[FLR-list] Error in a4aout at harvest + m(stock) : non-con

Wed Feb 25 13:17:54 CET 2015

Hi,

You can get the technical document from github 
https://github.com/a4a/tech-doc/blob/master/a4aAssessmentMethodology.pdf?raw=true 
.

Best

EJ

On 02/25/2015 12:14 PM, Luis Ridao wrote:
> Hi Ernesto,
>
> Thanks for your useful response.
> There was never any intention on replicating the XSA but given the 
> huge disparity
> in both the XSA and a4a outputs I was wondering why it was so.
>
> A statistical model sounds more sound than XSA but the problem with 
> a4a is that there is no much documentation to see upon (or maybe it's 
> me who can't find it)
>
> Your suggestion on including a year trend in the catchability of the 
> trawl cpue for example sounds good. The problem is how to implement it.
>
> Thanks again,
> Luis
>
> On 02/24/2015 09:01 AM, Ernesto wrote:
>> Hi,
>>
>> Sorry for the late reply.
>>
>> The problem is that your catch data has a large number of ages that 
>> don't have tunning data. The default model for a4a was not written 
>> for those cases. Check
>>
>> a4afit <- sca(gul0001, gul.indices)
>> wireframe(data~year+age, data=harvest(a4afit))
>>
>> F in the last ages gets loose and the fit is quite poor.
>>
>> Once that you want to compare with XSA we can take the same kind of 
>> approach, which is to force the oldest ages Fs the same. In this case 
>> the model will fit one coefficient (times the year coefficients) for 
>> ages older than 18, which mean they are fit together, which I think 
>> is slightly different from XSA. Note that you have a large +group in 
>> some years.
>>
>> This can be done using the "replace" method.
>>
>> fmod <- ~te(replace(age, age>18, 18), year, k = c(6, 10), bs = "tp")
>> a4afit <- sca(gul0001, gul.indices, fmodel=fmod)
>>
>> For comparison
>> xsafit <- FLXSA(gul0001, gul.indices, FLXSA.control())
>>
>> wireframe(data~year+age|qname, 
>> data=as.data.frame(FLQuants(a4a=harvest(a4afit), xsa=xsafit at harvest)))
>>
>> Now, the one million dollars question is why you want to replicate XSA ;)
>>
>> Best
>>
>> EJ
>>
>> ps: Take a look at the residuals and you'll see that both fits have 
>> some odd residuals. In a4a you have a couple of simple options to 
>> improve this fit, like including a year trend in the catchability of 
>> the trawl cpue, etc.
>>
>> bubbles(age~year|qname, data=xsafit at index.res)
>> plot(residuals(a4afit, gul0001, gul.indices))
>>
>> On 02/18/2015 02:35 PM, Havstovan FAMRI wrote:
>>> Hi,
>>>
>>> Well that solved the problem (trimming the indices object 
>>> trim(gul.indices[[2]], age 4:12). No errors come up but stock 
>>> numbers and F's are really spurious
>>> and nothing in the range of the XSA run. F are given below as a example:
>>>
>>> > gulfit at harvest[,ac(2010:2014)]  # a4a output
>>> An object of class "FLQuant"
>>> , , unit = unique, season = all, area = unique
>>>
>>>     year
>>> age  2010       2011       2012       2013 2014
>>>   4  0.00061883 0.00054841 0.00088312 0.00104010 0.00070947
>>>   5  0.00194001 0.00170594 0.00221326 0.00254813 0.00223464
>>>   6  0.00515556 0.00454088 0.00496769 0.00561751 0.00602334
>>>   7  0.01007180 0.00901793 0.00900872 0.01006750 0.01202780
>>>   8  0.01384780 0.01273870 0.01257350 0.01389800 0.01666770
>>>   9  0.01448220 0.01365150 0.01385510 0.01499370 0.01669910
>>>   10 0.01336920 0.01269240 0.01303890 0.01351880 0.01358130
>>>   11 0.01248430 0.01165750 0.01145380 0.01107690 0.01019620
>>>   12 0.01247860 0.01127330 0.00997400 0.00880635 0.00769486
>>>   13 0.01285800 0.01125750 0.00874288 0.00700203 0.00595635
>>>   14 0.01248820 0.01082000 0.00757566 0.00557152 0.00460538
>>>   15 0.01064480 0.00939845 0.00633618 0.00439713 0.00344647
>>>   16 0.00797521 0.00732193 0.00509501 0.00344610 0.00249670
>>>   17 0.00569563 0.00541743 0.00403299 0.00273092 0.00182383
>>>   18 0.00435670 0.00416232 0.00326465 0.00224600 0.00142545
>>>   19 0.00388555 0.00355594 0.00278197 0.00194974 0.00124088
>>>   20 0.00406418 0.00340829 0.00250123 0.00177887 0.00119688
>>>   21 0.00465988 0.00349410 0.00231912 0.00166745 0.00122197
>>>
>>> units:  f
>>> > gul_F[,ac(2010:2014)]  # XSA output
>>>      2010   2011   2012   2013   2014
>>> 4  0.0047 0.0115 0.0015 0.0066 0.0053
>>> 5  0.0143 0.0311 0.0100 0.0154 0.0296
>>> 6  0.0675 0.0787 0.0485 0.0655 0.0872
>>> 7  0.1257 0.1506 0.0947 0.1182 0.1404
>>> 8  0.1696 0.2211 0.1632 0.1897 0.2082
>>> 9  0.2107 0.2428 0.1669 0.2651 0.2607
>>> 10 0.2624 0.3209 0.2170 0.2783 0.2700
>>> 11 0.2686 0.3086 0.2396 0.3374 0.2840
>>> 12 0.3309 0.4213 0.2750 0.3371 0.2146
>>> 13 0.4980 0.6288 0.4110 0.4805 0.2861
>>> 14 0.4428 0.5557 0.4666 0.5562 0.3298
>>> 15 0.4462 0.5266 0.4679 0.6606 0.4605
>>> 16 0.3444 0.4555 0.4277 0.5946 0.4433
>>> 17 0.2812 0.3540 0.3561 0.5347 0.3910
>>> 18 0.2529 0.3157 0.3512 0.4841 0.3601
>>> 19 0.4566 0.2772 0.1803 0.2773 0.3215
>>> 20 0.2534 0.3124 0.2804 0.4170 0.4099
>>> 21 0.2534 0.3124 0.2804 0.4170 0.4099
>>>
>>> best,
>>> Luis
>>>
>>>
>>>
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>>
>>
>> -- 
>> Ernesto Jardim<ernesto.jardim at jrc.ec.europa.eu>
>> Fisheries Scientist
>> FISHREG – Scientific Support to Fisheries
>> IPSC Maritime Affairs Unit
>> EC Joint Research Center
>> TP 051, Via Enrico Fermi 2749
>> I-21027 Ispra (VA), Italy
>> Office : +39 0332 785311
>> Fax: +39 0332 789658
>> https://fishreg.jrc.ec.europa.eu/
>>
>>
>>
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>
>
>
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-- 
Ernesto Jardim<ernesto.jardim at jrc.ec.europa.eu>
Fisheries Scientist
FISHREG – Scientific Support to Fisheries
IPSC Maritime Affairs Unit
EC Joint Research Center
TP 051, Via Enrico Fermi 2749
I-21027 Ispra (VA), Italy
Office : +39 0332 785311
Fax: +39 0332 789658
https://fishreg.jrc.ec.europa.eu/

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