similar to: lme posthoc comparisons in R

Hi all: There's a question about glht function. My data:data_ori,which inclue CD4, GROUP,time. f_GROUP<-factor(data_ori$GROUP) f_GROUP is a factor of 3 levels(0,1,2,3) result <- lme(sqrt(CD4) ~ f_GROUP*time ,random = ~time|ID,data=data_ori) glht(result, linfct = mcp(f_GROUP="Tukey") ) Error in `[.data.frame`(mf, nhypo[checknm]) : undefined columns selected I can't

I'm trying to make a R package for a work in university. It´s my first time and i made some modifications on Pam algorithm that is included on cluster package. So before run Rcmd build command i made the next things: 1- I modified pam.c and cluster.h code. I added two new arguments on pam. 2- I modified pam.q, i added there my two new arguments. 4- I create all the necessary subdirectories

Dear Masters, I have a question to submit consider the following script m<-4.95 obs<-rpois(36,m) # i generate 36 realization from a poisson(m) hist(obs,freq=F) curve(dpois(x,m),add=T,col="red") #i wish to overlay on the histogram the theorical poisson density function errors are returned saing the x vector doesn't contain integers.... really bizarre i can't give

Hello. We have some questions concerning the statistical analysis of a dataset. We aim to compare the sample means of more than 2 independent samples; the sample sizes are unbalanced. The requirements of normality distribution and variance homogeneity were not met even after transforming the data. Thus we applied a nonparametric test: the Kruskal-Wallis-test (H-Test). The null hypothesis was

Dear List, are there post-hoc tests like Scheffe, LSD, etc. available after ANOVA test is performed with significant F-statistic? I have tried help.search("Scheffe"), but "No documentation found" (and I have most of packages installed). Probably there are such tests in R, and I am just searching badly... My second question is: Which test/method I should use for ANOVA-like

Hi All, I have performed a 3-way ANOVA analysis for my experimental data using aov function. My simple R funtion for this is: 3aof <- function(x){ m <- data.frame(R,S,T, x); anova(aov(x ~ R+S+T+R*S+R*T+S*T+R*S*T, m) ) } Now, I am getting P values for all the main and interactions effects. If I want to perform postdoc test on one of my main effects, say T, what method I should use (i have

Hi, I have analyzed my data using log-linear model as seen below: > yes.no <- c("Yes","No") > tk <- c("On","Off") > ats <- c("S","V","M") > L <- gl(2,1,12,yes.no) > T <- gl(2,2,12,tk) > A <- gl(3,4,12,ats) > n <- c(1056,4774,22,283,326,2916,27,360,274,1770,15,226) >

dear all, I want to perform a posthoc test for my ANCOVA: a1<-aov(seeds~treatment*length) With summary(glht(a1, linfct = mcp(treatment = "Tukey"))) R tells me: "covariate interactions found -- please choose appropriate contrast" How do I build these contrasts? Ideally, I would like to have the posthoc test for the ANCOVA including a block-effect

Dear R experts I am running a negative-binomial GLM (glm.nb) to test the null hypotheses that species 1 and 2 are equally abundant between site 1 and site2, and between each other. So, I have a 2x2 factorial design with factors Site (1,2) and Taxon (1,2). Since the Site:Taxon interaction is significant, I need to do the equivalent to a "post-hoc test" for ANOVA, however, the same tests

Dear R-group, I am trying desperately to get this Tukey test working. Its my first time here so I hope my question is not too stupid, but I couldn't find anything helpful in the help or in the forum. I am analysing a dataset of treated grasses. I want to find out, if the grasses from different Provenances react differently. In the aov test I found a significance for the combination Treatment