search for: pseudorepl

...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 circles. I'm concerned however, about Pseudoreplication and how to deal with it in R, (I thought of using the Error() term. I have four levels of one factor(called "Treatment"): NE.Dettol, EV.Dettol, NE.Garlic, EV.Garlic. ("NE.Dettol" is E.coli not evolved to dettol, exposed to dettol to get "dead zones". And t...

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 suggesting that this bel...

...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 suggested way to remove pseudoreplication is to average the points from the same animal. However, as my measures on the same animal are of different treatment combinations so this makes no sense. It is also suggested that as I have random and fixed effects I should use a mixed effects model. However, given that my independant variab...

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...

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 Fixed-factors: Targets (4-levels): 4, 9, 14, 19 Entropy (3-levels): Low, Medium, High Rand...

...m, convergence error code = 1 message = iteration limit reached without convergence (9) summary(model) http://n4.nabble.com/file/n948321/rawoctobercalciumexperiment2.xlsx rawoctobercalciumexperiment2.xlsx kind regards thank you in advance Ana -- View this message in context: http://n4.nabble.com/pseudoreplication-LME-tp948321p948321.html Sent from the R help mailing list archive at Nabble.com.

...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:...

...0 7 ... 0 1 winter 2011 1 ... freq 0 7 ... 0 1 spring 2011 15 We want to find out if season and sex influences diet composition. My experience with CCA is limited, but in repeated measures ANOVA, e.g. with aov() on has to define the between (animal) error term in order to deal with the pseudoreplication. Do I have to restructure or reshape the data in order to deal with pseudoreplication the data? Or do I have to define an error strata? I suspect I cannot simply run: library(vegan) model=cca(food ~ season*sex+year+animal, data) I would be grateful for any help. Thanks in advance, Ren?

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 http://old.nabble.com/file/p26300394/rawoc...

...hich 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 location and survived into the next sampling there. This pseudoreplication constitutes about 12% of all observations. I have information on the number of heterozygous locus per individual, its location, year, population size and number of immigrants at that location, and want to investigate the effect of population size and migration on heterozygosity through tim...

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 re...

...to the deviance analysis case. For example, when I fit the whole model, and then drop ht to test for the effect of ht, the effect of ht shows up in pop (I understand why, but don't know how to do this otherwise). If I compare the null model to the model including ht only, do I then commit a pseudoreplication? Thanks for your help Pascal

...y the presence of absence of an electric field (a fixed factor). This would normally be a straight forward 2 way anova; stress ~ cadmium*electric. But, I have the complication that I have nesting. I have 4 reps for each combination of cadmium and electic field. Within each of these 4 reps, I have pseudoreplication. Specifically, I have 3 reps within each of the 4 main reps. In detail, worms are held in little wells in plates (random factor). I have 4 plates for each combination of cadmium and electic field. These are geniune reps. Within each plate, there are 3 pseudoreps. I thought I could model this...

...t 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 to some environmental variables, taking away pseudoreplication. Thus, I have tried an lme model with “time” and “basin” as random factors. I have put hydrological cycle as a fixed factor, since I’m interested in quantifying their effect. But I’m not sure if there are other possibilities to do this. This is my model structure: Model1<-lme(shan...

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 generaliz...

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. Whe...

...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 individuals. This make a dataset with the 24 lines and not 12. Its smell pseudoreplication in a nested design, OK? I need to know: the species are different in proportion? the size affect the species's proportion? existe interaction between size and species? I make the analysis. > m.lme <- lme(nsp/tot~size*specie,random=~1|size/specie) > anova(m.lme)...

...be: #> AnovaModel.1 <-aov(VR ~ trat + trat/patient, data=Mesures) #> summary(AnovaModel.1) Df Sum Sq Mean Sq F value Pr(>F) trat 4 1.580 0.3950 0.751 0.565 trat:patient 10 4.811 0.4811 0.915 0.533 Residuals 30 15.778 0.5259 But this methods causes pseudoreplication because the F is the ratio of "MS of trat" and "MS of res". However the variability of "trat" is at least as big as the variability of "patient". The correct analysis suggested by ?aov is: #> AnovaModel.2 <- aov(VR ~ trat + Error(patient),dat...

...example of the R code that we started with is: PDmodel_1 <- lmer(Density~1+Treatment+Session+Biomass+(1|Colony/Plot), family=quasipoisson, data=DensityPD) My main question is: Does this accurately take into account our nested and repeated measures design? Have we accounted for any possible pseudoreplication with this type of analysis? Do I need to also look at type AR-1 error structures (or can I do that in a glmm?) Or is there a simpler way you might suggest? I can also look at the change in density between time intervals instead density at time intervals if that makes anything easier to ana...