similar to: glht multiple comparisons for glm with 2 factors

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, I have a few questions about glht() and the interpretation of output from Tukey's in multcomp package for lme() model. The main issue is that I noticed that a plot that I produced with code letters seem to contradict the graph itself. I provide data and code below. I end with my questions. A few things about data set. "LMA.vcp" is continuous response variable.

Hi, I have data with binomial response variable (survival) and 2 categorical independent variables (site and treatment) (see below).? I have run a binomial GLM and found that both IVs and the interaction are significant.? Now I want to do a post-hoc test for all pairwise comparisons to see which treatment groups differ.? I tried the glht function in the multcomp package, but I get surprising

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

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

Dear R list., I am running a script to get a compact letter display. library(lme4) library(multcomp) library(gplots) ####### Mixed Effects Model ######### data <- read.table("AJmix.txt",header=TRUE, sep="\t") attach(data) y<-cbind(positive,negative) treatment<-factor(treatment) mouse<-factor(mouse) data$obs<-1:nrow(data) names(data) detach(data) attach(data)

Hi everyone, I am new to R and have a question that relates to unplanned post-hoc comparisons using the multcomp package after a mixed effects model. I couldn't find the answer to it in the archive or in any manual. I have a dataset in which several plants have been treated in a particular way and a continuous response variable has been measured depending on several leaves per plant. I am

Hi, I have been trying glht() from multcomp package and contrast() from contrast package to test a contrast that I am interested in. With the following simulated dataset (fixed effect "type" with 3 levels (b, m, t), and random effect "batch" of 4 levels, a randomized block design with interaction), sometimes both glht() and contrast() worked and gave nearly the same p values;

Hi all, I've posted this question before, but did not get any reply. I post it again here and see if anybody can help. Thank you. I have a nested model with the following effects fixed: treatments random: experiment_date I used lme() to model the data mod1 <- lme(N_cells ~treatments-1, random=~1|experiment_date, method='ML') Then I want to compare all the other

Using a factor with 'number' levels the straightforward symbolic formulation of a contrast in 'glht' of the 'multcomp' package fails. How can this problem be resolved without having to redefine the factor levels? Example: #A is a factor with 'number' levels #B similar factor with 'letter' levels dat<-data.frame(y=1:4,A=factor(c(1,1,2,2)),

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

Hi, I am experimenting with using glht() from multcomp package together with coxph(), and glad to find that glht() can work on coph object, for example: > (fit<-coxph(Surv(stop, status>0)~treatment,bladder1)) coxph(formula = Surv(stop, status > 0) ~ treatment, data = bladder1) coef exp(coef) se(coef) z p treatmentpyridoxine -0.063 0.939 0.161

Hi, I'm wondering if i can use the function "TukeyHSD" to perform the all pairwise comparisons of a "aov()" model with one factor (e.g., GROUP) and one continuous covariate (e.g., AGE). I did for example: library(multcomp) data('litter', package = 'multcomp') litter.aov <- aov(weight ~ gesttime + dose, data = litter) TukeyHSD(litter.aov, which =

I have a question about TukeyHSD and the glht function because I'm getting different answers when a covariate is included in model for ANCOVA.? I'm using the cabbages dataset in the 'MASS' package for repeatability.? If I include HeadWt as a covariate, then I get different answers when performing multiple comparisons using TukeyHSD and the glht function. The difference appears

I've run an ancova, edadysexo is a factor with 3 levels,and log(lcc) is the covariate (continous variable) I get this results > ancova<-aov(log(peso)~edadysexo*log(lcc)) > summary(ancova) Df Sum Sq Mean Sq F value Pr(>F) edadysexo 2 31.859 15.9294 803.9843 <2e-16 *** log(lcc) 1 11.389 11.3887 574.8081 <2e-16 ***

Hi, i was working with this model > mq<-glm(rojos~edadysexo*zona*estacion,quasipoisson) and i get this minimal adequate model > anova(mq5,test="F") Df Deviance Resid. Df Resid. Dev F Pr(>F) NULL 518 64799 edadysexo 2 1556.5 516 63243 8.9434 0.0001524 *** zona 4

Dear R-user I used lme to fit a linear mixed model inlcuding weights=varPower(). Additionally I wanted to use glht to calculate Tukey-Kramer multiple comparision. error: > glht(modelF, linfct=mcp(Species="Tukey")) Error in glht.matrix(model = list(modelStruct = list(reStruct = list(SubPlot = -0.305856275920955, : ?ncol(linfct)? is not equal to ?length(coef(model))? >

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

Dear R members, I tried to fit an lme model and to use the glht function of multcomp. However, the glht function gives me some errors when using weights=varPower(). The glht error makes sense as glht needs factor levels and the model works fine without weights=. Does anyone know a solution so I do not have to change the lme model? Thanks Sibylle --> works fine

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