similar to: post hoc comparison in repeated measure

Hi all, I'd like to compare all levels of my interaction with each other. I read the pdf 'Additional multcomp Examples' but even though there is an example with an interaction it doesn't work for me when I want to compare within and between groups. Here is an example: #### d.fr<-data.frame(id=rep(1:16,3),treat1=rep(as.factor(LETTERS[1:3]),each=

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

Dear R users, I have a linear model of the kind outcome ~ treatment + covariate where 'treatment' is a factor with three levels ("0", "1", and "2"), and the covariate is continuous. Treatments "1" and "2" both have regression coefficients significantly different from 0 when using treatment contrasts with treatment "0" as the

I have just realised that I sent this to Per only. For those interested on the list: -----Original Message----- From: Gygax Lorenz FAT Sent: Tuesday, September 14, 2004 4:35 PM To: 'Per Tor??ng' Subject: RE: [R] glmmPQL and random factors Hi Per, > glmmPQL(Fruit.set~Treat1*Treat2+offset(log10(No.flowers)), > random=~1|Plot, family=poisson, data=...) > > Plot is supposed

Hello All, I try to use the attached code to produce a cross over plot. There are 13 subjects, 7 of them in for/sal group, and 6 of them in sal/for group. But in xyplot, all the subjects are listed in both subgraphs. Could anyone help me figure out how to get rid of the empty levels? Thanks library(lattice) pef1 <- c(310,310,370,410,250,380,330,370,310,380,290,260,90) pef2 <-

Hello there, I am trying to run an ANOVA model using a non-Standard F ratio. Imagine that the treatments (treatments 1 & 2) are applied to the row not to individual samples. Thus the row is the experimental unit. Therefore my error term in my ANOVA table should be the error associated with with row. The question is how do I check the assumptions of an ANOVA model when I have a non-standard F

I don't know if this is the best place to post this question but I will try anyway. I have two experiements for which I use one-way matched-randomized ANOVA for the analysis and I would like to compare different treatments in the two experiments. The only common group in the two experiments are the controls. Is there any ANOVA design that allows me to make this comparison taking into

Hi, please excuse the most likely very trivial question, but I'm having no idea where to find related information: I try to recapitulate very simple plotting behavior of Excel within R but have no clue how to get where I want. I have tab delimited data like cell treatment value line a treat1 4 line a treat2 3 line b treat1 8 line b treat2 11 I'd like to have a plot (barplot), that

Hi all! I have 4 treatments on 5 animals Treat1 Treat2 Treat3 Treat4 Animal1 36 37 35 39 Animal2 33 34 36 37 Animal3 37 35 33 38 Animal4 34 36 34 35 Animal5 35 36 33 36 I use an Anova and i try to verify calcul So i retrieve: DF SS

Hi all: I have 3 questions about the poisson regression of contingency table. Q1¡¢How to understand the "independent poisson process"as many books or paper mentioned? For instance: Table1 ------------------------------------------- treat caner non-cancer sum ------------------------------------------- treat1 52(57.18) 19(13.82) 71 treat2

Dear useRs, First off, sorry about the long post. Figured it's better to give context to get good answers (I hope!). Some time ago I wrote an R function that will get all pairwise interactions of variables in a data frame. This worked fine at the time, but now a colleague would like me to do this with a much larger dataset. They don't know how many variables they are going to have in the

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

Dear R-mixed-effects-modelers, I could not answer this questions with the book by Pinheiro & Bates and did not find anything appropriate in the archives, either ... We are preparing a short lecture on degrees of freedom and would like to show lme's as an example as we often need to work with these. I have a problem in understanding how many dfs are needed if random terms are used for

I'm trying to determine the lack of fit for regression on the following: data <- data.frame(ref=c(0,50,100,0,50,100), actual=c(.01,50.9,100.2,.02,49.9,100.1), level=gl(3,1)) fit <- lm(actual~ref,data) fit.aov <- aov(actual~ref+Error(level),data) According to the information I have, the lack of fit for this regression is the

Hi all I am trying to do a simple recode which I am stumbling on. I figure there must be any easy way but haven't come across it. Given data of A","B","C","D","E","A" it would be nice to recode this into say three categories ie A and B becomes "Treat1", C becomes "Treat 2" and E becomes "Treat 3". I tried

Hi, I have been struggling with this problem for some time now. Internet, books haven't been able to help me. ## I have factorial design with counts (fruits) as response variable. > str(stubb) 'data.frame': 334 obs. of 5 variables: $ id : int 6 23 24 25 26 27 28 29 31 34 ... $ infl.treat : Factor w/ 2 levels "0","1": 2 2 2 2 1 1 1 2 1 1 ... $ def.treat :

I have used lme() on data from a between-within subjects experiment. The correct ANOVA table is known because this is a textbook example (Experimental Design by Roger Kirk Chapter 12: Split-Plot Factorial Design). The lme() F-values differ from the known results. Please help me understand why. d<-read.table("kirkspf2.dat",header=TRUE) for(j in 1:4) d[,j] <- factor(d[,j]) ### Make

Hi all, What are current methods people use in R to identify mis-spelled column names when selecting columns from a data frame? Alice Johnson recently tackled this issue (see [BioC] posting below). Due to a mis-spelled column name ("FileName" instead of "Filename") which produced no warning, Alice spent a fair amount of time tracking down this bug. With my fumbling fingers

Quick question, as I attempt to learn R. For post-hoc tests 1) Is there an easy function that will take, say the results of tukeyHSD and create a grouping table. e.g., if I have treatments 1, 2, and 3, with 1 and 2 being statistically the same and 3 being different from both Group Treatment A 1 A 2 B 3 2) I've been stumbling over the proper syntax for simple effects for a tukeyHSD

Hello, I want to know if two quadratic regressions are significantly different. I was advised to make the test using step 1 bootstrapping both quadratic regressions and get their slope coefficients. (Let's call the slope coefficient *â*^1 and *â*^2) step 2 use the slope difference *â*^1-*â*^2 and bootstrap the slope coefficent step 3 find out the sampling distribution above and