similar to: More basic equivalent of TukeyHSD

I have insect data from twelve sites and like most environmental data it is non-normal mostly. I would like to preform an anova and a means seperation like tukey's HSD in a nonparametric sense (on some sort of central tendency measure - median?). I am searching around at this time on the internet. Any suggestions, books, etc. would be greatly appreciated. -- Stephen Sefick Research

Hi, The problem is that I would like to do an interaction plot with intervals based on Tukey's honestly significant difference (HSD) procedure, but I do not know how to do it in R. I have 3 factors "A", "B" and "C" and a response variable "response". I would like to study a model where there are main effects and second order interaction effects.

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 <-

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

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:

Dear R gurus, I have the following model: appcov.aov <- aov(yield ~ prevyield + trt + block) where prevyield is a continuous numeric covariate and trt and block are factors (yes, I did factor()!) Now, when I do a TukeyHSD, my diff's are all screwed up! For instance: treatment mean for treatmen "E" is 277.25 and for treatment "O" is 279.5, so I figure the diff O-E

Dear R users I have a big matrix like 6021 1188 790 290 1174 1015 1990 6613 6288 100714 6021 1 0.658 0.688 0.474 0.262 0.163 0.137 0.32 0.252 0.206 1188 0.658 1 0.917 0.245 0.331 0.122 0.148 0.194 0.168 0.171 790 0.688 0.917 1 0.243 0.31 0.122 0.15 0.19 0.171 0.174 290 0.474

Hi, In my analysis of impacts of insecticide-treated bednets on malaria, I look at the relationship between malaria incidence and mosquito behaviors. The condensed data set is copied here. Ordinary regression (lm) shows that Incidence was negatively related to Mortality. This makes sense because the latter reflected the strength of killing mosquitoes by insecticide-treated nets. Since the

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

We have several people in our church that recently became disabled. I am thinking of setting up an asterisk server and several phone lined so that they can call in to church during services to listen to the service. The phone lines at church are also used by our private school during the week. So I am thinking that I need to setup a schedule so that the lines used for the service will

Dear list, I have very little experience in dealing with proportions, i am sure this is a very simple question but i could find no suitable answer beyond doing a chi-sq test and then using the Marascuilo procedure as a post-hoc analysis. I am simply wanting to know if the proportions ( i.e the number of Yes / No) significantly differ between the cases and if so which cases are significantly

hi list, i have to ask you again, having tried and searched for several days... i want to do a TukeyHSD after an Anova, and want to get the adjusted p-values after the Tukey Correction. i found the p.adjust function, but it can only correct for "holm", "hochberg", bonferroni", but not "Tukey". Is it not possbile to get adjusted p-values after

Hi There, I''m trying to user fields_for inside a form_tag, but i can''t catch it on my controller. I just take some html code off to makes it easy to read my view... i''m using campuses_path(current_church, @campus) instead form_for @campus, because my route is like resources :campuses, :path => ":church/campuses" <%= form_tag

I've a small system that I use to support a number of churches. I provide web and email for them. My current server is running CentOS 6.3 with paired 1TB drives in a RAID1 configuration. It works well. One filesystem is very large, >500GB, and contains numerous large files: SQL, docs, church libraries in ebook and digital form, plus stored videos of church services. My problem is that

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 R-people, I am wanting to run Factorial ANOVA followed by Scheffe tests on some spatial subjective data. I'm comparing X-Y independent coordinates against x-y dependent coordinates. There are only four independent spatial coordinates that form a square. I am wondering whether I am doing the right thing, because there doesn't seem to be a simple way of doing this. I have attempted to

I would like to write a function that computes Tukey's 1 df for nonadditivity. Here is a simplified version of the function I'd like to write: (m is an object created by lm): tukey.test <- function(m) { m1 <- update(m, ~.+I(predict(m)^2)) summary(m1)$coef } The t-test for the added variable is Tukey's test. This won't work: data(BOD) m1 <- lm(demand~Time,BOD)

Hi everyone, I am new to this forum and also new to statistics and I would appreciated it if someone would take some time to answer my question. I am analyzing companies in regard to their leverage. I categorized the companies into 3 groups: small, mid and large. For the group small, I have 55 debt multiples, for mid 42 and for large 72. (Unfortunately I can not provide my data because it is

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.