FLCatch.RdMake an FLCatch object.
Catch data for a single species or stock unit is handled by the
FLCatch class. Data is separated as landings and discards by age, in
numbers, with the corresponding mean weights at age.
FLCatch(object, ...)
catch.sel(object, ...) <- value
discards.ratio(object, ...)
lrevenue(object, ...)
# S4 method for FLCatch
landings.n(object)
# S4 method for FLCatch,numeric
landings.n(object) <- value
# S4 method for FLCatch,numeric
landings.wt(object) <- value
# S4 method for FLCatch,numeric
discards.n(object) <- value
# S4 method for FLCatch,numeric
discards.wt(object) <- value
# S4 method for FLCatch,numeric
catch.sel(object) <- value
# S4 method for FLCatch,numeric
price(object) <- value
# S4 method for FLCatch,numeric
catch.q(object) <- value
# S4 method for FLCatch
landings(object)
# S4 method for FLCatch
discards(object)
# S4 method for FLCatch
catch(object)
# S4 method for FLCatch
catch.n(object)
# S4 method for FLCatch
catch.wt(object)
# S4 method for FLQuant
FLCatch(object, ...)
# S4 method for missing
FLCatch(object, ...)
# S4 method for FLCatch
lrevenue(object)
# S4 method for FLCatch
landings.sel(object)
# S4 method for FLCatch
discards.sel(object)
# S4 method for FLCatch
discards.ratio(object)
# S4 method for FLCatch,missing
plot(x, y, ...)Either an FLQuant (to determine the size of the FLQuant slots) or missing
Other things
Replacement value
FLCatch
missing
An FLCatch object
Make an FLCatch object.
This is class is used inside FLFishery to store the catches of
a single stock or species caught by that fleet.
Parameters of the catchability function, (FLPar).
Selectivity at age as proportions over fully-selected
ages, (FLQuant).
Description of the data contents and origin, (character).
Discards at age in numbers, (FLQuant).
Mean weight-at-age in the discards, (FLQuant).
Name of the object, e.g. species or stock code, (character).
Landings at age in numbers, (FLQuant).
Mean weight-at-age in the landings, (FLQuant).
Mean price by age per unit of weight, (FLQuant).
Ranges of age and years, plusgroup, (numeric).
All FLQuant slots must share
dimensions 2 to 5.
iter dim of length 1 or NThe 6th dimension in all
FLQuant and FLPar slots must be 1 or N, where N is the
same value for the whole object.
All FLQuant slots must share
dimensions 2 to 5.
You can inspect the class validity function by using
getValidity(getClassDef('FLCatch'))
All slots in the class have accessor and replacement methods defined that allow retrieving and substituting individual slots.
The values passed for replacement need to be of the class of that slot. A numeric vector can also be used when replacing FLQuant slots, and the vector will be used to substitute the values in the slot, but not its other attributes.
A construction method exists for this class that can take named arguments for
any of its slots. All slots are then created to match the requirements of the
class validity. If an unnamed FLQuant object is provided, this is used
for sizing but not stored in any slot.
Methods exist for various calculations based on values stored in the class:
Total landings as sum on 'age' of landings.n
times landings.wt.
Total discards as sum on 'age' of discards.n
times discards.wt.
Selectivity at age in the landings as proportions
over fully-selected ages, (FLQuant).
Selectivity at age in the discards as proportions
over fully-selected ages, (FLQuant).
Catch at age in numbers as landings.n plus
discards.n.
Weighted average of landings.wt and
discards.wt.
Total catch as sum of landings and
discards.
Proportion at age of discards in catch.
