simulate-methods.Rd
Simulation methods for a4a stock assessment fits.
simulate(object, nsim = 1, seed = NULL, ...)
# S4 method for a4aFitSA
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)
# S4 method for SCAPars
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)
# S4 method for a4aStkParams
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)
# S4 method for submodels
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)
# S4 method for submodel
simulate(object, nsim = 1, seed = NULL, empirical = TRUE)
object of relevant class (see signature of method)
number of iterations
numeric
with random number seed
additional argument list that might never be used
logical, shall the empirical method in MASS be used
data(ple4)
data(ple4.index)
fmodel <- ~factor(age) + factor(year)
qmodel <- list(~factor(age))
fit1 <- sca(fmodel=fmodel, qmodel=qmodel, stock=ple4, indices=FLIndices(ple4.index))
fit1
#> a4a model fit for: PLE
#>
#> Call:
#> .local(stock = stock, indices = indices, fmodel = ..1, qmodel = ..2)
#>
#> Time used:
#> Pre-processing Running a4a Post-processing Total
#> 0.5536301 5.1830571 0.3269377 6.0636249
#>
#> Submodels:
#> fmodel: ~factor(age) + factor(year)
#> srmodel: ~factor(year)
#> n1model: ~s(age, k = 3)
#> qmodel:
#> BTS-Combined (all): ~factor(age)
#> vmodel:
#> catch: ~s(age, k = 3)
#> BTS-Combined (all): ~1
summary(fit1)
#> An object of class "a4aFitSA"
#>
#> Name: PLE
#> Description: Plaice in IV. ICES WGNSSK 2018. FLAAP
#> Quant: age
#> Dims: age year unit season area iter
#> 10 61 1 1 1 1
#>
#> Range: min max pgroup minyear maxyear minfbar maxfbar
#> 1 10 10 1957 2017 2 6
#>
stock.n(fit1)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963
#> 1 473837.5 701921.4 859864.3 780079.5 841326.2 596229.5 620993.3
#> 2 345321.1 389772.0 567538.2 685313.8 618715.5 668173.6 472960.5
#> 3 248715.7 259057.7 282673.0 400116.3 478558.1 433175.2 466717.5
#> 4 178376.5 173026.4 171865.5 180238.7 251730.4 302178.4 272633.5
#> 5 126843.5 123614.9 114268.3 109023.5 112792.2 158114.0 189175.2
#> 6 89158.0 87977.7 81719.0 72569.2 68307.1 70929.0 99102.6
#> 7 61879.4 62217.1 58579.8 52320.3 45851.5 43314.1 44832.1
#> 8 42451.5 44470.9 42891.8 39004.4 34430.2 30269.6 28513.1
#> 9 28875.9 31531.6 31874.8 29840.7 26864.8 23779.3 20854.7
#> 10 19558.3 34689.0 44400.9 48589.5 48856.7 47044.8 43647.8
#> year
#> age 1964 1965 1966 1967 1968 1969 1970
#> 1 2640249.1 728826.9 610747.4 421925.0 386388.6 596409.0 603691.1
#> 2 487964.7 2057492.6 569303.7 482262.9 335494.9 306974.3 471849.1
#> 3 324267.8 329133.2 1394248.1 394093.3 338444.2 235047.6 213303.4
#> 4 286171.9 194324.5 198529.7 866503.9 249672.6 213911.8 146855.7
#> 5 166215.2 170460.0 116518.9 122704.0 546097.1 156975.1 132925.6
#> 6 115480.4 99140.5 102345.1 72104.4 77422.1 343746.6 97663.0
#> 7 61043.8 69539.5 60086.0 63886.9 45872.6 49140.9 215705.7
#> 8 28844.7 38494.7 44103.3 39118.4 42299.0 30309.3 32142.0
#> 9 19263.1 19154.7 25688.3 30098.9 27084.7 29234.9 20767.5
#> 10 38559.8 33844.4 31478.9 35794.9 42101.6 43415.4 45189.6
#> year
#> age 1971 1972 1973 1974 1975 1976 1977
#> 1 399389.9 348898.5 1345588.8 1017694.4 698844.7 548565.9 778479.0
#> 2 476060.3 313061.0 269546.7 1019844.8 763211.4 527449.0 417317.3
#> 3 325778.7 324817.8 207597.0 172138.4 637884.6 483394.5 339318.3
#> 4 132079.6 198404.9 190062.3 115225.7 92797.1 349978.1 271076.9
#> 5 90429.5 79972.6 115352.5 104738.7 61645.5 50540.6 194885.4
#> 6 81953.2 54824.8 46562.3 63669.5 56129.8 33629.4 28187.6
#> 7 60743.6 50143.5 32244.4 25992.9 34532.4 30977.3 18964.8
#> 8 139983.1 38850.7 30966.0 19012.3 14939.3 20155.7 18430.3
#> 9 21875.3 94090.8 25341.1 19413.2 11660.9 9284.6 12733.9
#> 10 39748.0 36896.5 83504.0 56853.4 38890.7 26049.6 18859.1
#> year
#> age 1978 1979 1980 1981 1982 1983 1984
#> 1 640981.5 704057.4 924265.8 931736.0 1847651.7 1298486.6 1206091.3
