similar to: Problem with lme using glht for multiple comparisons

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

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 everybody, problem solved, there was a typo. I wrote Type instead of Material Best ----Messaggio originale---- Da: angelo.arcadi@virgilio.it Data: 22-giu-2012 11.05 A: <r-help@r-project.org> Ogg: Error with glht function: Error in mcp2matrix(model, linfct = linfct) : Variable(s) 'Type' have been specified in 'linfct' but cannot be found in 'model'!

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))? >

Dear list members, I get the following error when using the glht function to perform a post hoc analysis for an ANOVA with repeated measures: require(nlme) lme_H2H_musicians = lme(H2H ~ Emotion*Material, data=musicians, random = ~1|Subject) require(multcomp) summary(glht(lme_H2H_musicians, linfct=mcp(Type = "Tukey")), test = adjusted(type = "bonferroni")) Error in

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 ***

Dear all, I was wondering if you could give me any suggestions/help on the following issue. So I carried out the analysis of my data using generalized linear model (glm). After that, to check for multiple comparisons, I applied the glht function from the multcomp package in R. The output, however, gave me a warning (please see below). So my question is whether this warning is smth that I should

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:

I'm trying to do an ANCOVA with two factors (clipping treatment with two levels, and plot with 4 levels) and a covariate (stem diameter). The response variable is fruit number. The minimal adequate model looks like this: model3<-lm(fruit~clip + plot + st.dia + clip:plot) I'd like to get some multiple comparisons like the ones from TukeyHSD, but TukeyHSD doesn't work with 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 =

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

Hi dears, I do > CHOL<-lme(chol~rt*cd4+sex+age+rf+nadir+pharmac+factor(hcv)+factor(hbs)+ haartd+hivdur+factor(arv), random= ~rt|id, na.action=na.omit) ...runs sweet,..then ....try a multicomparisons approach for the categorical rf > summary(glht(CHOL, linfct=mcp(rf="Tukey"))) * Error in model.frame.default(object, data, xlev = xlev) : l'oggetto non è una matrice

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

Hello R users, I'm analyzing an experiment in a balanced incomplet block design (BIB). The effect of blocks are assumed to be random, so I'm using nlme::lme for this. I'm analysing another more complex experiments and I notice some diferences from doBy::popMeans() compared multcomp::glht() and contrast::contrast(). In my example, glht() and contrast() were equal I suspect popMeans()

I am trying to do post-hoc tests associated with a repeated measures analysis with on factor nested within respondents. The factor (SOI) has 17 levels. The overall testing is working fine, but I can''t seem to get the multiple comparisons to work. The first step is to "stack" the data. Then I used "lme" to specify and test the overall model. Finally

Dear Help-list, I have a dataframe containing 6 variables, 4 of which are factors, 2 numeric. I want to create another factor variable (SitePos) by combining 2 existing factors (Site and Position). I have tried a number of approaches based on trolling the R FAQs, various R webpages, etc., none of which work. One approach e.g. Data1$SitePos <- paste(Data1$Site, Data1$Position) creates the

Hi I am a new user the R and I am very grateful for all your help but....... I have a problem and I can't resolve yet. I am trying to get the contrasts for a binomial 3-way GLM (T= 4 temperature, t= 2 time and c= 2 substrate levels, plus treatment control) in total they are 17 treatments. I have tried with the glht but this function only work for 1-way GLM, acacia<-cbind(g,N-g)

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

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

Hi All, I have used glm to model my data, I have two factors and a covariate as described in the example code below (mod.1). I have been able to "force" glht to perform multiple comparisons by creating a combined variable for the factors, accepting that there will be a loss of statistical power as it seems to do what I want. I then use the cld function to generate the letters of