Standard plot for the FLCatch class.
data(ple4)
# EXTRACT data from FLCore ple4, fake prices
fca <- FLCatch(name='PLE', desc='All NS PLE catches',
landings.n=landings.n(ple4), landings.wt=landings.wt(ple4),
discards.n=discards.n(ple4), discards.wt=discards.wt(ple4),
price=landings.wt(ple4) * 23, catch.q=FLPar(q=1),
catch.sel=catch.sel(ple4))
# Calculations
landings(fca)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963 1964 1965 1966
#> all 70926 74157 78178 88764 85267 90305 103162 111121 105424 98334
#> year
#> age 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976
#> all 103947 121020 122661 111783 117301 130443 133768 115181 94458 122166
#> year
#> age 1977 1978 1979 1980 1981 1982 1983 1984 1985 1986
#> all 108524 128366 119827 150640 151304 145669 143690 162681 182374 166633
#> year
#> age 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996
#> all 155005 168118 187666 174414 147844 134793 141800 126195 109681 93642
#> year
#> age 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006
#> all 82875 74217 97340 100318 66596 87138 74770 82529 61501 62161
#> year
#> age 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016
#> all 59713 64574 68842 74246 74437 82307 97123 86424 91033 86381
#> year
#> age 2017
#> all 84750
#>
#> units: t
catch.n(fca)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962
#> 1 42702.66 72733.05 99992.13 97351.36 105215.69 65815.54
#> 2 40141.28 71294.94 120597.60 152894.50 135019.66 147178.83
#> 3 79353.02 52031.60 80618.90 116614.20 137289.10 129967.50
#> 4 56560.05 67368.68 37763.40 51480.30 72242.52 89048.40
#> 5 31888.26 35848.98 43350.57 24004.78 32458.56 46321.31
#> 6 10988.02 21828.30 23811.03 27315.98 14742.89 20597.37
#> 7 12049.59 8568.27 14322.21 13940.69 15632.32 8738.10
#> 8 9595.80 8610.92 5999.88 9719.19 9045.32 9787.41
#> 9 5495.25 7050.64 6932.95 4592.23 6532.14 5770.30
#> 10 11779.09 14773.43 16725.71 15503.41 12505.12 13593.85
#> year
#> age 1963 1964 1965 1966 1967 1968
#> 1 51938.98 167733.27 41740.48 39663.31 37965.74 52595.95
#> 2 111598.54 116858.90 446985.00 111166.40 98310.59 82427.96
#> 3 161466.70 126250.50 123478.00 462675.40 120682.10 105022.10
#> 4 83925.51 90727.70 61372.16 61390.10 259964.00 71629.90
#> 5 57929.89 52829.48 53769.44 35187.61 35582.32 157422.80
#> 6 30973.68 38431.54 32417.30 30721.82 20186.83 21932.60
#> 7 12304.87 17096.54 18841.81 15318.96 15625.57 11270.73
#> 8 5372.20 7257.27 9369.66 10117.96 8730.53 9465.05
#> 9 6709.53 3823.86 4806.34 5832.41 6397.31 5731.52
#> 10 16653.29 19738.48 16512.27 13434.04 12784.66 14154.41
#> year
#> age 1969 1970 1971 1972 1973 1974
#> 1 109457.72 117963.12 73200.74 61075.31 245992.44 216251.72
#> 2 87678.20 133995.30 126945.00 83724.50 76932.20 308599.00
#> 3 74641.70 66343.10 100832.40 108372.60 77394.22 66810.75
#> 4 59702.90 41494.61 39796.45 67677.23 74297.75 47873.80
