similar to: broken example: lme() + multcomp() Tukey on repeated measures design

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

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 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

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

I am doing an analysis and would like to use lme() and the multcomp package to do multiple comparisons. My design is a within subjects design with three crossed fixed factors (every participant sees every combination of three fixed factors A,B,C). Of course, I can use aov() to analyze this with an error term (leaving out the obvious bits): y ~ A*B*C+Error(Subject/(A*B*C)) I'd also like

Dear all, Can anybody tell me how to export a 3d figure made with the plot3d function? I'm careless about whether it's still interactive or not in another format, as long I can get it out of R. Thanks! Alejandro Gonz?lez Departamento de Biodiversidad y Conservaci?n Real Jard?n Bot?nico Consejo Superior de Investigaciones Cient?ficas Claudio Moyano, 1 28014 Madrid, Spain Tel +0034

Dear *, I would like to obtain for each factor of my anova model the "response variable vs factor" plot with means and 95% Tukey HSD intervals. I would appreciate any information on how to do that. Cheers -------------------------------------------------------------------- Max MANFRIN Tel.: +32 (0)2 650 3168 IRIDIA - CoDE, CP 194/6

Hi, I am trying to test some contrasts, using glht() in multcomp package on fixed effects in a linear mixed model fitted with lme() in nlme package. The command I used is: ## a simple randomized block design, ## type is fixed effect ## batch is random effect ## model with interaction dat.lme<-lme(info.index~type, random=~1|batch/type, data=dat) glht(dat.lme, linfct = mcp(type

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:

Can anyone help me? I''m having problems with the following code where I want to test the null hypothesis that regression slopes are the same among regressions. Here''s the code I''ve written with comments that include the final error I get. ... initial.dir <- getwd() library(systemfit) library(multcomp) basdata <- read.table("data_into7_test.txt",

I am running a linear model with achiev as the outcome and major as my iv (5 levels). The lm statement runs fine, but for the glht command I get the following error. I noted that someone else asked the same question a while back but received no reply. I am hoping someone might know what is happening. anovaf2<-lm(achiev ~ major, data=data_mcp) > pairwise<- glht(anovaf2,linfct =

Dear all, I would be glad if someone could tell me if Tukey HSD (function of stats library) accept the NA values and could correct the unbalanced design (different number of sample in each group) Thanks for help Alice Le Bars

Hi All, I am trying use 'multcomp' for multiple comparisons after my ANOVA analysis. I have used the following code to do ANOVA: dat <- matrix(rnorm(45), nrow=5, ncol=9) f <- gl(3,3,9, label=c("C", "Tl", "T2")) aof <- function(x) { m <- data.frame(f, x); aov(x ~ f, m) } amod <- apply(dat,1,aof) Now, how can I use

I used the multcomp package sometime back for doing multiple comparisons. I see that it has been updated and the methods like simint are no longer supported. When I run the program it prompts to me to use glht. How do I get the lower and upper conf int and the pValues using glht? Does anyone have an example? Thanks ../Murli [[alternative HTML version deleted]]

Dear all, I'm trying to make multiple comparisons for an lme-object. The data is for an experiment on parental work load in birds, in which adults at different sites were induced to work at one of three levels ('treat'; H, M, L). The response is 'feedings', which is a quantitative measure of nest provisioning per parent per chick per hour. Site is included as a random effect

Hi R users, I sent a message yesterday about NA in model estimates ( http://r.789695.n4.nabble.com/How-set-lm-to-don-t-return-NA-in-summary-td3722587.html). If I use aov() instead of lm() I get no NA in model estimates and I use gmodels::estimable() without problems. Ok! Now I'm performing a lot of contrasts and I need correcting for multiplicity. So, I can use multcomp::glht() for this.

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

Hi, I would like to derive p-values for pair-wise comparison (Tukey's) of effects when the response is a count. I am trying a test case where y ~ Po( lambda(x) ). x has three levels : A, B and C with lambda(x) = 10, 20 and 20 respectively. Hence, p-values for the contrast C - B should distribute uniformally. I have implemented this test case as below but do not get uniform

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