similar to: Tukey Type III SS vs Type I SS

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 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

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

Please help. I really like R and I have been looking at how to do LSD multiple comparison test with data that has more than one factor. So far, I am unsuccessful. Please help! Me

Dear Colleagues, I fit this model: mod1 <- lmer(x~category*comp+(1|id),data=impchiefsrm) where category has 4 levels and comp has 8 levels. These work: glht(mod1, linfct=mcp(category="Tukey") glht(mod1, linfct=mcp(comp="Tukey") What I'd like is (conceptually): glht(mod1, linfct=mcp(category:comp="Tukey") but it gives a syntax error. Any help is

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

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

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

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 +

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

hey, how can i extract the values from the CI's when i use following code for a tukey test? the output shows three CI's and i know it should work with 'names but i don't really know how... thanks library(multcomp) data1$soort<-as.factor(data1$soort) amod<-aov(waarde~soort,data=data1) g<-glht(amod, linfct=mcp(soort = "Tukey")) confint(g) -- View this message in

Hello, I have some conflicting output from an aov summary and tukey contrasts with a mixed effects model I was hoping someone could clarify. I am comparing the abundance of a species across three willow stand types. Since I have 2 or 3 sites within a habitat I have included site as a random effect in the lme model. My confusion is that the F test given by aov(model) indicates there is no

Hello, I am performing a BACI analysis with ANOVA using the following glm: fit1<-glm(log(Cucs_m+1)~(BA*Otter)+BA+Otter+ID+Primary, data=b1) The summary(aov(fit1)) shows significance in the interaction; however, now I would like to determine what combinations of BA and Otter are significantly different (each factor has two levels). ID and PRIMARY substrates are categorical and included in

I am trying to decipher, via post hoc test (Tukey), which of my sites differ from eachother. I have 4 sites, 2 sets of In vs Out (MPA) in separate Regions. Therefore my Mixed Effects Model code has 2 fixed effects: CB.lme <- lme(AsinCB~ In_Out*Region, random = (~1| site.trans/Quadrat) , data = Subsampled_props, control = lmeControl(maxIter = 500, msMaxIter = 500, msMaxEval = 500))

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 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

Hi, I have a few questions about glht() and the interpretation of output from Tukey's in multcomp package for lme() model. The main issue is that I noticed that a plot that I produced with code letters seem to contradict the graph itself. I provide data and code below. I end with my questions. A few things about data set. "LMA.vcp" is continuous response variable.

Dear R-group, I am trying desperately to get this Tukey test working. Its my first time here so I hope my question is not too stupid, but I couldn't find anything helpful in the help or in the forum. I am analysing a dataset of treated grasses. I want to find out, if the grasses from different Provenances react differently. In the aov test I found a significance for the combination Treatment

Hi R Forum I am a newbie to R and I have been amazed by what I can get my team to accomplish just by implementing Scripting routines of R in all my team's areas of interest.. Recently i have been trying to adopt R scripting routine for some analysis with longitudanal data.. I am presenting my R script below that I have tried to make to automate data analysis for longitudanal data by employing