#> 5 45774.70 39552.22 27756.81 26517.95 42478.64 40611.35
#> 6 103358.62 30184.47 25591.45 17801.04 16061.13 22360.23
#> 7 12474.47 57327.07 17180.59 15407.22 10556.57 8600.26
#> 8 6747.59 7215.49 33998.65 10747.18 9710.27 6360.36
#> 9 6186.04 4365.02 4767.89 23096.60 7321.75 6455.12
#> 10 15527.56 17101.29 16488.17 15528.78 26611.03 22141.89
#> year
#> age 1975 1976 1977 1978 1979 1980
#> 1 187654.33 186977.31 311012.79 260241.69 237416.19 228048.89
#> 2 237186.50 168020.90 142113.10 222931.10 198319.50 218847.00
#> 3 232622.90 162809.20 113342.50 96339.80 152671.10 139741.72
#> 4 36792.38 128298.60 96958.20 69719.70 56458.10 80323.48
#> 5 23001.21 17915.05 70724.47 58168.61 40407.81 28827.79
#> 6 18900.43 11094.15 9943.65 41726.33 31902.36 19874.09
#> 7 11126.04 9921.34 6354.82 5572.73 21132.34 15387.55
#> 8 4998.26 6528.95 5846.88 3587.01 2988.53 11113.54
#> 9 4078.97 3088.62 3861.27 3341.05 2028.66 1676.81
#> 10 17837.63 13249.71 9737.37 8318.56 7618.36 6699.37
#> year
#> age 1981 1982 1983 1984 1985 1986
#> 1 185495.15 378858.07 312379.15 330326.34 468120.57 1083225.66
#> 2 269603.30 249567.40 462798.80 315003.20 294917.80 417134.40
#> 3 158997.60 195147.39 177915.40 330543.20 227034.40 213479.40
#> 4 65082.75 69630.68 91099.41 93427.50 186569.17 128385.20
#> 5 35922.17 27199.44 29609.48 41553.29 46043.95 96350.40
#> 6 13526.22 16783.02 12738.97 14250.85 20729.69 23032.71
#> 7 9994.94 7064.21 8864.51 6893.69 7680.85 10371.03
#> 8 8074.76 5264.18 3930.15 5312.93 4138.25 4255.10
#> 9 6106.59 4501.20 3172.50 2486.08 3255.40 2437.65
#> 10 5886.32 8191.05 8233.28 6983.05 5857.16 6299.68
#> year
#> age 1987 1988 1989 1990 1991 1992
#> 1 442682.83 374165.61 245185.87 205383.51 187958.37 168039.58
#> 2 1047256.40 478975.90 438711.90 298563.10 258742.30 242447.10
#> 3 302191.00 746685.00 331436.00 307676.00 223193.20 201697.80
#> 4 114725.07 148044.90 337985.90 152615.20 153467.20 115796.60
#> 5 65923.14 56419.88 71205.46 168825.14 80728.20 82733.51
#> 6 46662.01 31129.37 26393.55 33025.90 78235.21 38323.92
#> 7 10445.49 19869.41 12392.43 9827.96 12398.30 31918.82
#> 8 5248.06 4881.37 8448.05 5182.11 4630.49 6547.11
#> 9 2503.27 2916.79 2412.42 4215.04 3060.81 3024.36
#> 10 6660.50 6327.28 5119.13 3977.21 5138.26 5860.65
#> year
#> age 1993 1994 1995 1996 1997 1998
#> 1 104636.60 93290.14 126854.53 105190.15 272197.35 91657.58
#> 2 211487.60 128362.70 128617.10 232415.20 255916.60 701954.20
#> 3 183104.50 156029.90 106282.90 129090.50 242378.70 221681.40
#> 4 100871.90 88971.80 79720.43 56087.87 58913.90 85780.00
#> 5 60965.29 51632.74 44764.95 38876.43 25344.58 24336.34
#> 6 40691.96 30198.53 25052.05 21493.70 18509.83 11435.31
#> 7 16523.26 17190.20 12590.23 10906.74 9435.01 7208.62
#> 8 15502.77 6601.74 6586.87 5404.86 4914.63 3754.64
