

-------- 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 8:56 AM, Ben Ward<benjamin.ward@bathspa.org>   wrote:
>>  On 05/01/2011 16:37, Mike Marchywka wrote:
>>>
>>>
>>>
>>>>  Date: Wed, 5 Jan 2011 15:48:46 +0000
>>>>  From: benjamin.ward@bathspa.org
>>>>  To: r-help@r-project.org
>>>>  Subject: [R] Simulation - Natrual Selection
>>>>
>>>>  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 kill them as a percentage, the challenge
>>>>  number, the cleaner as a two level variable, and the lineage theyre in,
>>>>  because I have several different lineages. I'm expecting the values to
>>>>  rise for one cleaner but not the other as they aqquire resistance for
>>>>  one but not the other. Which has happened, but I have wide variation
>>>>  because one linage aqquired a very dramatic change which has made it
>>>>  immune to 50%, whereas the others, have exhibited a much more gradual
>>>>  increace, and so I have very weak p values for the cleaner variable,
>>>>  because it is secondary to the challenge vector, which has the most
>>>>  explanatory power, because without time and these challenges, the
>>>>  selection would no happen. I was using two bacterium species, but one
>>>>  was keen on giving hight erratic results, and insisted on becoming cross
>>>>  contaminated, BUT if I include it's data, It shoves cleaner over the
>>>>  p0.05 threshold, so i may just be having a problem with lack of data. So
>>>>  I've been asking about bootstrapping, which I plan to do to my cases,
>>>>  and thenfit a model to see what the confidence is like then. I assume if
>>>>  I bootstrap then it will re-select whole cases, and not jumble
>>>>  everything up, otherwise a microbe (totake the most extreme value as an
>>>>  example) with 50% concentration tolerance at the beginning, would make
>>>>  no sense at all. I'm also planning on doing models lineage by lineage,
>>>>  rather than putting them into one whole, just to have a look at what
>>>>  happens.
>
>>>  You can't really have a p-value without a specific hypothesis to test,
>  -- More precisely: A p-value loses its meaning unless the tested
>  hypotheses are PRESPECIFIED -- i.e. determined BEFORE looking at the
>  data.
>
My hypothesis was specified before I did my experiment. Whilst far from
perfect, I've tried to do the best I can to assess rise in resistance,
without going into genetics as it's not possible. (Although may be at
the next institution I've applied for MSc).

With my hypothesis (I mentioned it below), I was of the frame of mind
that a nonsignificant p-value on the cleaner variable (for now -
experiment is far from over), indicated a lack of evidence for rejecting
the null. And so at the minute, it looks like the type of cleaner makes
no difference.
>>>  if you have that then all your other questions are probably easy to
>>>  answer.
>>>  Generally you want to sample from things that are "iid" or maybe you
>>>  want to test the "identical" i.
>  -- This is false. iid is not required. Example: weighted least
>  squares. It is true that figuring out the sampling distribution under
>  non-iid sampling can be (much) more difficult. For example, pivots may
>  not exist; approximations must typically be used.
>
>  -- Bert
>
>>  My Hypothesis is that Cleaner A (I don't really want to go into names or
>>  brands), will exhbit a rise in concentration tolerance values, or rather,
>>  the microbial culture I keep exposed to it, will, reflecting aqquisition of
>>  antimicrobial resistance. And this has largely happened. And that in cleaner
>>  B, this will not happen, or if it does, it will not be as dramatic and take
>>  longer. So I expecting in my model, the cleaner variable to have a p below
>>  0.05, and quite hight explanatory power, and a satisfying coefficient. The
>>  notion behind the hypothesis being that one might have a more difficult
>>  complex chemical structure, requiring more mutations to develop some
>>  resistance.
>>  I can't really do anything with genes or chemical structure at my current
>>  institution and at my level because  of no equippment for that sort of
>>  thing, and that they felt it would be too far for a 3rd year project. So I'm
>>  using the concentration required to kill them - or stop them from growing,
>>  as a indication.
>>>  Generally you want to have done a lit search ahead of time and
>>>  had some idea of likely evolution dynamics of your system given
>>>  your design and things like your forcing functions etc.
>>>  Most statisticians would not take seriously a posteriori designs and
>>>  indeed it can be hard to avoid rationalization and selection bias (
>>>  problems
>>>  that always and only effect people who disagree with me LOL) as being
>>>  anything other than exploratory or hypothesis generating- you are looking
>>>  for predictive value. While it is not always worthwhile doing blind tests,
>>>  it may be something worth considering ( do you know which group gets what
>>>  thing?)
>>>
>>>
>>>>  But what I really wanted to know from this email, was if there's a
>>>>  package or function for natrual selection simulation I could make use
>>>>  of, to see if I can simulate the experiment. I want to start with a
>>>
>>>  http://www.google.com/#sclient=psy&hl=en&q=%22R+package%22+natural+selection
>>>
>>>  but as implied above, R has lots of analysis stuff and maybe you
>>>  would find something more useful that is not linked to the keywords
>>>  you suggest. You may find, for whatever reason, you could write a
>>>  differential
>>>  equation to express your results but that isn't often used with "natural
>>>  selection."
>>>
>>>
>>>>  distribution of concentration tolerance values, taken from th
>>>  e
>>>>  inhibitory concentration values from my first lot of microbes, back when
>>>>  term began. Draw 3000 from this. Then values in that draw that fall
>>>>  below the exposure concentration I did in my experiment, are removed, or
>>>>  have a high chance of being removed. Then, from what is left, a draw is
>>>>  made again - or perhaps a copy operation (rather than a random draw)
>>>>  until I have 3000 again, rather than have all exactly the same
>>>>  concentration, then a value can be added to some of them, that increaces
>>>>  their concentration tolerance slightly, but not by a great deal, except
>>>>  in a few individuals, where it may be increaced dramatically(some sort
>>>>  of exponential dstribution perhaps). Then when the distribution of this
>>>>  simulated population of microbes has reached the next concentration
>>>>  (possibly the mean or mode of the distribution) (I have a series of 1 in
>>>>  2 dilutions, so 100% 50%, 25% and so on), then they move on to the next
>>>>  concentration.
>>>>
>>>>  I know it's probably quite a heavy thing, it was just a thought that
>>>>  came to me, if anybody has any experience in this area of R or knows of
>>>>  something that allows this to be done, please let me know.
>>>>
>>>>  Thanks,
>>>>  Ben.
>>>>
>>>>  ______________________________________________
>>>>  R-help@r-project.org mailing list
>>>>  https://stat.ethz.ch/mailman/listinfo/r-help
>>>>  PLEASE do read the posting guide
>>>>  http://www.R-project.org/posting-guide.html
>>>>  and provide commented, minimal, self-contained, reproducible code.
>>>
>>>
>>  ______________________________________________
>>  R-help@r-project.org mailing list
>>  https://stat.ethz.ch/mailman/listinfo/r-help
>>  PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
>>  and provide commented, minimal, self-contained, reproducible code.
>>
>
>


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