similar to: Error: could not find function "glht" (multcomp)

Dear all, I was trying to understand how "multcomp" package works by running the examples given in the documentation. However I still don't understand when it comes to multiple comparison set by user (please refer to "Ksub" in the code). Therefore I run 2 other cases along with the original example (case 1), with the expectation I'll get the point from the output. The

Hello Everyone,   I've been learning to use R in my spare time over the past several months. I've read about 7-8 books on the subject. Lately I've been testing what I've learned by trying to replicate the analyses from some of my SAS books. This helps me make sure I know how to use R properly and also helps me to understand how the two programs are similar and different.   Below is

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

fixed @ R-forge. New version should appear on CRAN soon. Thanks for the report! Torsten > > ---------- Forwarded message ---------- > Date: Sat, 3 Sep 2011 23:56:35 +0200 > From: Ulrich Halekoh <Ulrich.Halekoh at agrsci.dk> > To: "r-help at r-project.org" <r-help at r-project.org> > Subject: [R] glht (multcomp): NA's for confidence intervals using

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.

i ran this model > model2<-glm(rojos~ageandsex+sector+season+sector:season,quasipoisson) > glht(model2,linfct=mcp(ageandsex="Tukey")) General Linear Hypotheses Multiple Comparisons of Means: Tukey Contrasts Linear Hypotheses: Estimate M - H == 0 0.2898 SUB - H == 0 -0.2261 SUB - M == 0 -0.5159 I tried to do the same changing factor season

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 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()

Hej, Calculation of confidence intervals for means based on a model fitted with lmer using the package multcomp - yields results for calpha=adjusted_calpha - NA's for calpha=univariate_calpha Example: library(lme4) library(multcomp) ### Generate data set.seed(8) d<-expand.grid(treat=1:2,block=1:3) e<-rnorm(3) names(e)<-1:3 d$y<-rnorm(nrow(d)) + e[d$block]

Quick question about the usage of glht. I'm working with a data set from an experiment where the response is bounded at 0 whose variance increases with the mean, and is continuous. A Gamma error distribution with a log link seemed like the logical choice, and so I've modeled it as such. However, when I use glht to look for differences between groups, I get significant

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 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

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

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 =

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)),

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'!

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 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

Dear R community I currently try to get post hoc multiple comparisons with the package multcomp from a cox mixed-effects model, where the survival is explained by two variables (cover with levels: nocover and cover; treatment with levels: tx, uv, meta), whose interaction is significant. I read Hothorn, T. 2011: Additional multcomp Examples and there is an example involving a two-way ANOVA with

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 ***