Method to append objects along the year dimensions, by extending, combining and substituting sections of them.
Arguments
- x
the object to which the values are to be appended to.
- values
to be included in the modified object.
- after
a year dimname after with the values are to be appended.
Details
FLR objects are commonly manipulated along the year dimension, and the append method offers a simple interface for substituting parts of an object with another, or combine them into one, extending them when necessary. The object to be included or added to the first will be placed as defined by the year dimnames, unless the after input argument specifies otherwise.
Attributes like dimnames and units will always be taken from the first argument, unless the necessary chnages to dimnames$year
Examples
# append(FLQuant, FLQuant)
fq1 <- FLQuant(1, dimnames=list(age=1:3, year=2000:2010))
fq2 <- FLQuant(2, dimnames=list(age=1:3, year=2011:2012))
fq3 <- FLQuant(2, dimnames=list(age=1:3, year=2014:2016))
# Appends by dimnames$year
append(fq1, fq2)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012
#> 1 1 1 1 1 1 1 1 1 1 1 1 2 2
#> 2 1 1 1 1 1 1 1 1 1 1 1 2 2
#> 3 1 1 1 1 1 1 1 1 1 1 1 2 2
#>
#> units: NA
# Appends by dimnames$year with gap (2011:2013)
append(fq1, fq3)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
#> 1 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> 2 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> 3 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> year
#> age 2015 2016
#> 1 2 2
#> 2 2 2
#> 3 2 2
#>
#> units: NA
# Appends inside x
append(fq1, fq2, after=2009)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011
#> 1 1 1 1 1 1 1 1 1 1 1 2 2
#> 2 1 1 1 1 1 1 1 1 1 1 2 2
#> 3 1 1 1 1 1 1 1 1 1 1 2 2
#>
#> units: NA
# Appends after end of x
append(fq1, fq2, after=2013)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014
#> 1 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> 2 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> 3 1 1 1 1 1 1 1 1 1 1 1 NA NA NA 2
#> year
#> age 2015
#> 1 2
#> 2 2
#> 3 2
#>
#> units: NA
# append(FLStock, FLStock)
data(ple4)
fs1 <- window(ple4, end=2001)
fs2 <- window(ple4, start=2002)
fs3 <- window(ple4, start=2005)
# Appends by dimnames$year
stock.n(append(fs1, fs2))
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963
#> 1 477074.0 710748.0 874712.0 797702.0 870799.0 615691.0 610017.0
#> 2 268185.0 391105.0 574019.0 696497.0 629331.0 688002.0 494583.0
#> 3 361589.0 204551.0 286216.0 404963.0 485155.0 441333.0 482883.0
#> 4 223134.0 251892.0 135739.0 182546.0 255875.0 308828.0 276136.0
#> 5 147904.0 148258.0 164040.0 87017.8 116368.0 163035.0 195022.0
