Matching fish condition to market categories

[mle2718]

It's unclear how to best match fish condtion to market categories.

One approach:

1. Use the CFLEN data to construct a length-frequency distribution for a market category. (A probability mass function that describes the probability of a landed fish measuring X cm, conditional on it being labeled as the m-th market category).
2. Construct an interval based on that distribution. Like, use the 25th through 75th percentile -- and then average over that.

Another Approach:

Use the probability masses from CFLEN to weight each of the condition factors.

• If 2% of the observed fish in the 5091 market category are 15cm, then we weight the condition factor contribution of the 15cm fish to be 2% of the total.
• This is probably going to be a little messy, because there probably will be gaps/holes both the CFLEN and condition data.

[Laurels1]

I'm not sure which would be best. Happy to ask a couple stock assessment leads for advice on how it's done in assessments if that's helpful. A couple issues that I foresee are 1) changes over time of shifting/emerging/disappearing market categories (would either of these be done by year?) and 2) overlap (sometimes nearly total) between market categories (would either of these methods deal with this situation better?)

[mle2718]

1. I can code this to be done by year. Just a question of whether the data is too sparse.
2. I don't think this is a big problem with assembling the data -- but it suggests that linking to the market category will not give us as much extra variation in the 'condition factor' variable as we were hoping for.

Leaning towards the 1st approach because it probably will hold up a little better any sparseness.