On Thu, 30 Jun 2011, Chun (Jimmie) Ye wrote:
Hi all, I have a rather peculiar dataset that I'm not sure how to model properly. This is data from an instrument that measures the size of particles but instead of giving a continuous value, it generates a "histogram" of the counts for a particular bin size. So the data looks like this: Condition 1 Condition 2 Dimension Rep1.A Rep1.B Rep2.A Rep2.B Rep1.A Rep1.B Rep2.A Rep2.B 2 5 6 7 8 40 35 33 31 2.1 7 8 4 5 30 30 31 29 2.2 10 11 10 12 20 18 21 20 2.3 50 45 44 39 5 8 7 7 2.4 80 90 75 77 8 10 3 5 3.5 30 22 31 35 10 5 7 9 ? 50 0 0 0 0 0 0 1 0
There are two biological replicates and two technical replicates for each of the conditions of interest. If these were continuous, I would just fit a nested mixed model to the data.
[...] a KS test but I'm not sure how to incorporate the replicate information (except for averaging). Finally, I thought perhaps I could estimate directly from the histogram the variance components from the data.
So each single biological sample gives rise to a distribution of particle sizes? Are you interested in central tendency, peak height (ie number of particles), shape of distribution? I would have thought treating it as a coarsened continuous measure would make best use of the the data (like for coarsened Gaussian, AS319; Wolynetz Appl Stat 1979 28:195-206). Just 2c, David Duffy.
| David Duffy (MBBS PhD) ,-_|\ | email: davidD at qimr.edu.au ph: INT+61+7+3362-0217 fax: -0101 / * | Epidemiology Unit, Queensland Institute of Medical Research \_,-._/ | 300 Herston Rd, Brisbane, Queensland 4029, Australia GPG 4D0B994A v