#> 6 60608.8 103574.0 100146.0 107321.0 55977.1 74519.9 103605.0
#> 7 62627.2 44411.7 73005.1 68029.4 71202.0 36669.7 47899.3
#> 8 49418.3 45231.8 32053.7 52465.8 48326.9 49594.8 24891.5
#> 9 31996.7 35608.8 32755.0 23308.8 38248.3 35143.0 35587.0
#> 10 68585.1 74612.3 79021.3 78690.9 73222.3 82791.3 88328.2
#> year
#> age 1964 1965 1966 1967 1968 1969 1970
#> 1 2449900.0 664500.0 579075.0 428110.0 418228.0 666902.0 671454.0
#> 2 502620.0 2057370.0 561599.0 486279.0 351300.0 328477.0 499525.0
#> 3 341643.0 343934.0 1437490.0 402660.0 346712.0 239678.0 214078.0
#> 4 283950.0 189574.0 194255.0 862270.0 249951.0 214175.0 146130.0
#> 5 170312.0 170953.0 113379.0 117592.0 533809.0 158259.0 137189.0
#> 6 121552.0 104036.0 103728.0 69241.0 72675.5 333787.0 99805.0
#> 7 64386.7 73564.1 63413.5 64736.1 43515.6 44971.0 204065.0
#> 8 31671.7 42047.6 48694.7 42848.4 43754.3 28686.1 28864.1
#> 9 17425.6 21773.1 29156.9 34459.9 30486.1 30610.0 19555.5
#> 10 89949.7 74801.9 67158.6 68866.5 75287.6 76834.4 76614.6
#> year
#> age 1971 1972 1973 1974 1975 1976 1977
#> 1 433599.0 367450.0 1391430.0 1074920.0 787372.0 674010.0 1033740.0
#> 2 495583.0 322847.0 274502.0 1025520.0 767410.0 534441.0 432591.0
#> 3 324931.0 328032.0 212726.0 175441.0 635424.0 469588.0 324352.0
#> 4 130830.0 198448.0 194134.0 119185.0 95486.7 354654.0 270674.0
#> 5 92885.0 80660.2 115448.0 105314.0 62528.3 51565.8 199395.0
#> 6 86638.5 57736.0 47858.4 64234.3 56842.0 34795.5 29688.2
#> 7 61697.5 54135.3 35370.6 28087.4 36940.6 33525.3 20971.2
#> 8 130295.0 39537.1 34377.0 21998.3 17263.3 22879.5 20930.5
#> 9 19273.9 85654.3 25584.3 21899.6 13875.4 10882.2 14512.6
#> 10 66652.6 57588.9 92986.6 75118.4 60677.9 46683.2 36597.8
#> year
#> age 1978 1979 1980 1981 1982 1983 1984
#> 1 879043.0 915553.0 1078660.0 999968.0 1935350.0 1375880.0 1302060.0
#> 2 640577.0 548706.0 603278.0 759629.0 728751.0 1391630.0 948596.0
#> 3 256771.0 368443.0 308663.0 338604.0 431979.0 422976.0 820717.0
#> 4 186122.0 141111.0 188898.0 147121.0 156042.0 206296.0 214367.0
#> 5 153084.0 102391.0 74240.4 94918.5 71554.5 75327.9 100486.0
#> 6 113431.0 83438.8 54395.5 39883.2 51872.3 38991.5 40129.1
#> 7 17442.0 63121.2 45292.9 30397.5 23273.3 31032.9 23210.4
#> 8 12952.3 10501.4 37093.0 26405.0 18034.5 14363.0 19675.9
#> 9 13395.2 8318.7 6668.8 23029.0 16239.1 11328.1 9269.7
#> 10 33351.6 31239.8 26643.8 22198.4 29551.0 29398.7 26037.4
#> year
#> age 1985 1986 1987 1988 1989 1990 1991
#> 1 1792220.0 4303680.0 1910200.0 1774940.0 1250510.0 1083810.0 981356.0
#> 2 864871.0 1177750.0 2866780.0 1308470.0 1251000.0 898824.0 785747.0
#> 3 559870.0 503176.0 670566.0 1602170.0 730349.0 716388.0 530410.0
#> 4 429744.0 291704.0 253291.0 320928.0 743636.0 347377.0 357105.0
