similar to: [R-sig-ME] Fwd: Re: ANOVA and Pseudoreplication in R

Hi, As part of my dissertation, I'm going to be doing an Anova, comparing the "dead zone" diameters on plates of microbial growth with little paper disks "loaded" with antimicrobial, a clear zone appears where death occurs, the size depending on the strength and succeptibility. So it's basically 4 different treatments, and I'm comparing the diameters (in mm) of

Hi, I have an experiment with 2 independant factors which I have been trying to analyse in R. The problem is that there are several data points recorded on the same animal. However, no combination of treatments is repeated on the same animal. All possible combinations of treatments are done in a random order with as many points as possible being done on 1 animal before moving onto the next. The

Sorry if this is the wrong ml for this question, I am new to R. I am trying to use R to analyze the data from my thesis experiment and I am having troubles accounting for the pseudoreplication properly from having each participant repeat each treatment combination (combination of fixed factors) 5 times. The design of the experiment is as follows... Responses: CompletionTIme VisitedTargets

Hello, I am trying to help someone who has carried out an experiment and I'm finding it quite difficult to understand the appropriate model to use & code it. The response is a measurement - the amount of DNA extracted during the experiment. There were 2 factors to be tested - one is the condition under which the experiment took place and the other is the type of DNA to be

Need some help please I am trying to use this model because I have temporal replication in my data results<-read.table(file=file.choose(),header=T) attach(results) names(results) results<-na.omit(results) library(nlme) library(lattice) results<-groupedData(weight~date|group,outer=~diet,results) plot(results) plot(results,outer=T) model<-lme(weight~diet,random=~date|group,results)

Hi, I've been modelling some data over the past few days, of my work, repeatedly challenging microbes to a certain concentration of cleaner, until the required concentration to inhibit or kill them increaces, at which point they are challenged to a slightly higher concentration each day. I'm doing ths for two different cleaners and I'm collecting the required concentration to

Dear all, How can I run a constrained correspondence analysis with the following data: 15 animals were measured repeatedly month-wise (over to 2 years) according to ther diet composition (8 food categories). our data.frame looks like this: food 1 2 ... 8 sex season year animal freq 12 8 ... 1 0 summer 2011 1 freq 0 7 ... 0 1 winter 2011 1 ... freq 0 7 ... 0 1 spring 2011 15 We

Hello I've created a 3d scatterplot, and had no problems labeling the points. However, I've been really struggling to change the color of the points based on a factor (see 'group' below). Is such a thing possible? My data look like this: food group x y z apple fruit 0.216 -0.112 -0.893 orange fruit 0.814 0.097 0.460 broccoli veg -0.239 0.240 -0.425 banana

CAN ANYONE PLEASE HELP ME WITH THIS i HAVE TO DO A MIXED EFFECT LINEAR MODEL WITH MY DATA DUE TO THE FACT THAT I have pseudoreplication! Although after reading and trying it for several times can get around due to "Error in na.fail.default(list(date = c(1L, 2L, 3L, 4L, 5L, 6L, 7L, 8L, : missing values in object" I uploaded my data file Thank you so much Kind regards AG

Question for all: Right now i have a Table in a mySQL DB that has a row called Ingredients. When the data is entered into the DB its enter like so from a text area: 1 1/2 lbs. beef top sirloin, thinly sliced 1/3 cup white sugar 1/3 cup rice wine vinegar 2 tablespoons frozen OJ concentrate 1 teaspoon salt 1 tablespoon soy sauce 1 cup long grain rice 2 cups water 1/4 cup cornstarch 2 teaspoons

Dear all, I collected my data from the different agricultural fields every week over a period of a month. how can I test for spatial autocorrelation in R with data that are temporally pseudoreplicated? I used lme with correlation=corCompSymm(form=~Date) to model temporal pseudoreplication. Regards, VG

Hi all, I'm not sure how to correctly analyse the following data with glm, and hope for some advice from this list, ideally showing how to specify the model in R and perform the tests, and also for suggestions of literature. The data structure is like this: - 20 plant populations were investigated (random factor pop), which belong to different habitat types (factor ht) - Within

Dear All, I'm rather a beginner on nested ANOVAs, so here goes with my 2 questions; Qu 1: I'm modelling the number of galls on a leaf (the response variable) as a function of; the tree on which I find the leaf, the branch on which I find the leaf. Then, the tree and the branch are both random factors, and I'm quite happy that I should write; aov(galls~tree/branch +

Dear all, I wonder if anyone can help me with specifying a right model for my analysis. I am a beginner to lme methods. I was unfortunately not able to find a solution to my problem on my own. Data structure: I have sampled monthly 6 basins during two hydrological cycles, and I have taken several (2 to 4) samples (“replicate”) for each basin and month. I’m trying to relate Shannon diversity

Hello, I'm having some trouble figuring out the correct model specification for my data. The system consists of multiple populations of an organism, which have been genetically sampled for several years. The problem is this: A minority of individuals are found in more than one sample, either they have survived into the next sampling at the same location, or have migrated to another another

Hi all, We just set up our new mail servers using Postfix and Dovecot. We have implemented a load balanced solution to where our two mail servers have a common NFS share. Everything is working great. Everything but Dovecot for the most part. In testing, Dovecot was great. Now that we're in the production environment, we've noticed that every 20 minutes, Dovecot will stop

Hi, sorry by this off. I'm still try to understand nested design. I have the follow example (fiction): I have 12 plots in 4 sizes in 3 replicates (4*3 = 12) In each plot I put 2 species (A and B) to reproduce. After a period I make samples in each board and count the number of individuals total (tot) and individuals A and B (nsp). Others individuals excepts A and B are in total of

-------- Original Message -------- Subject: Re: [R] Simulation - Natrual Selection Date: Wed, 05 Jan 2011 17:24:05 +0000 From: Ben Ward <benjamin.ward@bathspa.org> To: Bert Gunter <gunter.berton@gene.com> CC: Mike Marchywka <marchywka@hotmail.com> On 05/01/2011 17:08, Bert Gunter wrote: > Couple of brief comments inline below. -- Bert > > On Wed, Jan 5, 2011 at

Dear professors and collegues I need to perform a analysis of dates from a nested experimental design. From "Bioestatical Analysis" of Zar "Bimetry of Sokal" & Rohlf "Design and Analysis of Experiments" of Montgomery I have: Sum (mean(x)_i - mean(x)_T)2 / (a-1) -> var(epsilon) + n sigma2_B + n b (sum alfa_i)2 / (a-1) Sum (mean(x)_ij - mean(x)_i)2 /

Hello, I have a couple questions regarding generalized linear mixed models specifically around fitting the random effects terms correctly to account for any pseudo-replication. I am reading through and trying to follow examples from Zuur et al. Mixed Effects Models and Extensions in Ecology with R, but am still at bit unsure if I am specifying the models correctly. Background information: Our