similar to: more simplified output from glht object

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:

I suppose that multcomp in R and multicomp in S-Plus are related and it appears that it is possible to use multicomp with lme in S-Plus given the following correspondence on s-news sally.rodriguez at philips.com 12:57 p.m. 24/04/03 -0400 7 [S] LME summary and multicomp.default() Is it possible to use multicomp with lme in R and if so what is the syntax from a simple readily available

Dear R community, I performed a generalized linear mixed model using glmmPQL (MASS library) to analyse my data i.e : y is the response with a poisson distribution, t and Trait are the independent variables which are continuous and categorical (3 categories C, M and F) respectively, ind is the random variable. mydata<-glmmPQL(y~t+Trait,random=~1|ind,family=poisson,data=tab) Do you think it

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 >>

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

Dear r-helpers, I have a question about multiple testing. Here an example that puzzles me: All matrixes and contrast vectors are presented in treatment contrasts. 1. example: library(multcomp) n<-60; sigma<-20 # n = sample size per group # sigma standard deviation of the residuals cov1 <- matrix(c(3/4,-1/2,-1/2,-1/2,1,0,-1/2,0,1), nrow = 3, ncol=3, byrow=TRUE, dimnames =

Dear list, As a reply to my recent mail: > simint and TukeyHSD work for aov objects. > Can someone point me to similar functions for lme objects? Douglas Bates wrote There aren't multiple comparison methods for lme objects because it is not clear how to do multiple comparisons for these. I don't think the theory of multiple comparisons extends easily to lme models. One could

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 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

Dear R-help list, I would like to perform multiple comparisons for lme. Can you report to me if my way to is correct or not? Please, note that I am not nor a statistician nor a mathematician, so, some understandings are sometimes quite hard for me. According to the previous helps on the topic in R-help list May 2003 (please, see Torsten Hothorn advices) and books such as Venables &

Hello everybody, I use the sweetpotato database included in R package: data(sweetpotato) This dataset contains two variables: yield(continous variable) and virus(factor variable). Due to Levene test is significant I cannot assume homogeneity of variances and I apply Welch test in R instead of one-way ANOVA followed by Tukey posthoc. Nevertheless, the problems come from when I apply posthoc

This is only partly a question about R, as I am not quite sure about the underlying statistical theory either. I have fitted a non-linear mixed-effects model with nlme. In the fixed part of the model I have a factor with three levels as explanatory variable. I would like to use Tukey HSD or a similar test to test for differences between these three levels. I have two grouping factors:

Dear R-helpers: My question is how do I efficient and valid correct for multiple tests in a repeated measurement design: Suppose we measure at two distinct visits with repeated subjects a treatment difference on the same variable. The treatment differences are assessed with a mixed model and adjusted by two methods for multiple tests: # 1. Method: Adjustment with library(multcomp)

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

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 R-listers I'm a new R-user who needs some help with a test that I want to do. I have done a field experiment: four treatments (cont, x, y and xy) at three sites (A, B and C), the response is count data (0 - 15). I've done a Poisson regression: >glm(response~as.factor(treatment)*as.factor(site), family=quasipoisson, offset(max.response), data=dat) The "offset" is the

Dear Madam or Sir, I need to do multiple comparisons following nlme analysis (Compare the effects of different treatments on a response measured repeatedly over time; fixed = response ~ treat*time). On the web I found the notion that one might use the L argument from ANOVA. Do you have an example to show how this works together with nlme? Are there other ways to do a post-hoc analysis in

I just realized that in the call to `csimint' the argument `asympt=TRUE' is missing since we need to compute the confidence intervals for a glm based on the normal approximation. Torsten --------------------------------------------------------------------- library(multcomp) set.seed(290875) # a factor at three levels group <- factor(c(rep(1,10), rep(2, 10), rep(3,10))) # Williams

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the

New package `multcomp' for general multiple comparisons written by Frank Bretz, Torsten Hothorn and Peter Westfall We've uploaded the package `multcomp' to CRAN. The R package allows for multiple comparisons of k groups in general linear models. We use the unifying representations of multiple contrast tests, which include all common multiple comparison procedures, such as the