#> 5 105579.0 212338.0 142492.0 120680.0 150385.0 353208.0 169944.0
#> 6 51597.9 51971.1 101004.0 66591.7 55845.9 68749.9 159986.0
#> 7 22812.3 27066.0 25237.6 47264.9 30819.2 25576.2 30985.4
#> 8 14467.2 13364.5 14671.0 12950.5 23964.9 16156.4 13837.3
#> 9 12765.7 9167.3 8060.4 8304.2 7096.0 13682.2 9708.3
#> 10 22968.3 23691.3 21446.4 18014.1 15057.6 12910.2 16297.5
#> year
#> age 1992 1993 1994 1995 1996 1997 1998
#> 1 854841.0 550376.0 566448.0 932162.0 893056.0 2431310.0 778427.0
#> 2 709583.0 614021.0 398693.0 423980.0 722992.0 708162.0 1941390.0
#> 3 465814.0 412376.0 355244.0 239117.0 261727.0 433954.0 398394.0
#> 4 268732.0 230658.0 199926.0 173831.0 115825.0 114808.0 163844.0
#> 5 177919.0 133599.0 113279.0 96739.9 81895.9 51781.9 48217.9
#> 6 77446.0 82752.7 63227.4 53666.9 45201.5 37345.9 22897.6
#> 7 70809.8 33854.3 36415.8 28660.1 24870.8 20578.7 16297.6
#> 8 16300.8 33881.2 15013.9 16696.8 14022.1 12186.0 9697.5
#> 9 8133.4 8552.4 15995.5 7339.9 8872.5 7570.6 6374.5
#> 10 15761.0 13206.8 11093.7 14408.1 12542.4 12623.6 11589.9
#> year
#> age 1999 2000 2001 2002 2003 2004 2005
#> 1 683151.0 857525.0 634808.0 1792880.0 557844.0 1235790.0 863893.0
#> 2 617294.0 531403.0 653888.0 481832.0 1362670.0 417612.0 894667.0
#> 3 1091830.0 381345.0 349511.0 413685.0 272903.0 729522.0 238706.0
#> 4 151199.0 510105.0 205661.0 181506.0 185801.0 118238.0 365617.0
#> 5 67219.3 64650.9 232564.0 98489.7 90505.8 97419.1 66113.3
#> 6 20631.8 28114.9 27483.5 106479.0 49763.0 49696.3 57107.9
#> 7 9911.4 9398.3 13484.8 13278.2 51718.8 26043.7 29565.3
#> 8 7927.6 5445.6 5587.2 7752.1 7045.7 28478.9 16720.1
#> 9 5219.9 4778.1 3568.0 3675.4 4964.1 4649.5 20533.5
#> 10 10285.7 9459.5 9463.7 9308.9 9758.3 11423.5 12764.9
#> year
#> age 2006 2007 2008 2009 2010 2011 2012
#> 1 875191.0 1379750.0 1135050.0 1088820.0 1444570.0 1608190.0 1278010.0
#> 2 618023.0 651007.0 1067080.0 879336.0 826492.0 1109460.0 1299830.0
#> 3 552652.0 393107.0 420759.0 719298.0 627121.0 613580.0 837139.0
#> 4 136058.0 326369.0 234965.0 266894.0 493740.0 448410.0 436124.0
#> 5 219194.0 87128.7 219954.0 163268.0 188123.0 349991.0 318435.0
#> 6 41505.0 148406.0 62707.2 164048.0 123503.0 142101.0 261180.0
#> 7 37777.6 29245.7 109232.0 48044.5 129629.0 98702.9 112594.0
#> 8 21233.8 28310.9 22299.1 85182.6 38534.8 106302.0 81974.3
#> 9 12941.5 17026.1 23120.2 18460.8 71434.6 32718.3 91209.7
#> 10 27199.2 33875.1 43959.2 58555.1 67452.5 121698.0 135291.0
#> year
#> age 2013 2014 2015 2016 2017
#> 1 1455050.0 1640700.0 895620.0 1211320.0 1823000.0
#> 2 1063130.0 1193580.0 1286330.0 689025.0 973599.0
#> 3 980853.0 793858.0 880738.0 948914.0 515997.0
#> 4 582663.0 681616.0 559741.0 626799.0 672732.0
