other possible, non-influential predictors

[mcmelnychuk]

Just throwing this out there: what are your thoughts about including a couple additional numerical predictors in the analysis, such as:

• trophic level
• year of fishery development.
  I wouldn't expect these to have a strong influence, and there could even be some confounding with maximum body size, but I'm just wondering if there might be some value in showing what other variables are not influential, so as to highlight (by comparison) the variables that are (max landings, price).

[James-Thorson]

Trophic level is predicted to be strongly associated with max length under
size structure theory, and I doubt it's well measure for all our stocks.
Year of first development, hard to say, but might be with the acceleration
rate.

If rather keep the existing analysis as is, unless you have a strong
opinion Phil

On Nov 19, 2016 12:16 PM, "Michael Melnychuk" notifications@github.com
wrote:

Just throwing this out there: what are your thoughts about including a
couple additional numerical predictors in the analysis, such as:

• trophic level
• year of fishery development.
  I wouldn't expect these to have a strong influence, and there could
  even be some confounding with maximum body size, but I'm just wondering if
  there might be some value in showing what other variables are not
  influential, so as to highlight (by comparison) the variables that are (max
  landings, price).

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[Philipp-Neubauer]

Hi Mike;

I didn't include TL since i) its not as readily available as size info, so
would need to use taxonomic imputation to get it for quite a few taxa and
ii) its strongly correlated with size. Since the latter doesn't come out as
a strong predictor, it may siluffice to reference the strong correlation of
size with TL to suggest that TL would have a similarly small influence. I'
d say its worth discussing in the context of Sethi et al....

I like the idea of having year of fishery dev in there - not sure if it'll
work since it also enters in the time-to-assessment calculation. Doesn't
cost much to give that a quick try...

On 20/11/2016 9:16 AM, "Michael Melnychuk" notifications@github.com wrote:

Just throwing this out there: what are your thoughts about including a
couple additional numerical predictors in the analysis, such as:

• trophic level
• year of fishery development.
  I wouldn't expect these to have a strong influence, and there could
  even be some confounding with maximum body size, but I'm just wondering if
  there might be some value in showing what other variables are not
  influential, so as to highlight (by comparison) the variables that are (max
  landings, price).

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[mcmelnychuk]

Sure, no problem at all leaving the analysis as is, especially if year of development is already part of the calculation. To clarify, is the "start time" of the time-to-event analysis the year of development (as Sethi et al use it, year when landings reached 25% of max historical landings), or is it the year of first recorded landings?

[Philipp-Neubauer]

I think the first year could be informative since it gives a more
straightforward way of talking about assessment probabilities for old vs
newly landed stocks. I'll give it a go to see what happens.

The development year is the year that species first appear in the landings
DB.

On 20/11/2016 10:05 AM, "Michael Melnychuk" notifications@github.com
wrote:

Sure, no problem at all leaving the analysis as is, especially if year of
development is already part of the calculation. To clarify, is the "start
time" of the time-to-event analysis the year of development (as Sethi et al
use it, year when landings reached 25% of max historical landings), or is
it the year of first recorded landings?

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[Philipp-Neubauer]

A thought - I tried year of fishery development as a predictor and it is HIGHLY influential, much more so than anything else. But it also screws up the model and leads to unrealistic predicitons.

I think the effect is due to a "technology effect" - i.e., prior to some date, it was simply impossible to have a model that would fit our definition of assessment. So for all stocks that were first landed prior to that date, there is a mandatory waiting period...

Since the positive effect means the model assumes a linear increase in assessment probability with year of first landing, all recently landed but un-assessed stocks will have a near 1 probability of being assessed very soon - not very realistic I'd say for small stocks. So my feeling is that we'd need to have a non-linear effect OR have a binary variable for pre- and post "first ever stock assessment" instead of just a linear effect with year. I think the technology effect is probably something we need to account for in some way...

Hope this makes sense...

[mcmelnychuk]

Apologies if I opened up a can of worms with that suggestion. The other
predictors we have thus far can legitimately be seen as independent,
whereas this one is probably more correlative (when landings and price
of some species pass some threshold, they begin to be recorded, and
sometime thereafter they might be assessed). If inclusion of this
predictor is screwing up the model and predictions, should we just omit
it given that it's not really independent anyway? Either way I'm happy
to defer to your judgement about changing it to include a technology
effect. It looks like the first assessment was in 1960, so that only
leaves 10 years between the censored start of landings data and the
first assessment.

