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

Wed Feb 25 12:14:24 CET 2015

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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