#> 5 309658.0 413410.0 483694.0 398150.0 447979.0
#> 6 234879.0 228655.0 308142.0 362736.0 298156.0
#> 7 204189.0 183635.0 180488.0 244483.0 286575.0
#> 8 93937.9 169885.0 151867.0 148885.0 202450.0
#> 9 70713.1 80901.8 145583.0 130001.0 128177.0
#> 10 198423.0 235842.0 277893.0 372467.0 443492.0
#>
#> units: 1000
# Appends by dimnames$year with gap (2011:2013)
stock.n(append(fs1, fs3))
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963
#> 1 477074.0 710748.0 874712.0 797702.0 870799.0 615691.0 610017.0
#> 2 268185.0 391105.0 574019.0 696497.0 629331.0 688002.0 494583.0
#> 3 361589.0 204551.0 286216.0 404963.0 485155.0 441333.0 482883.0
#> 4 223134.0 251892.0 135739.0 182546.0 255875.0 308828.0 276136.0
#> 5 147904.0 148258.0 164040.0 87017.8 116368.0 163035.0 195022.0
#> 6 60608.8 103574.0 100146.0 107321.0 55977.1 74519.9 103605.0
#> 7 62627.2 44411.7 73005.1 68029.4 71202.0 36669.7 47899.3
#> 8 49418.3 45231.8 32053.7 52465.8 48326.9 49594.8 24891.5
#> 9 31996.7 35608.8 32755.0 23308.8 38248.3 35143.0 35587.0
#> 10 68585.1 74612.3 79021.3 78690.9 73222.3 82791.3 88328.2
#> year
#> age 1964 1965 1966 1967 1968 1969 1970
#> 1 2449900.0 664500.0 579075.0 428110.0 418228.0 666902.0 671454.0
#> 2 502620.0 2057370.0 561599.0 486279.0 351300.0 328477.0 499525.0
#> 3 341643.0 343934.0 1437490.0 402660.0 346712.0 239678.0 214078.0
#> 4 283950.0 189574.0 194255.0 862270.0 249951.0 214175.0 146130.0
#> 5 170312.0 170953.0 113379.0 117592.0 533809.0 158259.0 137189.0
#> 6 121552.0 104036.0 103728.0 69241.0 72675.5 333787.0 99805.0
#> 7 64386.7 73564.1 63413.5 64736.1 43515.6 44971.0 204065.0
#> 8 31671.7 42047.6 48694.7 42848.4 43754.3 28686.1 28864.1
#> 9 17425.6 21773.1 29156.9 34459.9 30486.1 30610.0 19555.5
#> 10 89949.7 74801.9 67158.6 68866.5 75287.6 76834.4 76614.6
#> year
#> age 1971 1972 1973 1974 1975 1976 1977
#> 1 433599.0 367450.0 1391430.0 1074920.0 787372.0 674010.0 1033740.0
#> 2 495583.0 322847.0 274502.0 1025520.0 767410.0 534441.0 432591.0
#> 3 324931.0 328032.0 212726.0 175441.0 635424.0 469588.0 324352.0
#> 4 130830.0 198448.0 194134.0 119185.0 95486.7 354654.0 270674.0
#> 5 92885.0 80660.2 115448.0 105314.0 62528.3 51565.8 199395.0
#> 6 86638.5 57736.0 47858.4 64234.3 56842.0 34795.5 29688.2
#> 7 61697.5 54135.3 35370.6 28087.4 36940.6 33525.3 20971.2
#> 8 130295.0 39537.1 34377.0 21998.3 17263.3 22879.5 20930.5
#> 9 19273.9 85654.3 25584.3 21899.6 13875.4 10882.2 14512.6
#> 10 66652.6 57588.9 92986.6 75118.4 60677.9 46683.2 36597.8
#> year
#> age 1978 1979 1980 1981 1982 1983 1984
#> 1 879043.0 915553.0 1078660.0 999968.0 1935350.0 1375880.0 1302060.0
#> 2 640577.0 548706.0 603278.0 759629.0 728751.0 1391630.0 948596.0
#> 3 256771.0 368443.0 308663.0 338604.0 431979.0 422976.0 820717.0
