From: Andrew Robinson <A.Robinson_at_ms.unimelb.edu.au>

Date: Sun, 23 Nov 2008 20:51:00 +1100

Date: Sun, 23 Nov 2008 20:51:00 +1100

Hi David,

On Fri, Nov 21, 2008 at 12:01:52PM -0800, dschruth wrote:

> I'm a programmer in a biology lab who is starting to use R to automate

*> some of our statistical analysis of growth rate determination. But I'm
**> running into some problems as I re-code.
**>
**> 1) Hypotheses concerning Slope similarity/difference:
**> I'm using R's anova(lm()) methods to analyse a model which looks
**> like this:
**> growth.metric ~ time * test.tube
**> I understand that testing the the interaction between time and tube
**> (time:test.tube) will tell us if the growth rates (for the last three
**> test tubes) are significantly different from one another (Ho=slopes
**> are the same). The purpose of doing this test is so that we can be
**> certain our cultures have fully acclimated to the treatment and aren't
**> going to change much if we stop measuring. This is an important cost
**> saving practice too as measurements can go on for years. Yet I'm
**> worried that our null and alternative hypotheses should be swapped so
**> that our test is more conservative (Ho=slopes are different ... ie
**> still acclimating.)
*

Good thinking.

> Is there a way to specify my model that flips these hypotheses?

*> Should I be using a different method? Is this even appropriate?
*

You could think about equivalence tests. See e.g. references in the equivalence package.

> 2) Growth Rate is confounded with Variance of Growth Rate

*> I'm also worried about the fact that rates for cultures with faster
**> growth are calculated using fewer data points (assuming similar
**> sampling times between treatments) . The result is that growth ~ var
**> (growth). Not only does this put a wrinkle in my analysis between
**> treatments, but it also biases the growth acclimation determining
**> ANCOVA test above. Faster growing cultures will usually pass the "no
**> significant difference between slopes test" more easily because there
**> are fewer points from which to be certain about rejecting Ho.
**>
**> Is there a way to control for this?
**> Perhaps I could include the number of points in my model?
*

Depending on the model that you apply, you might be able to explicitly model the variance to allow for this possibility. I would guess that it's not necessarily only the fewer data points contributing to the greater variation. Faster-growing cultures might also be inherently more variable.

> 3) Statistical validity of using subsets of growth.metric measurements

*> within a test tube
**> There are some lab members who insist that we can throw out the
**> beginning and end of our log transformed growth.metric measurements
**> because they are outliers in determining maximum growth. I've
**> proposed looping through all possible combinations of 3 or more points
**> within the growth curve and using the highest or best fitting (best R-
**> squared) slope. But this idea has been rejected by our PI as not be a
**> valid thing to do.
**>
**> Ideas here?
*

I'm feeling very cautious about giving advice on this question as I don't know enough about the area. Sorry.

I hope that this helps, otherwise.

Andrew

-- Andrew Robinson Department of Mathematics and Statistics Tel: +61-3-8344-6410 University of Melbourne, VIC 3010 Australia Fax: +61-3-8344-4599 http://www.ms.unimelb.edu.au/~andrewpr http://blogs.mbs.edu/fishing-in-the-bay/ ______________________________________________ R-help_at_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.Received on Sun 23 Nov 2008 - 09:56:10 GMT

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