similar to: Tukey HSD

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

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 list members, I'm running some tests looking at differences between means for various levels of a factor, using Tukey's HSD method. I would like to plot the data as boxplots or dotplots, with horizontal significance lines indicating which groups are statistically significantly different, according to Tukey HSD. Here's a nice image showing an example of such a graphical

Hi, I need to determinate HSD value from a matrix like that Thesis Days A1 Cx 0 66.07 Cx 0 60.24 Cx 0 42.86 Tw 0 66.07 Tw 0 60.24 Tw 0 42.86 Aa 0 66.07 Aa 0 60.24 Aa 0 42.86 Qe 0 66.07 Qe 0 60.24 Qe 0 42.86 Cx 56 310.86 Cx 56 223.17 Cx 56 186.77 Tw 56 149.42 Tw 56 127.75 Tw 56 138.59 Aa 56 130.24 Aa 56 214.83 Aa 56 137.95 Qe 56 186.64 Qe 56 189.09 Qe 56 187.87 this is my script

Hi everyone, I am fairly new to R, and I am aware that others have had this problem before, but I have failed to solve the problem from previous replies I found in the archives. As this is such a standard procedure in psychological science, there must be an elegant solution to this...I think. I would much appreciate a solution that even I could understand... ;-) Now, I want to calculate a

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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, 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

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

Hi everyone I'm using Tukey HSD as post-hoc test following a lme analysis. I'm measuring hemicelluloses in different species treated with three different CO2 concentrations (l=low, m=medium, h=high). The whole experiment is a split-plot design and the Tukey-function from the package multcomp is suitable for lme-analysis with random factors. The analysis works fine but I get a non

Dear all, I am doing a one way between subjects anova in an unbalanced data set. Suppose we have "a" levels of the one factor. I want to merge the not so significantly different levels into the same cluster. Can I do a Tukey Kramer HSD and then use the following algorithm: For i in 2 : "a" For j in 1 : i-1 if mean of level i is not significantly different

Hi all!

Dear all, I would be glad if someone could tell me if Tukey HSD (function of stats library) accept the NA values and could correct the unbalanced design (different number of sample in each group) Thanks for help Alice Le Bars

Dear all Is there any way of doing a Tukey HSD post-hoc test after a Kruskal- Wallis or Friedman rank sum test (in the ctest package)? Thanks in advance, Albertus Dr. Albertus J. Smit Department of Botany University of Cape Town Private Bag Rondebosch 7700 South Africa Tel. +27 21 689 3032

Hi, I was wondering if you could help me: The following are the first few lines of my data set: subject group condition depvar s1 c ver 114.87 s1 c feet 114.87 s1 c body 114.87 s2 c ver 73.54 s2 c feet 64.32 s2 c body 61.39 s3 a ver 114.87 s3 a feet 97.21 s3 a body 103.31 etc. I entered the following ANOVA command: >dat <- read.table("mydata.txt", header=T)

dear all effect of a, b and c on d, total 48 comparisons, got one anova result in model1=aov(d~A*B*C). can we get all the result in one command? or can we interpret the whole comparisons from this result? how it work in Tukey HSD? jose ____________________________________________________________________________________ Access over 1 million songs.

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

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

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)

Hello, I am trying to do a manova test in r, and have used the "manova" function to test differences between two dependent variables. The results were significant for the whole model, but the sources I've read say that in order to do a post-hoc multiple comparison, I have to do separate aovs for each dependent variable, then call the TukeyHSD function. I have used the TukeyHSD with