On 27/02/2011 05:46, Robert A LaBudde wrote:
I think you are over-concerned with the term "pseudo-replication". All
this means is that the error source is nested, and not a full replicate.
What you haven't done, and need to do, is to describe your experiment
in terms of the real variables and error sources. Then code them as
"fixed" or "random" and write the model form.
You description is very complex and confusing. You need to identify:
1. Exactly what culture is used in each trial.
2. All subcultures: NE vs EV, which chemical, etc.
3. Which plate.
4. Which circle on which plate.
5. Which measurement on which circle.
After you have identified all descriptors that identify each
individual measurement, put them in a file with columns for factors
and the outcome.
At this point it should be clear that you have a number of random
factors: plate, circle, measurement, plus any culture replicates, if
you have them.
One you successfully do this, you can write the model equation with
the proper nesting.
The time to do all this is BEFORE you conduct the experiment.
I think then, number 1 mute, because it's all E.coli culture. NE and EV
I think is a fixed effect, it should affect the mean, rather than the
error/variance, as it the the bit of the experiment I'm manipulating and
want to evaluate. Which plate is mute, because I put 3 circles on each
plate, so there are no more than one plate for each treatment - because
that's how the methodology is done in all books on the subject, and
there wouldn't be enough plates to go around. Circle could be random
because it could alter the variance/errors, I don't expect it to alter
the mean. The measurements on the cirlces - there are 4 for each circle,
are my (y) value, what I'm measuring, so I'm unsure on this one, I took
4 measurements from each circle, and I would have thought they are all
very similar in which case I think they may be a source of a little
error, but this is also the thing I'm measuring so I'm unusre on what to
do about this. As I said, my first port for advise told me It wouldn't
be horrificly detrimental to my project at this level if I took means of
the 4 values of each circles and worked with those instead, but if I can
solve this with a mixed effect model, it would be better.
The reason this was not worked out before the experiment, was that it
was always thought a straight worward ANOVA was going to be sufficient,
because this project has not been done in our uni before and new
territory even for my supervisor, were both discovering things about
this project, so it's been a little experimental in places, in having to
adapt the methodology if a certain issue of practicality arose. But it's
nearly done now.
Thanks,
Ben W.
At 10:44 AM 2/26/2011, Ben Ward wrote:
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.ber...@gene.com>
To: Ben Ward<benjamin.w...@bathspa.org>
CC: r-help<r-help@r-project.org>
I can hopefully save bandwidth here by suggesting that this belongs on
the R-sig-mixed-models list.
-- Bert
As an aside, shouldn't you be figuring this out yourself or seeking
local consulting expertise?
I did consult with the lecturer at university that knows most about
stats, and he advised me:
"Pseudo replication is really about a lack of independence between
measurements, So you need to work backwards and see where you are
building in a known lack of independence. And where that is the case
you need to use means of all the values."
And I have done this and came to the conclusion I mentioned as to
where I thought Pseudoreplicaton was comming from, however, I do not
know about the one other 'potential' source as it really is for me at
least, a grey area.
I've consulted a few forums that deal with the theory more and await
any response. Until then I'll have to try and get as many opinions on
it as possible.
-Ben W.
On Fri, Feb 25, 2011 at 9:08 AM, Ben
Ward<benjamin.w...@bathspa.org> wrote:
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
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 for
NE.Garlic, but with
garlic, not dettol. "EV.Dettol" is E.coli that has been evolved
against
dettol, and then tested afterwards against dettol to get the
"dead zones".
Same applies for "EV.Garlic" but with garlic). You see from the
four levels
(or treatments) there are two chemicals involved. So my first
concern is
whether they should be analysed using two seperate ANOVA's.
NE.Dettol and NE.Garlic are both the same organism - a lab stock
E.coli,
just exposed to two different chemicals.
EV.Dettol and EV.Garlic, are in principle, likely to be two
different forms
of the organism after the many experimental doses of their
respective
chemical.
For NE.Garlic and NE.Dettol I have 5, what I've called
"Lineages", basically
seperate bottles of them (10 in total).
Then I have 5 Bottles (Lineages) of EV.Dettol, and 5 of
EV.Garlic. - This
was done because there was the possiblity that, whilst I'm
expecting them
all to respond in a similar manner, there are many evolutionary
paths to the
same result, and previous research and reading shows that
occasionally one
or two react differently to the rest through random chance.
The point I observed above ("NE.Dettol and NE.Garlic are both the
same
organism...") is also applicable to the 5 bottles: The 5 bottles
each of
NE.Garlic and NE.Dettol are supposed to be all the same organism
- from a
stock one kept in store in the lab.
There is potential though for the 5 of EV.Garlic, to be different
from one
another, and potential for the 5 EV.Dettol to be different from
one another.
The Lineage (bottle) is also a factor then, with 5 levels
(1,2,3,4,5).
Because they may be different.
To get the measurements of the diamter of the zones. I take out a
small
amount from a tube and spread it on a plate, then take three
paper disks,
soaked in their respective chemical, either Dettol or Garlic. and
press them
and and incubate them.
Then when the zones have appeared after a day or 2. I take 4
diameter
measurements from each zone, across the zone at different angles,
to take
account for the fact, that there may be a weird shape, or not quite
circular.
I'm concerned about pseudoreplication, such as the multiple
readings from
one disk, and the 5 lineages - which might be different from one
another in
each of the Two "EV." treatments, but not with "NE." treatments.
I read that I can remove pseudoreplication from the multiple
readings from
each disk, by using the 4 readings on each disk, to produce a
mean for the
disks, and analyse those means - Exerciseing caution where there
are extreme
values. I think the 3 disks for each lineage themselves are not
pseudoreplication, because they are genuinley 3 disks on a plate:
the "Disk
Diffusion Test" replicated 3 times - but the multiple readings
from one disk
if eel, is pseudoreplication. I've also read about including
Error() terms
in a formula.
I'm unsure of the two NE. Treatments comming from the same
culture does not
introduce pseudoreplications at Treatment Factor Level, because
of the two
different antimicrobials used have two different effects.
I was hoping for a more expert opinion on whether I have identified
pseudoreplication correctly or if there is indeed
pseudoreplication in the 5
Lineages or anywhere else I haven't seen. And how best this is
dealt with in
R. At the minute my solution to the multiple readings from one
disk is to
simply make a new factor, with the means on and do Anova from
that, or even
take the means before I even load the dataset into R. I'm
wondering if an
Error() term would be correct.
Thanks,
Ben W.
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================================================================
Robert A. LaBudde, PhD, PAS, Dpl. ACAFS e-mail: r...@lcfltd.com
Least Cost Formulations, Ltd. URL: http://lcfltd.com/
824 Timberlake Drive Tel: 757-467-0954
Virginia Beach, VA 23464-3239 Fax: 757-467-2947
"Vere scire est per causas scire"
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