Displaying 20 results from an estimated 28 matches for "pseudoreplication".
2011 Feb 25
1
ANOVA and Pseudoreplication in R
...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 the same...
2011 Feb 26
2
[R-sig-ME] Fwd: Re: ANOVA and Pseudoreplication in R
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 belongs on...
2009 Apr 29
3
2 way ANOVA with possible pseudoreplication
...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 variables are...
2011 Feb 04
0
spatial autocorrelation for data that are temporally pseudoreplicated
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
2009 Nov 09
1
Incomplete, unbalanced design, and pseudoreplication?
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
2009 Apr 01
1
Help with mixed-effects model with temporal pseudoreplication!
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
Random-fact...
2009 Dec 04
0
pseudoreplication - LME
...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.
2008 May 26
0
use aov or lme for split plot design?
...Species in funktGr the way I did? It doesn't
make sense to
analyse Species as a main factor in the whole analysis, I would need
to do four separate
analysis within a functional plant group, is that correct?
- Do I need to average over the four pseudoreplicates for each chamber
or is the
pseudoreplication taken out by the Error term?
- I often found in the R-helps that people use the lme functiona
instead of aov. Which
one is more appropriate?
- with the aov function can I also eliminate the non significant
factors to simplifiy the
model?
Thanks a lot for your answers
Christina
--
Christina...
2011 Nov 18
1
cca with repeated measures
...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?
2009 Nov 11
1
LINEAR MIXED EFFECT
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/rawoctoberca...
2010 Oct 04
0
glmer or not - glmer model specification
...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 time. Hete...
2008 Jan 31
1
Error handling in nlme call
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
2003 Nov 06
1
Hierarchical glm
...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
2002 Apr 15
1
Nested ANOVA with covariates
...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 as;
a...
2007 Nov 02
1
lme model with replicates within a random factor
...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(shannon<...
2011 Apr 21
1
Accounting for overdispersion in a mixed-effect model with a proportion response variable and categorical explanatory variables.
...lly (first to last)
Df Deviance Resid. Df Resid. Dev F Pr(>F)
NULL 17 60.467
treatment 3 31.756 14 28.711 5.1646 0.01303 *
---
Signif. codes: 0 ?***? 0.001 ?**? 0.01 ?*? 0.05 ?.? 0.1 ? ? 1
>
I then tried to take the effect of pseudoreplication into account with
3:
3. A generalized linear mixed-effects model with binomial error in
which technical replication is treated as a random effect.
Here is the input file:
treatment mouse observation positive negative
A 1 1 73 149
A 1 2 50 129
A 1 3 85 161
A 2 1 73 139
A 2 2 89 144
A 2 3 53...
2008 Nov 27
1
lmer refuses nested random factors
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
2003 Apr 09
1
[OFF] Nested or not nested, this is the question.
...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)
numDF...
2012 Feb 06
1
multiple comparisons in nested design
...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),data=Mesur...
2010 Aug 19
1
GLMM random effects
...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 analyze?...