similar to: Resid() and estimable() functions with lmer

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 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, 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 :

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

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, 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 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

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, I have a simple dataset with repeated measures. one factor is treatment with 3 levels (treatment1, treatment2 and control), the other factor is time (15 time points). Each treatment group has 10 subjects with each followed up at each time points, the response variable is numeric, serum protein amount. So the between subject factor is treatment, and the within subject factor is time. I ran a

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 all, I was wondering if anyone could help solve a problem of a missing interaction effect!! I carried out a 2 x 2 factorial experiment to see if eggs from 2 different locations (Origin = 1 or 2) had different hatching success under 2 different incubation schedules (Treat = 1 or 2). Six eggs were taken from 10 females (random = Female) at each location and split between the treatments,

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

Dear R Users, Am using lm() with contrasts as below. If I skip the contrasts() statement, I get the coefficient names to be > names(results$coef) [1] "(Intercept)" "VarAcat" "VarArat" "VarB" which are much more meaningful than ones based on integers. Can anyone tell me how to get R to keep the coefficient names based on the factor levels

I'm trying to write some functions extending influence measures to multivariate linear models and also allow subsets of size m>=1 to be considered for deletion diagnostics. I'd like these to work roughly parallel to those functions for the univariate lm where only single case deletion (m=1) diagnostics are considered. Corresponding to stats::hatvalues.lm, the S3 method for class

Hi, I have the following code from Splus that I'd like to migrate to R. So far, the only problem is the arima.filt function. This function allows me to filter an existing time-series through a previously estimated arima model, and obtain the residuals for further use. Here's the Splus code: # x is the estimation time series, new.infl is a timeseries that contains new information # a.mle

In Venables \& Ripley 3rd edition (p. 231) the proportional odds model is described as: logit(p<=k) = zeta_k + eta but polr apparently thinks there is a minus in front of eta, as is apprent below. Is this a bug og a feature I have overlooked? Here is the naked code for reproduction, below the results. ------------------------------------------------------------------------ --- version

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"),