Mike

On 2016-11-21 12:42 PM, Philipp Neubauer wrote:

A thought - I tried year of fishery development as a predictor and it
is HIGHLY influential, much more so than anything else. But it also
screws up the model and leads to unrealistic predicitons.

I think the effect is due to a "technology effect" - i.e., prior to
some date, it was simply impossible to have a model that would fit our
definition of assessment. So for all stocks that were first landed
prior to that date, there is a mandatory waiting period...

Since the positive effect means the model assumes a linear increase in
assessment probability with year of first landing, all recently landed
but un-assessed stocks will have a near 1 probability of being
assessed very soon - not very realistic I'd say for small stocks. So
my feeling is that we'd need to have a non-linear effect OR have a
binary variable for pre- and post "first ever stock assessment"
instead of just a linear effect with year. I think the technology
effect is probably something we need to account for in some way...

Hope this makes sense...

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[Philipp-Neubauer]

Hmm, maybe the time-to-assessment should really be assessment_year -
max(1960,first_landing) then...taht would settle it without having to have
a predictor...

On Tue, Nov 22, 2016 at 10:16 AM, Michael Melnychuk <
notifications@github.com> wrote:

Apologies if I opened up a can of worms with that suggestion. The other
predictors we have thus far can legitimately be seen as independent,
whereas this one is probably more correlative (when landings and price
of some species pass some threshold, they begin to be recorded, and
sometime thereafter they might be assessed). If inclusion of this
predictor is screwing up the model and predictions, should we just omit
it given that it's not really independent anyway? Either way I'm happy
to defer to your judgement about changing it to include a technology
effect. It looks like the first assessment was in 1960, so that only
leaves 10 years between the censored start of landings data and the
first assessment.

Mike

On 2016-11-21 12:42 PM, Philipp Neubauer wrote:

A thought - I tried year of fishery development as a predictor and it
is HIGHLY influential, much more so than anything else. But it also
screws up the model and leads to unrealistic predicitons.

I think the effect is due to a "technology effect" - i.e., prior to
some date, it was simply impossible to have a model that would fit our
definition of assessment. So for all stocks that were first landed
prior to that date, there is a mandatory waiting period...

Since the positive effect means the model assumes a linear increase in
assessment probability with year of first landing, all recently landed
but un-assessed stocks will have a near 1 probability of being
assessed very soon - not very realistic I'd say for small stocks. So
my feeling is that we'd need to have a non-linear effect OR have a
binary variable for pre- and post "first ever stock assessment"
instead of just a linear effect with year. I think the technology
effect is probably something we need to account for in some way...

Hope this makes sense...

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Phil

[Philipp-Neubauer]

I just pushed an updated model version (need to sync my dropbox file, so it
won't have updated for you guys yet). The updated version has

i) the time to assessment calculated as per my last comment. I think the
technology effect was a legitimate concern, and by taking 1960 as the start
year, I took the easy way to eliminate that concern.

ii) I added the interaction term of landings*price - at first only out of
curiosity, but it seems that a) it leads to a positive effect for length
(i.e. faster assessment for larger species), which I think is interesting,
b) Is negative, suggesting the price matters most when landings are low,
and c) it provides more clarity for some of the random effects (e.g.,
Gadidae are now found to have slightly higher than average assessment
rates). Those are all pretty interesting results, I thought, so I thought
it is probably worth keeping that term in...

On Tue, Nov 22, 2016 at 10:20 AM, Philipp Neubauer neubauer.phil@gmail.com
wrote:

Hmm, maybe the time-to-assessment should really be assessment_year -
max(1960,first_landing) then...taht would settle it without having to have
a predictor...

On Tue, Nov 22, 2016 at 10:16 AM, Michael Melnychuk <
notifications@github.com> wrote:

Apologies if I opened up a can of worms with that suggestion. The other
predictors we have thus far can legitimately be seen as independent,
whereas this one is probably more correlative (when landings and price
of some species pass some threshold, they begin to be recorded, and
sometime thereafter they might be assessed). If inclusion of this
predictor is screwing up the model and predictions, should we just omit
it given that it's not really independent anyway? Either way I'm happy
to defer to your judgement about changing it to include a technology
effect. It looks like the first assessment was in 1960, so that only
leaves 10 years between the censored start of landings data and the
first assessment.