#> 9 3569.78 6301.80 2542.41 2953.18 2644.70 2236.10
#> 10 5512.56 4370.61 4990.73 4174.69 4409.92 4065.62
#> year
#> age 1999 2000 2001 2002 2003 2004
#> 1 91337.92 128525.79 97495.00 273411.66 91796.79 235514.85
#> 2 186960.90 138479.50 187737.70 172176.50 531815.50 146919.67
#> 3 505281.00 147232.10 142364.40 199413.20 136174.20 311226.00
#> 4 76360.70 242206.10 92615.80 77923.40 74693.40 43158.15
#> 5 34618.15 32822.43 109946.10 41589.14 34002.05 32763.40
#> 6 9804.85 12638.84 12252.56 47176.75 20056.13 16254.67
#> 7 3720.12 3078.29 4697.26 5248.95 19344.91 7219.69
#> 8 2527.38 1433.52 1455.38 2162.48 1819.76 5516.38
#> 9 1623.54 1209.85 716.29 593.44 673.88 541.68
#> 10 3199.13 2395.19 1899.89 1503.05 1324.69 1330.89
#> year
#> age 2005 2006 2007 2008 2009 2010
#> 1 172458.80 148426.56 190996.81 155534.36 167161.84 208142.93
#> 2 271017.30 175205.50 177494.30 259601.30 177602.40 141436.20
#> 3 84388.60 183320.40 127413.40 120064.40 165611.40 125432.90
#> 4 117804.80 37951.03 79448.20 52002.37 56248.90 101973.80
#> 5 19322.44 52637.29 16996.50 36841.46 25518.30 29618.53
#> 6 14652.44 8757.52 26391.73 9157.63 19803.51 13736.46
#> 7 5814.47 6184.97 4385.02 14378.47 5198.22 11568.37
#> 8 2303.57 2302.40 2627.86 1806.28 5937.37 2261.77
#> 9 1902.16 829.78 738.38 776.27 563.45 2149.01
#> 10 1182.49 1743.94 1469.08 1475.94 1787.19 2029.21
#> year
#> age 2011 2012 2013 2014 2015 2016
#> 1 163502.68 98149.48 129467.17 208745.31 127812.76 128907.81
#> 2 175630.70 205693.78 177076.40 209915.05 226503.32 113194.28
#> 3 125474.80 184253.10 217019.50 167118.70 179280.50 195889.00
#> 4 92002.60 89537.10 119932.30 140221.90 114154.30 125580.90
#> 5 58469.50 56101.56 54291.80 69447.80 78925.40 65419.30
#> 6 16829.21 33838.48 30423.65 27804.45 36150.81 43845.72
#> 7 7720.61 8357.86 15653.39 15042.83 15185.55 19751.87
#> 8 5236.21 3640.73 4310.38 8559.20 7800.51 6880.26
#> 9 987.75 2763.26 2123.34 2339.97 3873.36 3037.44
#> 10 3674.02 4098.71 5958.17 6821.41 7393.56 8702.57
#> year
#> age 2017
#> 1 115058.24
#> 2 143659.04
#> 3 110813.00
#> 4 131782.60
#> 5 75562.10
#> 6 38451.76
#> 7 21449.14
#> 8 7873.59
#> 9 2567.24
#> 10 8882.65
#>
#> units: 1000
catch.wt(fca)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963 1964
#> 1 0.043999 0.047000 0.051000 0.045000 0.044000 0.042000 0.048000 0.032000
#> 2 0.109801 0.105531 0.119977 0.115036 0.101425 0.099279 0.109657 0.125989
#> 3 0.194296 0.189472 0.191506 0.203724 0.180139 0.181375 0.174760 0.203920
#> 4 0.256615 0.256336 0.260406 0.287076 0.301163 0.273064 0.304230 0.271018
#> 5 0.349100 0.329376 0.345271 0.377377 0.402368 0.396960 0.391876 0.378894
#> 6 0.455454 0.452345 0.471905 0.479940 0.506119 0.538035 0.531254 0.484717
#> 7 0.532988 0.512971 0.591979 0.600988 0.603987 0.569980 0.623985 0.627988
#> 8 0.588982 0.614964 0.622952 0.682962 0.670966 0.691974 0.666969 0.699965
