[FLR-list] Use of FLQuantPoint

Iago Mosqueira iago.mosqueira at gmail.com
Thu Oct 20 13:18:09 CEST 2011 

Great,


Iago

On 20 October 2011 13:14, Mark Payne <mpa at aqua.dtu.dk> wrote:
> Much of the discussion moved off-list. We more or less decided that we could manage most things without having to involk FLQuantPoint. The consensus was to try and make things as accessible as possible within the FLSAM object so that whatever function needed the data could get access to it. So, no changes as far as I can see.
>
> Mark
>
>
> ________________________________________
> Fra: flr-list-bounces at r-forge.wu-wien.ac.at [flr-list-bounces at r-forge.wu-wien.ac.at] P&#229; vegne af Iago Mosqueira [iago.mosqueira at gmail.com]
> Sendt: 20. oktober 2011 13:12
> Til: flr-list at flr-project.org
> Emne: Re: [FLR-list] Use of FLQuantPoint
>
> Hi,
>
> Any kind of agreement on this discussion? Anything to get done on FLQuantPoint?
>
> Cheers,
>
>
> Iago
>
> On 10 October 2011 10:41, Laurie <lauriekell at googlemail.com> wrote:
>> What slots will you return?
>>
>> Since  fbar a weighted sum of harvest? and  ssb & tsb are weighted sums of
>> stock.n Then you will have slots with data for only harvest & stock.n? (and
>> maybe add wt & mat slots).
>>
>> If you return these as slots with the hessian (and/or covariance) matrix
>> then you can calculate anything else.
>>
>> I.e. stock.n will be a slot but rec an accessor.
>>
>> Laurie
>>
>>
>> On 10/10/2011 10:25 AM, Mark Payne wrote:
>>>
>>> Thanks for the comments and suggestions. I think I'm starting to see a way
>>> forward. It sounds like the general advice would be to base everything on
>>> accessor methods, and so long as everything is (somehow) accessible, then
>>> its fine. However, there are some limitations with, the ability of current
>>> classes to store  uncertainties that I need to live with (I'm not so excited
>>> about the idea of developing FLStock_with_uncertainties!), and therefore
>>> there can't be a one size fits all solution.
>>>
>>> How does this sound for a set of methods / solutions?
>>>
>>> 1. A set of standard accessor functions, that return simple (error-free)
>>> FLQuants in the same way as for other classes e.g
>>> stock.n()
>>> harvest()
>>> ssb()
>>> fbar()
>>> tsb()
>>> rec()
>>>
>>> 2. Having all the estimated parameters, with uncertainties, readily
>>> accessible through an FLPar object (?), so that any other object or method
>>> that wants access to them (e.g. a plot(FLSAM) method) can have at them. I
>>> will store the estimate and standard deviation only, so that people can go
>>> further with that if they like...
>>>
>>> Mark
>>>
>>>
>>> ________________________________________
>>> Fra: flr-list-bounces at r-forge.wu-wien.ac.at
>>> [flr-list-bounces at r-forge.wu-wien.ac.at] P&#229; vegne af Laurie
>>> [lauriekell at googlemail.com]
>>> Sendt: 10. oktober 2011 09:07
>>> Til: flr-list at flr-project.org
>>> Emne: Re: [FLR-list] Use of FLQuantPoint
>>>
>>> Looking at all the data.frame slots, these currently store value, std and
>>> up.bnd&  low.bnd
>>>
>>> library(FLSAM)
>>> data(NSH.sam)
>>>
>>>
>>> but it appears that a normal distribution is assumed when calculating the
>>> bnds, and that these are the 95th percentiles. However, depending on
>>> the application other percentiles might be more appropriate, but you can
>>> always calculate these if you know the mean&  std&  assumed distribution.
>>>
>>> with(ssb(   NSH.sam), (up.bnd-value)/std)
>>> with(logssb(NSH.sam), (up.bnd-value)/std)
>>> with(ssb(   NSH.sam), (value-low.bnd)/std)
>>> with(logssb(NSH.sam), (value-low.bnd)/std)
>>>
>>> So returning the bounds is not necessary and the bounds being returned
