similar to: Post-hoc test on ANCOVA

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

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

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

I'm trying to do an ANCOVA with two factors (clipping treatment with two levels, and plot with 4 levels) and a covariate (stem diameter). The response variable is fruit number. The minimal adequate model looks like this: model3<-lm(fruit~clip + plot + st.dia + clip:plot) I'd like to get some multiple comparisons like the ones from TukeyHSD, but TukeyHSD doesn't work with the

Hi there, The following data is obtained from a long-term experiments. > mydata <- read.table(textConnection(" + y year Trt + 9.37 1993 A + 8.21 1995 A + 8.11 1999 A + 7.22 2007 A + 7.81 2010 A + 10.85 1993 B + 12.83 1995 B + 13.21 1999 B + 13.70 2007 B + 15.15 2010 B + 5.69 1993 C + 5.76 1995 C + 6.39 1999

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

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.

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:

I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the covariate (continous variable) I get this results > ancova<-aov(log(peso)~edadysexo*log(lcc)) > summary(ancova) Df Sum Sq Mean Sq F value Pr(>F) edadysexo 2 31.859 15.9294 803.9843 <2e-16 *** log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***

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

I have done ancova with categorical and continuous predictor variables. The categorical predictor variable shows significant effect on the dependent variable. I would like to do a post-hoc test to see which groups in the categorical variable differ. I have explored Tukey test in multcomp package. My study is similar to the "litter data". In the code it's mentioned that the contrast

Hello dear R-help mailing list, A friend of mine teaches a regression and experimental design course and asked me the following question. She is trying to find a way to display the "homogeneous groups" (after performing tukey test on an aov object). here's an example for what she means by "homogeneous groups": She did one way anova and got these results for tukey test:

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

Dear R Users, Recently I began to use R. I`m interested about comparing several regression curves. REcently I found the package sm and the function sm.ancova which I understand allows me to do this. I applied this function to my data (seven regression curves) and I found that there are significant differences. Nevertheless, when I want to find out where are the differences, the package tells that

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 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-

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

Hi! as my subject says I am struggling with the different of a two-way ANOVA and a (two-way) ANCOVA. I found the following examples from this webpage: http://www.statmethods.net/stats/anova.html # One Way Anova (Completely Randomized Design) fit <- aov(y ~ A, data=mydataframe) # Randomized Block Design (B is the blocking factor) fit <- aov(y ~ A + B, data=mydataframe) # Two Way

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

Hi, I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous covariate (e.g., AGE). I did for example: library(multcomp) data('litter', package = 'multcomp') litter.aov <- aov(weight ~ gesttime + dose, data = litter) TukeyHSD(litter.aov, which =