similar to: lm() and post hoc test

Let's assume that according to Anova(lm(y~a*b, data=d)) the a:b interaction is significant, and I would like to know if there are specific combinations of a and b levels that differ from the control group. Are there any caveats against simply looking at the p-values in the output generated by summary(lm(y~a*b,data=d))? The documentation for summary.lm says nothing about multiple comparison

Dear R-users, I need to produce a histogram where for every breaks there are the mean of the data. I tried tu use the function >hist(x, break=20 ... ) but this return the numerosity for every breaks, not the mean. Any hint? Thanks in advance, francesco --------------------------------------- Francesco Nutini CNR-IREA Ist. per il Rilevamento Elettromagnetico dell'Ambiente Via

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

Dear [R] Users, I have implemented a linear model with this syntax: model<- lm (var_dependent ~ var_indipendent + factor + var_indipendent : factor, dataframe) anova (model) Response: var_dependent Df Sum Sq Mean Sq F value Pr(>F) var_indipendent 1 20.5522 20.5522 87.8701 1.167e-14 *** factor 1 0.1060

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-

Hi Folks, I have been using the TukeyHSD to conduct post hoc comparisons. The challenge is I have two interacting factors, one of which has 6 levels (cover types), and the other 2 (study areas). As a result interpreting the post hoc comparisons is difficult when you get a list of every possible combination (66 of them). In grad school I was using SAS and I recall most of the post hoc tests

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

I'm using lm to run an ANOVA, and would like to use Ryan's Q as my post-hoc (as recommended by Day and Quinn, 1989, Ecological Monographs). I can't seem to find any methods in the base stats package that implement this post-hoc. Is there a good package of post-hoc methods out there, or has someone written a method for Ryan's Q previously? Thanks! -Jarrett

Hi * i've done my anova anlysis but now i need post-hoc test, are these included in R ? I've a Big problem, working with people that don't like to use command-line software (but prefer something like openoffice) does someone is trying to do a usable GUI for R ? i'm reading something on R commander SciView and others but all seem to be beta. I'd like to make possible to make

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.

Dear all, I have a serious problem in applying 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

Does anyone have any suggestions for how to test for violation of the sphericity assumption and apply the Huynh-Feldt correction to a repeated-measure model? My model follows the format below: Rep.anova=aov(Y~X1*X2+Error(Subject/(X1*X2),data=DataFile)) I've already looked at the explanations aov() and Error() in Baron's “Notes on the use of R for psychology experiments 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

Dear R users, I'm using xyplot to obtain a graph about a correlation x~y for different years (a categoric variable). The program dispose automatically the graphs in this way: 2010 2007 2008 2009 2004 2005 2006 2001 2002 2003 1998 1999 2000 Which code should I use to obtain this sequance of graphs? 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 I have tried with

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

Let's say I've run Anova(lm(y~a*b)) and found the a:b interaction to be significant. Now I'm interested in which specific level combinations of a and b significantly differ from the control group. Can I use the t-tests from summary(lm(y~a*b)) to answer that question? I saw no mention of multiple comparison in the documentation for summary.lm, so am I right in assuming I need to

Dear Colleagues, I have scoured the help files and been unable to find an answer to my question. Please forgive me if I have missed something obvious. I have run the following two models, where "category" has 3 levels and "comp" has 8 levels: mod1 <- lmer(x~category+comp+(1|id),data=impchiefsrm) mod2 <- lmer(x~category+comp+category*comp+(1|id),data=impchiefsrm)

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 R-users, I have to plot two xyplot, and I wish to enclose this two graphs with just one headline, the same x scale, the same grid etc. These parameters should tie in, in order to obtain, visually, a unique graph formed by two xyplot. I try to give an idea: xyplot1: |_|_|_| xyplot2: |_|_|_| what i want: | | | | |_|_|_| I tried to use the command

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