similar to: glht (multcomp): NA's for confidence intervals using univariate_calpha (fwd)

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]

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)),

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 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

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

It's been a few weeks I'm racking my brains on how to use the function glht the package multcomp to test interactions. Unfortunately, the creator of the package forgot to put a sample in pdf package how to do it. I have looked in several places, but found nothing. If someone for the love of God can help me I'll be extremely grateful. The model is glm. -- View this message in context:

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

Dear List, Could you tell why I get the error message? > library(multcomp) > data("cholesterol") > m = aov(response ~ trt, data = cholesterol) > cht <- glht(m, linfct = mcp(trt = "Tukey")) Error: could not find function "glht" Thank you G?bor

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()

Hello, I have a question as to how the syntax for glht(package multcomp) and estimable (gmodels) works, since I'm not getting everything from the documents I've googled so far, especially with models with 2nd order terms. A modestly complex model: 2-way anova with one continuous covariate, no random effects(and no repeated measures) to keep it modestly complex: Y = treatmentgroup + sex

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 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

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.

Dear all, the application of the function rstandard() in the base package to a glm object does not produce residuals standardized to have variance one: the reason is that the deviance residuals are divided by the dispersion estimate and not by the square root of the estimate for the dispersion. Should the function not be changed to produce residuals with a variance about 1? R 1.8.1 on

the function confint uses the profiling method of the function of the package MASS confint.glm even after the package has been detached! 1: might this be the intenden behavior? 2. How does the function remember its 'MASS' functionality after detaching the package? R: 1.8.0; Windows 2000 Here is a sample program > set.seed(7882) > x<-rep(c(0,1),c(20,20)) >

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 all, Two questions: 1. Is there a way to evaluate models from lmer() with a poisson distribution? I get the following error message: library(lme4) lmer(tot.fruit~infl.treat+def.treat+(1|initial.size),family=poisson)->model plot(fitted(model),resid(model)) Error: 'resid' is not implemented yet Are there any other options? 2. Why doesn't the function estimable() in gmodels

Hi R experts, I have been using ReML as follows... model<-lmer(late.growth~mtf+year+treat+hatch.day+hatch.day:year+hatch.day:treat+ mtf:treat+ treat:year+ year:treat:mtf+(1|fybrood), data = A) then I wanted to plot the results of the three way interaction using lines() as follows... tmp<-as.vector(fixef(model)) graph1<-plot(mtf,fitted(f2), xlab=list("Brood Size"),

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

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