#> 9 0.395964 0.664949 0.749944 0.723929 0.811933 0.776924 0.714950 0.736908
#> 10 0.997959 0.991965 0.999970 1.093964 1.070964 1.126954 1.027970 1.004970
#> year
#> age 1965 1966 1967 1968 1969 1970 1971 1972
#> 1 0.038000 0.038000 0.035999 0.060000 0.052007 0.049160 0.057058 0.066877
#> 2 0.075866 0.104304 0.110549 0.116347 0.174246 0.131116 0.159854 0.207158
#> 3 0.214406 0.148098 0.189574 0.223414 0.271611 0.268303 0.276554 0.289992
#> 4 0.313280 0.314817 0.234828 0.274537 0.283588 0.352160 0.388422 0.407373
#> 5 0.381437 0.427637 0.422016 0.339914 0.356021 0.393751 0.444319 0.486404
#> 6 0.468814 0.482866 0.543256 0.516098 0.408318 0.440916 0.512235 0.539756
#> 7 0.538992 0.558990 0.596989 0.589983 0.572974 0.498998 0.541984 0.607979
#> 8 0.662961 0.623983 0.661974 0.595971 0.654967 0.671964 0.606994 0.645971
#> 9 0.725914 0.689948 0.737946 0.685915 0.657962 0.743909 0.698947 0.673982
#> 10 0.886975 0.932969 0.977957 0.910967 0.892974 0.891969 0.890974 0.938971
#> year
#> age 1973 1974 1975 1976 1977 1978 1979 1980
#> 1 0.045283 0.056495 0.068767 0.087787 0.071297 0.069846 0.066755 0.055802
#> 2 0.205176 0.120785 0.151806 0.180926 0.217858 0.190274 0.190466 0.196997
#> 3 0.333995 0.343206 0.204961 0.261605 0.245025 0.315341 0.295421 0.342960
#> 4 0.403174 0.403747 0.392940 0.347254 0.318061 0.364251 0.337713 0.399370
#> 5 0.478238 0.472376 0.492452 0.509426 0.395996 0.432299 0.426454 0.471181
#> 6 0.537635 0.552275 0.585128 0.591583 0.551315 0.486444 0.472122 0.542439
#> 7 0.604968 0.608978 0.635984 0.640979 0.646945 0.608967 0.549993 0.587979
#> 8 0.626970 0.692947 0.702944 0.704946 0.720928 0.686894 0.674948 0.661962
#> 9 0.676947 0.706939 0.782910 0.740877 0.714912 0.775834 0.795858 0.771797
#> 10 0.841983 0.925980 1.018976 0.979962 0.977947 0.949934 0.959948 1.012908
#> year
#> age 1981 1982 1983 1984 1985 1986 1987 1988
#> 1 0.048365 0.055830 0.051689 0.053062 0.054057 0.049257 0.043000 0.043000
#> 2 0.183437 0.152001 0.152694 0.149910 0.169134 0.141397 0.113402 0.101804
#> 3 0.326828 0.308248 0.274762 0.265457 0.267967 0.275409 0.218606 0.197485
#> 4 0.414729 0.420607 0.379126 0.321042 0.333496 0.310525 0.343365 0.275873
#> 5 0.501685 0.511917 0.506954 0.470452 0.443875 0.402396 0.375422 0.415128
#> 6 0.555582 0.605900 0.601997 0.588348 0.561543 0.471807 0.470664 0.476689
#> 7 0.603969 0.663973 0.676959 0.676968 0.666955 0.667978 0.573979 0.589985
#> 8 0.641961 0.711936 0.770909 0.725935 0.729903 0.749920 0.727922 0.679945
#> 9 0.724928 0.737919 0.814865 0.838846 0.806857 0.855849 0.834822 0.807882
#> 10 1.006917 0.983944 0.983941 1.035921 1.020912 1.013928 0.992912 1.016921
#> year
#> age 1989 1990 1991 1992 1993 1994 1995 1996
#> 1 0.047111 0.053316 0.056328 0.054789 0.062608 0.063660 0.071146 0.053846
#> 2 0.117643 0.129659 0.149493 0.145431 0.160205 0.179072 0.182859 0.140176
