search for: pseudoreplicate

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

On 25/02/2011 21:22, Ben Ward wrote: > > -------- Original Message -------- > Subject: Re: [R] ANOVA and Pseudoreplication in R > Date: Fri, 25 Feb 2011 12:10:14 -0800 > From: Bert Gunter<gunter.berton at gene.com> > To: Ben Ward<benjamin.ward at bathspa.org> > CC: r-help<r-help at r-project.org> > > > > I can hopefully save bandwidth here by

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

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

...t was repeated twice, but in one of the experiments there was not enough input material and one of the DNA types (call it type D) was not tested at all, but all other levels of that factor and the condition factor were tested. From this, I think: 1. The replicates within each experiment are pseudoreplicates - there are pairs of measures with the same input material, and both factor levels are the same. 2. The 2 experiments can be treated as blocks, but they are not balanced or complete. There are 2 questions of interest to the experimenter: 1. Does the amount of DNA extracted differ for the d...

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

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)

...tment is CO2 in each chamber we have the same 4 functional plant groups (funktGr), each represented by four species (the species are nested in the functional plant groups, because if I take species as a single factor I get 16 species, which is not true. in each chamber I have four replicates (pseudoreplicates) half of the plants got fertilisation (Fert), that means each plant got it's own fertilisation the response variable is biomass (g) Randomisation was done to plot and subplot level. I tried this: aov(log(g)~CO2*funktGr/Species*Fert+Error(Gruppe/CO2),data=biomass) the output is: Error...

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

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

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

In some trial simulation work I need to create batch files that will repeatedly generate pseudoreplicate datasets and then create non- linear mixed effects models using nlme. Inevitably these models sometimes fail to converge but I need the batch file to simply move on to another simulation rather than abort. I am using the try() function as in model<-try((nlme(...))) which handles reporte...

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

Dear R-help-list, I have a problem in which the explanatory variables are categorical, the response variable is a proportion, and experiment contains technical replicates (pseudoreplicates) as well as biological replicated. I am new to both generalized linear models and mixed- effects models and would greatly appreciate the advice of experienced analysts in this matter. I analyzed the data in 4 ways and want to know which is the best way. The 4 ways are: 1. A generalized li...

I am trying to run the following model in R > lmer(leaves.eaten~Geocytotype+(1|TEST/ PLANT),data=cyphoplantfeeding,family=poisson) My experimental setup is 41 replicates (TEST) of an experiment in which there are three Geocytotypes of a plant species in each TEST, and two plant pseudoreplicates per Geocytotype in each test (i.e. 3*2=6 plants per test). So my random factors are trying to examine/ account for variation between replicates and between pairs of plants in each test. The response variable is counts of damaged leaves on each plant hence the poisson distribution. When I t...

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

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