#> 4 186122.0 141111.0 188898.0 147121.0 156042.0 206296.0 214367.0
#> 5 153084.0 102391.0 74240.4 94918.5 71554.5 75327.9 100486.0
#> 6 113431.0 83438.8 54395.5 39883.2 51872.3 38991.5 40129.1
#> 7 17442.0 63121.2 45292.9 30397.5 23273.3 31032.9 23210.4
#> 8 12952.3 10501.4 37093.0 26405.0 18034.5 14363.0 19675.9
#> 9 13395.2 8318.7 6668.8 23029.0 16239.1 11328.1 9269.7
#> 10 33351.6 31239.8 26643.8 22198.4 29551.0 29398.7 26037.4
#> year
#> age 1985 1986 1987 1988 1989 1990 1991
#> 1 1792220.0 4303680.0 1910200.0 1774940.0 1250510.0 1083810.0 981356.0
#> 2 864871.0 1177750.0 2866780.0 1308470.0 1251000.0 898824.0 785747.0
#> 3 559870.0 503176.0 670566.0 1602170.0 730349.0 716388.0 530410.0
#> 4 429744.0 291704.0 253291.0 320928.0 743636.0 347377.0 357105.0
#> 5 105579.0 212338.0 142492.0 120680.0 150385.0 353208.0 169944.0
#> 6 51597.9 51971.1 101004.0 66591.7 55845.9 68749.9 159986.0
#> 7 22812.3 27066.0 25237.6 47264.9 30819.2 25576.2 30985.4
#> 8 14467.2 13364.5 14671.0 12950.5 23964.9 16156.4 13837.3
#> 9 12765.7 9167.3 8060.4 8304.2 7096.0 13682.2 9708.3
#> 10 22968.3 23691.3 21446.4 18014.1 15057.6 12910.2 16297.5
#> year
#> age 1992 1993 1994 1995 1996 1997 1998
#> 1 854841.0 550376.0 566448.0 932162.0 893056.0 2431310.0 778427.0
#> 2 709583.0 614021.0 398693.0 423980.0 722992.0 708162.0 1941390.0
#> 3 465814.0 412376.0 355244.0 239117.0 261727.0 433954.0 398394.0
#> 4 268732.0 230658.0 199926.0 173831.0 115825.0 114808.0 163844.0
#> 5 177919.0 133599.0 113279.0 96739.9 81895.9 51781.9 48217.9
#> 6 77446.0 82752.7 63227.4 53666.9 45201.5 37345.9 22897.6
#> 7 70809.8 33854.3 36415.8 28660.1 24870.8 20578.7 16297.6
#> 8 16300.8 33881.2 15013.9 16696.8 14022.1 12186.0 9697.5
#> 9 8133.4 8552.4 15995.5 7339.9 8872.5 7570.6 6374.5
#> 10 15761.0 13206.8 11093.7 14408.1 12542.4 12623.6 11589.9
#> year
#> age 1999 2000 2001 2002 2003 2004 2005
#> 1 683151.0 857525.0 634808.0 NA NA NA 863893.0
#> 2 617294.0 531403.0 653888.0 NA NA NA 894667.0
#> 3 1091830.0 381345.0 349511.0 NA NA NA 238706.0
#> 4 151199.0 510105.0 205661.0 NA NA NA 365617.0
#> 5 67219.3 64650.9 232564.0 NA NA NA 66113.3
#> 6 20631.8 28114.9 27483.5 NA NA NA 57107.9
#> 7 9911.4 9398.3 13484.8 NA NA NA 29565.3
#> 8 7927.6 5445.6 5587.2 NA NA NA 16720.1
#> 9 5219.9 4778.1 3568.0 NA NA NA 20533.5
#> 10 10285.7 9459.5 9463.7 NA NA NA 12764.9
#> year
#> age 2006 2007 2008 2009 2010 2011 2012
#> 1 875191.0 1379750.0 1135050.0 1088820.0 1444570.0 1608190.0 1278010.0
#> 2 618023.0 651007.0 1067080.0 879336.0 826492.0 1109460.0 1299830.0
#> 3 552652.0 393107.0 420759.0 719298.0 627121.0 613580.0 837139.0
#> 4 136058.0 326369.0 234965.0 266894.0 493740.0 448410.0 436124.0