>>> might not be the correct ones. Therefore it would be better to just return
>>> the mean&  std and
>>> have a method to calculate any required statistic. There is also
>>> redundancy as you return logssb as well as ssb (same for tsb&  fbar etc).
>>>
>>> Also FLQuantPoint was designed to summarise emprical distributions, but
>>> ssb etc come from an assumed distribution.
>>>
>>> Also if you know wt, mat stock.n&  harvest then you know ssb fbar etc. So
>>> agian there is redundancy.
>>>
>>> How does fbar relate to harvest, presumably each F has a std&  variance
>>> and fbar is function of these.
>>>
>>> Laurie
>>>
>>>
>>>
>>> On 10/08/2011 01:55 PM, Mark Payne wrote:
>>>
>>> Ok, that makes sense, but I'm still a little unsure about how this should
>>> be implemented - as I understand it you're talking mainly about how to store
>>> the data, rather than pass it around. I currently have an array of accessor
>>> functions:
>>>
>>> ssb(sam.object)
>>> tsb(sam.object)
>>> fbar(sam.object)
>>> stock.n(sam.object)
>>> harvest(sam.object)
>>> rec(sam.object)
>>>
>>> What sort of objects should these return, given that these quantites now
>>> can all have confidence intervals associated with them?
>>>
>>> Mark
>>>
>>> ________________________________________
>>> Fra:
>>> flr-list-bounces at r-forge.wu-wien.ac.at<mailto:flr-list-bounces at r-forge.wu-wien.ac.at>
>>>  [flr-list-bounces at r-forge.wu-wien.ac.at<mailto:flr-list-bounces at r-forge.wu-wien.ac.at>]
>>> P&#229; vegne af Laurie
>>> [lauriekell at googlemail.com<mailto:lauriekell at googlemail.com>]
>>> Sendt: 8. oktober 2011 12:18
>>> Til: flr-list at flr-project.org<mailto:flr-list at flr-project.org>
>>> Emne: Re: [FLR-list] Use of FLQuantPoint
>>>
>>> On 10/08/2011 11:47 AM, Mark Payne wrote:
>>>
>>>
>>> Hi,
>>>
>>> I am a bit confused about the intended use of FLQuantPoints. The FLSAM
>>> assessment model returns the confidence intervals for nearly everything that
>>> it calculates, including, for example, the ssb. I am trying to write an
>>> accessor function that will return the ssb as an FLQuantPoint object. The
>>> problem is that the confidence intervals for this value are asymmetric, so
>>> it's not sufficient just to return the mean and the variance. I was
>>> wondering what the intention for the uppq and lowq dimension was? Is this
>>> the appropriate place to store the estimated confidence bounds? Or are they
>>> solely for cases where you have a large distribution that you are trying to
>>> characteriste non-parametrically ie through median and the quartiles...
>>>
>>> Second question - is there a plural class, FLQuantPoints? I can't seem to
>>> find one at the moment....
>>>
>>> Mark
>>>
>>>
>>>
>>> You say ssb has an asymmetric distribution but that only matters  if it
>>> is from an empirical distribution, e.g. derived from an MC simulation.
>>> When I had a quick look at ssb, the upper&  lower CIs were just +-1.96
>>> times the CV. I.e. if you know the distribution and parameters you can
>>> derive the CIs.
>>> Also for what percentiles do you want the CIs? this will vary on a
>>> case-by-case basis.
>>>
>>> FLQuantPoint was designed to summarise the 6th dim (iter) to reduce
>>> storage space. If you are not running MC simulations then there is no
>>> need for FLQuantPoint.
>>>
>>> Instead if you return the parameters and hessian as FLPar objects then
>>> you can design methods to calculate quantities of interest.
>>>
>>> Laurie
>>>
>>>
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>>>
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>>
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