similar to: Tukey post hoc test and two way repeated measures ANOVA

Hi everyone, I woudl like to apply a Tukey post hoc after a repeated measure ANOVA. I followed the suggestions that I found in this help -list especially this one: /[R] Tukey HSD (or other post hoc tests) following repeated measures ANOVA You want to use lme() in package nlme, then glht() in the multcomp package. This will give you multiplicity adjusted p-values and confidence intervals.

I am trying to do Tukey HSD comparisons on a repeated measures expt. I found the following example on r-help and quoted approvingly elsewhere. It is broken. Can anyone please tell me how to get it to work? I am using R 2.4.1. > require(MASS) ## for oats data set > require(nlme) ## for lme() > require(multcomp) ## for multiple comparison stuff > Aov.mod <- aov(Y ~ N + V +

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

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

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 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

Hi, I am fairly new to R and still trying to figure out how it all works, and I have run into a few issues. I apologize in advance if my questions are a bit basic, I'm also no statistics wizard, so part of my problem my be a more fundamental lack of knowledge in the field. I have a dataset that looks something like this: Week Subj Group Readout 0 1 A 352.2 1 1 A

Thanks in advance for your help. I am running a repeated measures ANOVA in r. The same group undergoes to four different treatment conditions. So, all individuals are treated with treatments A, B, C and D in four different occasions. Once I get a significant ANOVA, I first run a paired samples t-test using the code: t.test(X1,X2,paired=TRUE) #being x1 the punctuation after treatment 1 and x2 the

Hello R experts, I have having a difficult time figuring out how to perform and interpret an ANCOVA of my nested experimental data and would love any suggestions that you might have. Here is the deal: 1) I have twelve tanks of fish (1-12), each with a bunch of fish in them 2) I have three treatments (1-3); 4 tanks per treatment. (each tank only has one treatment applied to it) 3) I sampled

Hi, I am performing experiments in the field of visual perception where we often apply balanced designs. Within a group of normal subjects, we vary different stimulus conditions (like contrast, luminance, temporal frequency of stimulation) and derive some psychophysical or electrophysiological results from our subjects. Often, the main question is to test the effect of these parameters

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 trying to figure out how to get R to analyze this experiment properly. I have a series of subjects each with two legs. Within each leg there are two bones that I am interested in. There are also two treatments that I am interested in. That results in four different combinations of treatments. Obviously, since the subjects only have two legs, they can’t receive each

Hi, I am new to R and I am trying to perform a post hoc tukey test using the multcomp package's ghlt for a lme model. My first attempt at doing so gave me an output, HOWEVER I have tried to do this again, it keeps coming up with the error: (Error in contrMat(table(mf[[nm]]), type = types[pm]) : less than two groups) My model is looking at effect of incubation temperature (3 groups) on

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

Dear all, Can anybody tell me how to export a 3d figure made with the plot3d function? I'm careless about whether it's still interactive or not in another format, as long I can get it out of R. Thanks! Alejandro Gonz?lez Departamento de Biodiversidad y Conservaci?n Real Jard?n Bot?nico Consejo Superior de Investigaciones Cient?ficas Claudio Moyano, 1 28014 Madrid, Spain Tel +0034

Hi, I have a fairly simple repeated measures-type data set I've been attempting to analyze using the lme function in the nlme package. Repeated searches here and other places lead me to believe I have specified my model correctly. However, I am having trouble with post-hoc tests. From what I gather, other people are successfully using the glht function from the multcomp package to

Hi R users, I sent a message yesterday about NA in model estimates ( http://r.789695.n4.nabble.com/How-set-lm-to-don-t-return-NA-in-summary-td3722587.html). If I use aov() instead of lm() I get no NA in model estimates and I use gmodels::estimable() without problems. Ok! Now I'm performing a lot of contrasts and I need correcting for multiplicity. So, I can use multcomp::glht() for this.

Oats[Oats$Variety %in% c("Victory", "Golden Rain"),] or subset(Oats, Variety %in% c("Victory", "Golden Rain")) Stefano -----Messaggio originale----- Da: r-help-bounces at stat.math.ethz.ch [mailto:r-help-bounces at stat.math.ethz.ch]Per conto di elyakhlifi mustapha Inviato: luned? 23 aprile 2007 9.56 A: R-help at stat.math.ethz.ch Oggetto: [R] extract from

Greetings, In the following one-way ANOVA I am attempting to calculate the means of each treatment along with their 95% Tukey confidence intervals for the data shown below using a routine from the HH package. library(HH) options(digits=10) # load data treat voltage 1 130 1 74 1 155 1 180 2 150 2 159 2 188 2 126 3 138 3 168 3 110 3 160 4 34

Dear R users, this is a follow up of this message http://tolstoy.newcastle.edu.au/R/e6/help/09/05/13897.html I'm reproducing the core of it for convenience. > // > / data(Oats, package = "MEMSS") / > / tp1.oats <- xyplot(yield ~ nitro | Variety + Block, / > / data = Oats, / > / panel = function(x, y, subscripts, ...) { /