#> 5 219194.0 87128.7 219954.0 163268.0 188123.0 349991.0 318435.0
#> 6 41505.0 148406.0 62707.2 164048.0 123503.0 142101.0 261180.0
#> 7 37777.6 29245.7 109232.0 48044.5 129629.0 98702.9 112594.0
#> 8 21233.8 28310.9 22299.1 85182.6 38534.8 106302.0 81974.3
#> 9 12941.5 17026.1 23120.2 18460.8 71434.6 32718.3 91209.7
#> 10 27199.2 33875.1 43959.2 58555.1 67452.5 121698.0 135291.0
#> year
#> age 2013 2014 2015 2016 2017
#> 1 1455050.0 1640700.0 895620.0 1211320.0 1823000.0
#> 2 1063130.0 1193580.0 1286330.0 689025.0 973599.0
#> 3 980853.0 793858.0 880738.0 948914.0 515997.0
#> 4 582663.0 681616.0 559741.0 626799.0 672732.0
#> 5 309658.0 413410.0 483694.0 398150.0 447979.0
#> 6 234879.0 228655.0 308142.0 362736.0 298156.0
#> 7 204189.0 183635.0 180488.0 244483.0 286575.0
#> 8 93937.9 169885.0 151867.0 148885.0 202450.0
#> 9 70713.1 80901.8 145583.0 130001.0 128177.0
#> 10 198423.0 235842.0 277893.0 372467.0 443492.0
#>
#> units: 1000
# Appends inside x
stock.n(append(fs1, fs3, after=2000))
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963
#> 1 477074.0 710748.0 874712.0 797702.0 870799.0 615691.0 610017.0
#> 2 268185.0 391105.0 574019.0 696497.0 629331.0 688002.0 494583.0
#> 3 361589.0 204551.0 286216.0 404963.0 485155.0 441333.0 482883.0
#> 4 223134.0 251892.0 135739.0 182546.0 255875.0 308828.0 276136.0
#> 5 147904.0 148258.0 164040.0 87017.8 116368.0 163035.0 195022.0
#> 6 60608.8 103574.0 100146.0 107321.0 55977.1 74519.9 103605.0
#> 7 62627.2 44411.7 73005.1 68029.4 71202.0 36669.7 47899.3
#> 8 49418.3 45231.8 32053.7 52465.8 48326.9 49594.8 24891.5
#> 9 31996.7 35608.8 32755.0 23308.8 38248.3 35143.0 35587.0
#> 10 68585.1 74612.3 79021.3 78690.9 73222.3 82791.3 88328.2
#> year
#> age 1964 1965 1966 1967 1968 1969 1970
#> 1 2449900.0 664500.0 579075.0 428110.0 418228.0 666902.0 671454.0
#> 2 502620.0 2057370.0 561599.0 486279.0 351300.0 328477.0 499525.0
#> 3 341643.0 343934.0 1437490.0 402660.0 346712.0 239678.0 214078.0
#> 4 283950.0 189574.0 194255.0 862270.0 249951.0 214175.0 146130.0
#> 5 170312.0 170953.0 113379.0 117592.0 533809.0 158259.0 137189.0
#> 6 121552.0 104036.0 103728.0 69241.0 72675.5 333787.0 99805.0
#> 7 64386.7 73564.1 63413.5 64736.1 43515.6 44971.0 204065.0
#> 8 31671.7 42047.6 48694.7 42848.4 43754.3 28686.1 28864.1
#> 9 17425.6 21773.1 29156.9 34459.9 30486.1 30610.0 19555.5
#> 10 89949.7 74801.9 67158.6 68866.5 75287.6 76834.4 76614.6
#> year
#> age 1971 1972 1973 1974 1975 1976 1977
#> 1 433599.0 367450.0 1391430.0 1074920.0 787372.0 674010.0 1033740.0
#> 2 495583.0 322847.0 274502.0 1025520.0 767410.0 534441.0 432591.0
#> 3 324931.0 328032.0 212726.0 175441.0 635424.0 469588.0 324352.0
#> 4 130830.0 198448.0 194134.0 119185.0 95486.7 354654.0 270674.0
