 427.91 1.49 --- Signif.

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:

Hello everyone, So I ran an anova with aov and then I want to run post-hoc comparisons but keep receiving this message : > no applicable method for "TukeyHSD" Here is my code: > d<-read.table("d.txt") > d > Obs subj Hand Gaze RT > 1 1 s1 1 1 401.4 > 2 2 s2 1 1 363.3...... > summary(ano <-

Dear R-Helpers, I was calling the TukeyHSD function and not getting confidence intervals or p-values. It turns out this was caused by missing data and the fact that I had previously turned on R Commander (Rcmdr). John Fox knew that Rcmdr sets na.action to na.exclude, which causes the problem. If you have this problem, you can either exit Rcmdr before calling TukeyHSD or you can set na.action to

Why do I see this error? > library(stats) > require(stats) [1] TRUE > > tHSD <- TukeyHSD(aov) Error in TukeyHSD(aov) : no applicable method for "TukeyHSD" In case it helps: > aov Call: aov(formula = roi ~ (Cue * Hemisphere) + Error(Subject/(Cue * Hemisphere)), data = roiDataframe) Grand Mean: 8.195069 Stratum 1: Subject Terms: Residuals Sum

Dear r-help members. I would like to compare species numbers of moths between eight different forests (each sampled for six nights). I would like to do a nested anova to compare species numbers between forests and nights. For more site specific details I wanted to do a Tukey test (TukeyHSD). Unfortunately this test is not working (message: "nicht anwendbare Methode f?r 'TukeyHSD' auf

Hello, When plotting the results of a TukeyHSD multiple comparisons procedure with an ANOVA (lm) object, an extra line appears in the confidence intervals that contain 0. For example (this is straight from the TukeyHSD helpfile): > summary(fm1 <- aov(breaks ~ wool + tension, data = warpbreaks)) > TukeyHSD(fm1, "tension", ordered = TRUE) > plot(TukeyHSD(fm1,

pairwise.t.test is returning NAs when one of the samples only has one entry, while TukeyHSD returns results (maybe not trustworthy or believable, but results). I stumbled on this because I did not realize one of my samples only had one entry while most of the others had several hundred, so I realize this is not a desirable situation. I'm really just curious about the difference between how

Hello R Developers, Dear R-developers, I ran some standard tests with currently (today morning) compiled R release candidate in Linux R 2.12.1 RC (2010-12-13 r53843). Some of these tests used plot.TukeyHSD function. This worked OK on the screen (X11 device), but PostScript file could not be rendered. The following example had the problem with me: postscript(file="tukeyplot.ps")

I am a new R user but a long time SAS user. I searched for a response to this question but no luck, so forgive me if this topic has been covered before. I am running a TukeyHSD post hoc test after running an ANOVA. I get the results of all pairwise comparisons, no problem. However, the output table is a little "busy", and I'd like to make the output easier to read. Specifically, I

Dear R people, I have a couple of questions about post-doc analyses for 2 by 2 within subjects ANOVA. I conducted a psycholinguistic study that combined a 2 by 2 design and a latin square design. Specifically, I had 32 items each of which generated 4 conditions. Participants saw each of the 32 items only once: 8 in Condition A, 8 in B, 8 in C, and 8 in D. The table below serves as an example.