Mike

On 2016-11-21 12:42 PM, Philipp Neubauer wrote:

A thought - I tried year of fishery development as a predictor and it
is HIGHLY influential, much more so than anything else. But it also
screws up the model and leads to unrealistic predicitons.

I think the effect is due to a "technology effect" - i.e., prior to
some date, it was simply impossible to have a model that would fit our
definition of assessment. So for all stocks that were first landed
prior to that date, there is a mandatory waiting period...

Since the positive effect means the model assumes a linear increase in
assessment probability with year of first landing, all recently landed
but un-assessed stocks will have a near 1 probability of being
assessed very soon - not very realistic I'd say for small stocks. So
my feeling is that we'd need to have a non-linear effect OR have a
binary variable for pre- and post "first ever stock assessment"
instead of just a linear effect with year. I think the technology
effect is probably something we need to account for in some way...

Hope this makes sense...

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Phil

Phil

[mcmelnychuk]

thanks, and sounds good - looking forward to going through it again.

Mike

On 2016-11-23 1:19 AM, Philipp Neubauer wrote:

I just pushed an updated model version (need to sync my dropbox file,
so it
won't have updated for you guys yet). The updated version has

i) the time to assessment calculated as per my last comment. I think the
technology effect was a legitimate concern, and by taking 1960 as the
start
year, I took the easy way to eliminate that concern.

ii) I added the interaction term of landings*price - at first only out of
curiosity, but it seems that a) it leads to a positive effect for length
(i.e. faster assessment for larger species), which I think is interesting,
b) Is negative, suggesting the price matters most when landings are low,
and c) it provides more clarity for some of the random effects (e.g.,
Gadidae are now found to have slightly higher than average assessment
rates). Those are all pretty interesting results, I thought, so I thought
it is probably worth keeping that term in...

On Tue, Nov 22, 2016 at 10:20 AM, Philipp Neubauer
neubauer.phil@gmail.com
wrote:

Hmm, maybe the time-to-assessment should really be assessment_year -
max(1960,first_landing) then...taht would settle it without having
to have
a predictor...

On Tue, Nov 22, 2016 at 10:16 AM, Michael Melnychuk <
notifications@github.com> wrote:

Apologies if I opened up a can of worms with that suggestion. The other
predictors we have thus far can legitimately be seen as independent,
whereas this one is probably more correlative (when landings and price
of some species pass some threshold, they begin to be recorded, and
sometime thereafter they might be assessed). If inclusion of this
predictor is screwing up the model and predictions, should we just omit
it given that it's not really independent anyway? Either way I'm happy
to defer to your judgement about changing it to include a technology
effect. It looks like the first assessment was in 1960, so that only
leaves 10 years between the censored start of landings data and the
first assessment.

Mike

On 2016-11-21 12:42 PM, Philipp Neubauer wrote:

A thought - I tried year of fishery development as a predictor and it
is HIGHLY influential, much more so than anything else. But it also
screws up the model and leads to unrealistic predicitons.

I think the effect is due to a "technology effect" - i.e., prior to
some date, it was simply impossible to have a model that would
fit our
definition of assessment. So for all stocks that were first landed
prior to that date, there is a mandatory waiting period...

Since the positive effect means the model assumes a linear
increase in
assessment probability with year of first landing, all recently
landed
but un-assessed stocks will have a near 1 probability of being
assessed very soon - not very realistic I'd say for small stocks. So
my feeling is that we'd need to have a non-linear effect OR have a
binary variable for pre- and post "first ever stock assessment"
instead of just a linear effect with year. I think the technology
effect is probably something we need to account for in some way...

Hope this makes sense...

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Phil

Phil

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[Philipp-Neubauer]

My take on this as of today:

• Leave out the pre- vs post 1996 comparison; even though the rate post-1996 is lower than pre-1996, both are estimated to be >1 (i.e., increasing assessment rates), so not sure how important this is to mention.

• Add a paragraph in the discussion about the length effect.

[mcmelnychuk]

Sounds good! Mike