#> 5 92885.0 80660.2 115448.0 105314.0 62528.3 51565.8 199395.0
#> 6 86638.5 57736.0 47858.4 64234.3 56842.0 34795.5 29688.2
#> 7 61697.5 54135.3 35370.6 28087.4 36940.6 33525.3 20971.2
#> 8 130295.0 39537.1 34377.0 21998.3 17263.3 22879.5 20930.5
#> 9 19273.9 85654.3 25584.3 21899.6 13875.4 10882.2 14512.6
#> 10 66652.6 57588.9 92986.6 75118.4 60677.9 46683.2 36597.8
#> year
#> age 1978 1979 1980 1981 1982 1983 1984
#> 1 879043.0 915553.0 1078660.0 999968.0 1935350.0 1375880.0 1302060.0
#> 2 640577.0 548706.0 603278.0 759629.0 728751.0 1391630.0 948596.0
#> 3 256771.0 368443.0 308663.0 338604.0 431979.0 422976.0 820717.0
#> 4 186122.0 141111.0 188898.0 147121.0 156042.0 206296.0 214367.0
#> 5 153084.0 102391.0 74240.4 94918.5 71554.5 75327.9 100486.0
#> 6 113431.0 83438.8 54395.5 39883.2 51872.3 38991.5 40129.1
#> 7 17442.0 63121.2 45292.9 30397.5 23273.3 31032.9 23210.4
#> 8 12952.3 10501.4 37093.0 26405.0 18034.5 14363.0 19675.9
#> 9 13395.2 8318.7 6668.8 23029.0 16239.1 11328.1 9269.7
#> 10 33351.6 31239.8 26643.8 22198.4 29551.0 29398.7 26037.4
#> year
#> age 1985 1986 1987 1988 1989 1990 1991
#> 1 1792220.0 4303680.0 1910200.0 1774940.0 1250510.0 1083810.0 981356.0
#> 2 864871.0 1177750.0 2866780.0 1308470.0 1251000.0 898824.0 785747.0
#> 3 559870.0 503176.0 670566.0 1602170.0 730349.0 716388.0 530410.0
#> 4 429744.0 291704.0 253291.0 320928.0 743636.0 347377.0 357105.0
#> 5 105579.0 212338.0 142492.0 120680.0 150385.0 353208.0 169944.0
#> 6 51597.9 51971.1 101004.0 66591.7 55845.9 68749.9 159986.0
#> 7 22812.3 27066.0 25237.6 47264.9 30819.2 25576.2 30985.4
#> 8 14467.2 13364.5 14671.0 12950.5 23964.9 16156.4 13837.3
#> 9 12765.7 9167.3 8060.4 8304.2 7096.0 13682.2 9708.3
#> 10 22968.3 23691.3 21446.4 18014.1 15057.6 12910.2 16297.5
#> year
#> age 1992 1993 1994 1995 1996 1997 1998
#> 1 854841.0 550376.0 566448.0 932162.0 893056.0 2431310.0 778427.0
#> 2 709583.0 614021.0 398693.0 423980.0 722992.0 708162.0 1941390.0
#> 3 465814.0 412376.0 355244.0 239117.0 261727.0 433954.0 398394.0
#> 4 268732.0 230658.0 199926.0 173831.0 115825.0 114808.0 163844.0
#> 5 177919.0 133599.0 113279.0 96739.9 81895.9 51781.9 48217.9
#> 6 77446.0 82752.7 63227.4 53666.9 45201.5 37345.9 22897.6
#> 7 70809.8 33854.3 36415.8 28660.1 24870.8 20578.7 16297.6
#> 8 16300.8 33881.2 15013.9 16696.8 14022.1 12186.0 9697.5
#> 9 8133.4 8552.4 15995.5 7339.9 8872.5 7570.6 6374.5
#> 10 15761.0 13206.8 11093.7 14408.1 12542.4 12623.6 11589.9
#> year
#> age 1999 2000 2001 2002 2003 2004 2005
#> 1 683151.0 857525.0 634808.0 NA NA NA 863893.0
#> 2 617294.0 531403.0 653888.0 NA NA NA 894667.0
#> 3 1091830.0 381345.0 349511.0 NA NA NA 238706.0
