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
2.5 30 22 31 35 10
5 7 9
...
50 0 0 0 0 0
1 0 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. But instead, I have counts.
I was thinking that this might be suitable for modeling using a
multinomial response. I could also just compare the distribution using
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. Any thoughts is greatly appreciated!
~Jimmie
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