similar to: Post hoc comparissons

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 was trying to figure out how to do post-hoc tests for Two Way ANOVAs and found the following 2 approaches: a. Do pairwise t-tests (bonferroni corrected) if one finds significance with the ANOVA. Link- http://rtutorialseries.blogspot.com/2011/01/r-tutorial-series-two-way-anova-with.html b. Do TukeyHSD on an aov model Link-

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.

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

Hello all R-helpers, I've performed an experiment to test for differential effects of elevated temperatures on three different groups of corals. I'm currently performing a cox proportional hazards regression with censoring on the survivorship (days to mortality) of each individual in the experiment with two factors: Temperature Treatment (2 levels: ambient and elevated) and

Is there code in R to automatically assign letters to different groups that are found to be statistically significant using a post-hoc test following an ANOVA? For example, let's say I found that relative rates of tree growth were statistically significant between tree species using an ANOVA and then I ran a TukeyHSD post-hoc test to determine which species were statistically

Hi, is there a specific/appropriate function/package to perform post hoc tests when running a two way repeated measures anova? I'm looking for something that will be equivalent to the 'TukeyHSD()' for between subjects anova (with 'aov()'). For one way repeated measures anova, the 'pairwise.t.test()' function seems to work correctly but the results are questionable for

Hi, I have searched and found a response to a question similar to mine but when I tried the code, R says it's not an actual function so I thought I'd ask here. http://r.789695.n4.nabble.com/file/n4633035/Cookies.csv Cookies.csv I have attached the data I am using. I am trying to look at two things: how moisture content changes over time, and how it changes along the length of a log

Hi R-helpers, TukeyHSD() works for models fitted with aov(), but could anyone point me to a function that performs a similar post hoc test for models fitted with lm() or glm()? Thanks in advance, Mark

Post-hoc test for a zero inflated continuous data set with a tweedie distribution? I have a zero inflated continuous data set of aphid feeding duration on 10+ species of plant. I have fitted a glm model with a tweedie distribution and used anova() function to show that there is significance between the plant species. However, I would now like to perform of post-hoc test, ideally a Tukey-Kramer

Hello, Despite my search, I didn't find a post-hoc test for an ANCOVA. I used the functions aov() and lm() to run the ANCOVA then I tried TukeyHSD() but it didn't work (because of the covariable is a continuous variable?). Furthermore, I would like to plot the adjusted values (i.e. the values of the tested variable taking into account the covariable). Thanks for your help! N. Poulet

Dear R users, I am conducting multiple comparisons among 12 groups (after a significant F-test) that are heteroscedastic (as judged by a significant Levene's test). It seems from the literature that the Games-Howell post-hoc test is the most appropriate for these data - but, I can't seem to locate a function on the R-site or CRAN or the help lists that implements this test in any R

Is there a standardized way in R to perform post-hoc comparisons for main adn interaction effects of between- and within-subject factors? For instance, I have a data set of performance of adults of different age groups (20-30, 60-70,70-80) performing a WM task (n-back, with n=1,2,3,4) in two different conditions (while sitting or walking). The corresponding ANOVA produces the following output

Greetings all, I've done some ANOVAs and have found significant effects across my groups, so now I have to do some post-hoc tests to ascertain which of the groups is driving the significant effect. Usually one would do something like a Newman-Keuls or Scheffe test to get at this but based on googling and browsing through the r-help archives R doesn't support these and they seem to be

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

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

Dear list, I?m wondering if there is something analogous to the TukeyHSD function that could be used for parametric terms in a GAM. I?m using the mgcv package to fit models that have some continuous predictors (modeled as smooth terms) and a single categorical predictor. I would like to do post hoc test on the categorical predictor in the models where it is significant. Any suggestions?

Hi, I found a problem dealing with repeated measures anova post hoc. I'm desperate in finding how to use it in R. The problem is that an Error term in aov() creates class of aovlist and that's why I cannot use TukeyHSD() which needs aov class. I really appreciate your help. Migle

Dear R users, I posted a couple of questions and got no response, so I am giving it another shot. I ran an experiment with a TWO-WAY within subject design. A sample dataset is in http://www-scf.usc.edu/~hex/data.txt I already ran ANOVA by using the following formula: aov(RT~Factor1*Factor2 + Error(Subject/(Factor1*Factor2)), data=data) and I obtained the following information: -------------

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