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@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.
