similar to: simple plot question

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 <-

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

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 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 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

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

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 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

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, 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, 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 :

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 been trying for hours now to perform an orthogonal contrast through an ANOVA in R. I have done a two-factor factorial experiment, each factor having three levels. I converted this dataset to a dataframe with one factor with nine treatments, as I couldn't work out what else to do. I have set up a matrix with the eight orthogonal contrasts that I wish to perform, but despite

Hi, I have many files containing one column of data. I like to use the scan function to parse the data. Next I like to bind to a large vector. I try this like: count<-1 files <- list.files() # all files in the working directory for(i in files) { tmp <- scan(i) assign(files[count], tmp) count<-count+1 } This part works! Now I like to plot the data in a boxplot.

Hi. I'm looking to create a user-friendly program built around some R methods I've written. The program should be as easy to install and use as possible and will be built around a GUI. This program will be cross-platform; that's crucial. I'm familiar with Java and its GUI packages, I've been looking at the JRI package (interfaces R with Java) but I'm a little uneasy about

Hi, I have a question concerning the image function and how to generate custom axis labels: dat<-sample(0:1,1000,replace=T) matrix(dat,ncol=5,nrow=200)->x x[order(x[,1],x[,2],x[,3],x[,4],x[,5]),]->x I would like to have a heatmap kind of thing like this: image(t(x),col=c(0,1),axes=F) axis(1, 1:5, c(colnames(x))) I only do see parts of the axis (only an "A" is

Hi all, I am trying to plot a bar chart and trying to plot a line as a secondary axis as my scale is different for two y axis. I am plotting a clustered bar chart by using besides = True option in barplot function and my y coordinates are not plotted exactly at the center on each two bars. Please help me. I am pasting the code as follows. x =