#> 3 0.215080 0.210649 0.210932 0.225705 0.249957 0.256510 0.282589 0.267965
#> 4 0.291297 0.290494 0.272753 0.275225 0.303272 0.331360 0.333654 0.336259
#> 5 0.364309 0.359634 0.349364 0.324154 0.339674 0.372247 0.372883 0.412679
#> 6 0.512269 0.439763 0.449276 0.410348 0.406913 0.416102 0.419083 0.463592
#> 7 0.590975 0.585968 0.525990 0.529995 0.511991 0.490993 0.473992 0.489980
#> 8 0.667958 0.689926 0.665960 0.606975 0.629973 0.609971 0.592967 0.552953
#> 9 0.784831 0.760900 0.742884 0.718891 0.719903 0.730933 0.733853 0.711873
#> 10 0.939919 1.009880 0.923918 0.890936 0.855927 0.905917 0.905884 0.857914
#> year
#> age 1997 1998 1999 2000 2001 2002 2003 2004
#> 1 0.045137 0.047196 0.053825 0.063148 0.090053 0.056714 0.065718 0.054372
#> 2 0.128986 0.093865 0.103245 0.123154 0.135808 0.130844 0.123961 0.124949
#> 3 0.220379 0.208050 0.198554 0.209624 0.196628 0.221728 0.226407 0.220420
#> 4 0.353209 0.298684 0.266515 0.274163 0.233921 0.285107 0.283692 0.296929
#> 5 0.408398 0.449011 0.413306 0.371930 0.303216 0.326126 0.335699 0.375498
#> 6 0.472671 0.544009 0.414278 0.452321 0.409800 0.426357 0.385257 0.421241
#> 7 0.540972 0.612968 0.537933 0.564982 0.576465 0.469180 0.419098 0.505507
#> 8 0.573943 0.672883 0.636879 0.600437 0.700436 0.643572 0.634464 0.559824
#> 9 0.615899 0.686827 0.747766 0.751606 0.795388 0.759522 0.762447 0.796136
#> 10 0.911896 0.898866 0.803876 0.887791 0.798831 0.903724 0.856513 0.871236
#> year
#> age 2005 2006 2007 2008 2009 2010 2011 2012
#> 1 0.067422 0.060317 0.058775 0.056569 0.061207 0.061822 0.047647 0.052327
#> 2 0.117393 0.139368 0.113666 0.123499 0.124699 0.131690 0.114740 0.096187
#> 3 0.212930 0.211812 0.222959 0.247632 0.232112 0.226039 0.211950 0.195563
#> 4 0.297902 0.293315 0.310085 0.324622 0.326594 0.311303 0.277422 0.294436
#> 5 0.351575 0.374795 0.351229 0.389383 0.399101 0.395925 0.369451 0.348424
#> 6 0.347476 0.383401 0.375268 0.436803 0.465827 0.442379 0.453226 0.425402
#> 7 0.453163 0.428475 0.490651 0.368272 0.518436 0.462564 0.595436 0.509182
#> 8 0.554110 0.456878 0.357361 0.468685 0.440965 0.573445 0.444870 0.557246
#> 9 0.616866 0.530799 0.586515 0.639565 0.667432 0.681865 0.555780 0.557920
#> 10 0.909298 0.747735 0.631758 0.637659 0.791518 0.648744 0.803854 0.679823
#> year
#> age 2013 2014 2015 2016 2017
#> 1 0.050915 0.025005 0.026000 0.048000 0.051034
#> 2 0.091130 0.093435 0.080271 0.083793 0.085895
#> 3 0.178565 0.171657 0.161845 0.157402 0.158552
#> 4 0.273559 0.253256 0.257901 0.243039 0.218409
#> 5 0.344682 0.317605 0.325621 0.298878 0.314393
#> 6 0.409448 0.396089 0.393707 0.352469 0.386271
#> 7 0.490250 0.472624 0.461250 0.422309 0.437717
#> 8 0.599009 0.541619 0.480815 0.465186 0.531501
#> 9 0.606877 0.627925 0.581978 0.555971 0.641979
#> 10 0.679910 0.649906 0.599938 0.683933 0.734886
#>
#> units: kg