#> 2 589503.1 478537.9 518557.6 676092.5 680952.0 1357953.3 959194.4
#> 3 266047.8 365468.5 288875.8 308838.2 401960.0 409349.2 824510.2
#> 4 187879.2 141651.4 187449.9 145387.4 155054.4 204959.7 211670.2
#> 5 149016.0 99256.3 72050.6 93531.9 72364.0 78399.3 105115.3
#> 6 107304.2 78861.5 50579.9 36019.8 46643.7 36657.7 40281.4
#> 7 15697.7 57488.7 40718.7 25632.1 18209.5 23944.3 19080.2
#> 8 11158.4 8924.5 31630.8 22035.4 13841.4 9967.7 13268.6
#> 9 11533.3 6780.3 5272.9 18424.8 12811.9 8141.9 5925.3
#> 10 17450.2 15379.8 10934.2 7904.4 14797.2 14409.6 11466.4
#> year
#> age 1985 1986 1987 1988 1989 1990 1991
#> 1 1644325.8 3960903.4 1860346.2 1839952.8 1352882.5 1185561.9 1052307.5
#> 2 889539.7 1201509.6 2858362.1 1326879.9 1304527.1 964010.6 852854.3
#> 3 580594.4 528658.8 696763.7 1619851.5 743177.0 737889.1 555573.1
#> 4 424498.1 291336.3 256305.2 327069.5 747967.1 347934.7 354640.8
#> 5 108079.3 211172.1 139957.4 119159.1 149539.9 346805.9 165676.3
#> 6 53771.2 53869.0 101654.0 65207.3 54600.4 69485.6 165479.5
#> 7 20876.5 27169.1 26308.9 48086.5 30348.1 25760.9 33644.7
#> 8 10533.1 11268.1 14229.0 13394.0 24130.6 15414.6 13388.9
#> 9 7861.8 6121.7 6381.9 7865.9 7313.3 13313.0 8673.5
#> 10 8734.6 8566.4 7173.0 6573.5 7125.1 7093.9 10882.6
#> year
#> age 1992 1993 1994 1995 1996 1997 1998
#> 1 916723.2 617155.5 637103.1 996785.8 978696.3 3186325.9 1127018.6
#> 2 758826.6 655004.5 436955.2 451386.3 705623.2 680833.4 2157479.8
#> 3 493854.6 431531.3 365862.1 244393.9 252047.4 380718.6 348328.3
#> 4 268801.7 232956.1 198496.1 168605.6 112366.4 110439.1 154835.0
#> 5 170014.9 125589.4 106095.9 90573.9 76753.8 48713.7 44390.4
#> 6 79587.7 79603.3 57324.0 48518.3 41323.0 33353.8 19631.4
#> 7 80653.9 37830.5 36908.1 26627.5 22485.6 18261.2 13692.6
#> 8 17588.8 41237.7 18920.2 18489.2 13312.0 10777.0 8198.9
#> 9 7571.4 9758.9 22451.4 10315.5 10062.9 6988.0 5349.3
#> 10 9639.8 8272.2 8854.4 16571.6 12294.4 10081.8 7033.0
#> year
#> age 1999 2000 2001 2002 2003 2004 2005
#> 1 918089.3 964100.3 610284.4 1693795.5 509232.1 992838.0 608790.9
#> 2 754317.2 626356.5 675855.1 429739.7 1176824.0 351801.5 698940.2
#> 3 1078941.0 391699.7 343095.4 373477.0 231295.7 626359.4 194310.3
#> 4 137206.3 448037.6 175308.3 155459.3 163083.8 99427.9 283611.9
#> 5 60249.3 56327.8 198459.6 78630.8 67160.9 69343.2 44565.0
#> 6 17320.1 24797.4 25008.1 89216.5 34050.8 28626.3 31151.0
#> 7 7808.7 7258.3 11190.3 11423.7 39292.1 14765.9 13067.6
#> 8 5977.9 3570.5 3544.1 5523.3 5458.7 18519.3 7283.7
#> 9 3973.1 3014.0 1904.4 1907.8 2891.9 2824.7 9964.2
#> 10 5014.6 3841.9 3137.1 2266.4 1882.7 2279.1 2485.7
#> year
#> age 2006 2007 2008 2009 2010 2011 2012
#> 1 624075.8 1071216.1 918743.4 875218.7 1202849.7 1502023.8 1354187.8
#> 2 440434.2 461420.9 804426.5 700679.7 678462.9 943987.7 1187249.3
#> 3 407329.0 267893.5 289367.0 520051.1 467747.9 464014.2 654770.0
#> 4 94860.4 211148.3 144951.2 163392.9 307162.4 285811.3 289187.0
#> 5 137207.5 48772.7 113388.4 81282.9 95903.8 186610.0 177153.4
#> 6 21603.5 70674.0 26235.3 63681.7 47775.6 58339.0 115806.9
#> 7 15318.2 11272.2 38471.6 14896.2 37801.8 29327.5 36517.9
#> 8 6885.4 8507.0 6499.6 23027.9 9274.9 24247.7 19140.5
#> 9 4146.8 4100.7 5232.1 4127.6 15126.4 6249.8 16583.0
#> 10 7102.7 5750.8 5344.8 6130.1 6035.5 13946.6 12237.0
#> year
#> age 2013 2014 2015 2016 2017
#> 1 1658681.6 1961517.1 1171848.6 1687998.2 2283110.5
#> 2 1075766.0 1329901.6 1594711.4 969046.7 1421171.0
#> 3 831644.7 767387.9 974951.1 1208820.7 760968.3
#> 4 413742.8 539092.8 516870.2 688193.6 896622.1
#> 5 181773.6 266888.0 361532.2 363517.7 508965.9
#> 6 111483.6 117382.3 179156.3 254474.5 269022.0
#> 7 73486.2 72550.1 79336.7 126831.0 189192.6
#> 8 24123.2 49641.9 50683.0 57748.6 96416.1
#> 9 13226.6 16992.3 35988.0 38059.1 45008.7
#> 10 18891.1 20766.3 25551.8 44558.3 61496.2
#>
#> units: 1000