similar to: Newbie question Multcomp

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 want to export the result of glht in R into a LaTeX table, such as that result: Linear Hypotheses: Estimate Std. Error z value Pr(>|z|) Group1 - Group2 == 0 -0.14007 0.01589 -8.813 <0.001 "***" Group1 - Group3 == 0 -0.09396 0.01575 -5.965 <0.001 *** --- Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05

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]]

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

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:

Hi R users: I have de following data frame (called "Sx") Descripcion Nitratos Cont85g 72.40 Cont85g 100.50 Cont85g 138.30 Cont80g 178.33 Cont80g 79.01 Cont80g 74.16 Cont75g 23.70 Cont75g 15.80 Cont75g 16.20 Patron80g

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

I am not sure whether there is a bug. When I tested the example given for "glht" in the help, I entered the following error: Running commands: amod <- aov(minutes ~ blanket, data = recovery) rht <- glht(amod, linfct = mcp(blanket = "Dunnett"), alternative = "less") Errors are: Error in try(coef.(model)) : could not find function

I am not sure whether there is a bug. When I tested the example given for "glht" in the help, I entered the following error: Running commands: amod <- aov(minutes ~ blanket, data = recovery) rht <- glht(amod, linfct = mcp(blanket = "Dunnett"), alternative = "less") Errors are: Error in try(coef.(model)) : could not find function

Hello all, I would like to do a Dunnett test in glht(). However, the factor level I want to use as the control is not the first. dunn1<-glht(model3, linfct = mcp(Container = "Dunnett"), alternative = "less") The factor container has 8 levels, so it would be nice not to manually enter in all of the contrasts. I originally discovered glht() when working with a glm model

Hi, I have a fairly simple repeated measures-type data set I've been attempting to analyze using the lme function in the nlme package. Repeated searches here and other places lead me to believe I have specified my model correctly. However, I am having trouble with post-hoc tests. From what I gather, other people are successfully using the glht function from the multcomp package to

Hi I have some experimental data where I have counts of the number of insects collected to different trap types rotated through 5 different location (variable -location), 4 different chemical attractants [A, B, C, D] were applied to the traps (variable - semio) and all were trialled at two different CO2 release rates [1, 2] (variable CO2) I also have a selection of continuous variables

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

Could you please help me on the following ANCOVA issue? This is a part of my dataset: sampling dist h 1 wi 200 0.8687212 2 wi 200 0.8812909 3 wi 200 0.8267464 4 wi 0 0.8554508 5 wi 0 0.9506721 6 wi 0 0.8112781 7 wi 400 0.8687212 8 wi 400 0.8414646 9 wi 400 0.7601675 10 wi 900 0.6577048 11 wi 900

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.

Hello, R-i-zens. I'm working on an data set with a factorial ANOVA that has a significant interaction. I'm interested in seeing whether the simple effects are different from 0, and I'm pondering how to do this. So, I have my.anova<-lm(response ~ trtA*trtB) The output for which gives me a number of coefficients and whether they are different from 0. However, I want the

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 =

Hello everyone, I've conducted a Type III repeated-measures ANOVA using Anova() from the car package, based on the suggestions at http://blog.gribblelab.org/2009/03/09/repeated-measures-anova-using-r/(option 3) and http://languagescience.umd.edu/wiki/EEG#ERP_ANOVA_in_R. My ANOVA has two factors: Condition (3 levels) and Region (6 levels) and their interaction. Below is code to run the Anova

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 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 +