similar to: Letters group Games-Howell post hoc in R

Hi all, Is there a R-function that orders Tukey results with conveniant letters, similar to the SPSS output (A, AB, ABC, C, etc.) . [[alternative HTML version deleted]]

Hi, I would like to ask how the paste(S1, S2, sep="") function internally works. Are the two stings copied to a new String? I have a program where successively strings are build up. First the program calls an external function and depending on the result it builds up strings to visualize the result. The external function is really fast, also for huge input data. But the

Hi list, I have a question regarding post-hoc extraction of data from a two-way categorical anova. I have a categorical anova of this form: width ~ steepness + patchiness (4 steepness levels, 4 patchiness levels) This simple setup answers if for the widths I collected across different levels of steepness and patchiness significant differences can be found. Is there a way to look at these

Hallo, we are trying to extend the R package multcompView in agreement with the author Hans-Peter Piepho. The function multcompLetters implements so far a heuristic. We would like to add a function that implements an exact algorithm and returns a provable optimum result. This algorithm has been implemented in Objective Caml and we would like to reuse this code. We wrote an R function

Dear colleagues, In checking a function I am adding to an R package, I get the following warning pair: ... Bad \usage lines found in documentation object 'nominal': "\\method{print}{nominal}"(x, max.print = 10, posthoc = "std.pearson.residuals.sign", assoc = ifelse("univariate" list(c("N", "alpha.X2",

Hi all, I apologize for what might be a silly question. I am interested in doing a one way anova. This is not too hard in and of itself, either with anova, aov or oneway.test . However, I need to 1) get pvalues, 2) do a posthoc analysis with Tukey HSD, 3) and have (sometimes) an unbalanced design. I just can't seem to put all the pieces together. Any suggestions? Thanks in advance, Dan.

dear all, I want to perform a posthoc test for my ANCOVA: a1<-aov(seeds~treatment*length) With summary(glht(a1, linfct = mcp(treatment = "Tukey"))) R tells me: "covariate interactions found -- please choose appropriate contrast" How do I build these contrasts? Ideally, I would like to have the posthoc test for the ANCOVA including a block-effect

Dear R experts, I'm beginner. My question about ANOVA for unequal sample sized data should be obsolete but I can not clarify it. I have a dataset from 23 males and 18 females. I measured one condition('cond') with 4 levels. So I'd like to see main effect of gender, cond and gender by cond interaction and also postHoc test. (In fact, I have to do anova 90 times) * 1. Question

Hello all, Quick question: I want to do posthoc contrasts for a linear mixed effects model. However, when trying to use the csimtest function in multcomp package I receive an error message saying it cannot find the function, even after installing and loading package multcomp. Any pointers would be greatly appreciated Daniel

I am interested in using "multcompLetters" after running "kruskalmc" but I'm a newbie and I'm not having luck figuring it out. I can run "kruskalmc" just fine, but after studying the documentation for "multcompletters" for a long time, I cannot figure out how to make it work. Any ideas? R input is below > >> #here is my data set >>

Hi, I am doing a retrospective analysis on a cohort from a designed trial, and I am fitting the model fit<-glmD(survived ~ Covariate*Therapy + confounder,myDat,X=TRUE, Y=TRUE, family=binomial()) My covariate has three levels ("A","B" and "C") and therapy has two (treated and control), confounder is a continuous variable. Also patients were randomized to

Dear R experts, I'm new in R and a beginner in terms of statistics. It should be simple question, but definitely difficult to solve it by myself. I'd like to see main effect of group(gender: sample size is different(M:F=23:18) and one of condition(cond) and the interaction at each subset from 90 datasets So I perform anova 90 times using a command like below; for(i in 1:90)

Dear useRs, I'm working on multiple comparison design on two factor (2 3 levels) ANOVA. Each of the tests I have tried (Tukey, multcomp package) seem to do only with one factor at a time. fm1 <- aov(breaks ~ wool * tension, data = warpbreaks) tHSD <- TukeyHSD(fm1, "tension", ordered = FALSE) $tension diff lwr upr p adj M-L -10.000000 -19.35342

Hi! I am quiet new with R and I have some problems to perform a posthoc test with an lme model. My model is the following: >lme1<-lme(eexp~meal+time, random=~1|id,na.action=na.omit) and then i try to get a post hoc test: >summary(glht(lme1,linfct=mcp(meal="Tukey))) but I get a warning message: Erreur dans as.vector(x, mode) : argument 'mode' incorrect Thank you for your

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

Hi, I use glht to make multcomp, using Tukey, from a glm model. It is possible to get a more simplified output of result? Somethink like ordering by letters. Thanks Ronaldo -- Human kind cannot bear very much reality. -- T. S. Eliot, "Four Quartets: Burnt Norton" -- > Prof. Ronaldo Reis J?nior | .''`. UNIMONTES/Depto. Biologia Geral/Lab. de Ecologia | : :' :

Dear R experts I am running a negative-binomial GLM (glm.nb) to test the null hypotheses that species 1 and 2 are equally abundant between site 1 and site2, and between each other. So, I have a 2x2 factorial design with factors Site (1,2) and Taxon (1,2). Since the Site:Taxon interaction is significant, I need to do the equivalent to a "post-hoc test" for ANOVA, however, the same tests

Dear all, I have a quite basic questions about anova analysis in R, sorry for this, but I have no clue how to explain this result. I have two datasets which are named: nmda123, nmda456. Each dataset has three samples which were measured three times. And I would like to compare means of them with Posthoc test using R, following please see the output: (CREB, mCREB and No virus are the name of

I have run the following logistic regression model: options(contrasts=c("contr.treatment", "contr.poly")) m <- glm(wolf.cross ~ null.cross + feature, family = "binomial") where: wolf.cross = likelihood of wolves crossing a linear feature null.cross = proportion of random paths that crossed a linear feature feature = CATEGORY of linear feature with 5 levels:

Hello. We have some questions concerning the statistical analysis of a dataset. We aim to compare the sample means of more than 2 independent samples; the sample sizes are unbalanced. The requirements of normality distribution and variance homogeneity were not met even after transforming the data. Thus we applied a nonparametric test: the Kruskal-Wallis-test (H-Test). The null hypothesis was