similar to: csimtest function in multcomp package

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:

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 &

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

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

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

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

I am trying to use multcomp to do a Tukey posthoc on growth increments among genetic crosstypes. #Fixed effect model m1 <- lm(inc ~ 0 + Age+ Crosstype + Sex, data = Data.age) summary(m1) RESULTS of the model: summary(m1) Call: lm(formula = inc ~ 0 + Age + Crosstype + Sex, data = Data.age) Residuals: Min 1Q Median 3Q Max -0.87180 -0.34002 -0.02702 0.27710 2.17820

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

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 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

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

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

Hi group, I'm trying to do a Tukey test to compare the means of a factor ("treatment") with three levels in an lme model that also contains the factors "site" and "time": model = response ~ treatment * (site + time) When I enter this model in csimtest, it takes all but the main factor "treatment" as covariables, not as factors (see below). Is it

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

Dear R-users, I'm currently using the multcomp package to produce plots of means with 95% confidence intervals i.e. mult<-glht(lm(response~treatment, data=statdata), linfct=mcp(treatment="Means")) plot(confint(mult,calpha = sig)) Unfortunately the y-axis on the plot appears to be fixed and hence if the labels on the y-axis (treatment levels) are too long, then they are not

Hola, Lo primero muchas gracias a todos por vuesta habitual ayuda. Llevo meses con las funciones lmer, pero tengo un problema no se como plantear unas comparaciones multiples una vez demostrado que el factor tiene diferencias significativas. Tengo multiples opciones, Solución A: una es hacer un bonferroni a saco (pero eso me chirria ya que tego muchos datos) Solución B: Otra es utilizar glht,

Hi folks, I am working with repeated measures data and I ran into issues where the paired t-test results did not match those obtained by employing glht() contrasts on a lme model. While the lme model itself appears to be fine, there seems to be some discrepancy with using glht() on the lme model (unless I am missing something here). I was wondering if someone could help identify the issue. On

Hello, I'm a totally newbie to R and I'm taking a class using S+. In the class we use the multcomp command which takes a aov object and calculates confidence intervals for all pairwise differences by the Fisher least significant differences method. How can I do this in R. Thank you for taking the time with such a basic question. I've been looking on the net for a few days and I

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