#> 4 151199.0 510105.0 205661.0 NA NA NA 365617.0
#> 5 67219.3 64650.9 232564.0 NA NA NA 66113.3
#> 6 20631.8 28114.9 27483.5 NA NA NA 57107.9
#> 7 9911.4 9398.3 13484.8 NA NA NA 29565.3
#> 8 7927.6 5445.6 5587.2 NA NA NA 16720.1
#> 9 5219.9 4778.1 3568.0 NA NA NA 20533.5
#> 10 10285.7 9459.5 9463.7 NA NA NA 12764.9
#> year
#> age 2006 2007 2008 2009 2010 2011 2012
#> 1 875191.0 1379750.0 1135050.0 1088820.0 1444570.0 1608190.0 1278010.0
#> 2 618023.0 651007.0 1067080.0 879336.0 826492.0 1109460.0 1299830.0
#> 3 552652.0 393107.0 420759.0 719298.0 627121.0 613580.0 837139.0
#> 4 136058.0 326369.0 234965.0 266894.0 493740.0 448410.0 436124.0
#> 5 219194.0 87128.7 219954.0 163268.0 188123.0 349991.0 318435.0
#> 6 41505.0 148406.0 62707.2 164048.0 123503.0 142101.0 261180.0
#> 7 37777.6 29245.7 109232.0 48044.5 129629.0 98702.9 112594.0
#> 8 21233.8 28310.9 22299.1 85182.6 38534.8 106302.0 81974.3
#> 9 12941.5 17026.1 23120.2 18460.8 71434.6 32718.3 91209.7
#> 10 27199.2 33875.1 43959.2 58555.1 67452.5 121698.0 135291.0
#> year
#> age 2013 2014 2015 2016 2017
#> 1 1455050.0 1640700.0 895620.0 1211320.0 1823000.0
#> 2 1063130.0 1193580.0 1286330.0 689025.0 973599.0
#> 3 980853.0 793858.0 880738.0 948914.0 515997.0
#> 4 582663.0 681616.0 559741.0 626799.0 672732.0
#> 5 309658.0 413410.0 483694.0 398150.0 447979.0
#> 6 234879.0 228655.0 308142.0 362736.0 298156.0
#> 7 204189.0 183635.0 180488.0 244483.0 286575.0
#> 8 93937.9 169885.0 151867.0 148885.0 202450.0
#> 9 70713.1 80901.8 145583.0 130001.0 128177.0
#> 10 198423.0 235842.0 277893.0 372467.0 443492.0
#>
#> units: 1000
# Appends after end of x
stock.n(append(fs1, fs3, after=2005))
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> age 1957 1958 1959 1960 1961 1962 1963
#> 1 477074.0 710748.0 874712.0 797702.0 870799.0 615691.0 610017.0
#> 2 268185.0 391105.0 574019.0 696497.0 629331.0 688002.0 494583.0
#> 3 361589.0 204551.0 286216.0 404963.0 485155.0 441333.0 482883.0
#> 4 223134.0 251892.0 135739.0 182546.0 255875.0 308828.0 276136.0
#> 5 147904.0 148258.0 164040.0 87017.8 116368.0 163035.0 195022.0
#> 6 60608.8 103574.0 100146.0 107321.0 55977.1 74519.9 103605.0
#> 7 62627.2 44411.7 73005.1 68029.4 71202.0 36669.7 47899.3
#> 8 49418.3 45231.8 32053.7 52465.8 48326.9 49594.8 24891.5
#> 9 31996.7 35608.8 32755.0 23308.8 38248.3 35143.0 35587.0
#> 10 68585.1 74612.3 79021.3 78690.9 73222.3 82791.3 88328.2
#> year
#> age 1964 1965 1966 1967 1968 1969 1970
#> 1 2449900.0 664500.0 579075.0 428110.0 418228.0 666902.0 671454.0
#> 2 502620.0 2057370.0 561599.0 486279.0 351300.0 328477.0 499525.0
#> 3 341643.0 343934.0 1437490.0 402660.0 346712.0 239678.0 214078.0
#> 4 283950.0 189574.0 194255.0 862270.0 249951.0 214175.0 146130.0
#> 5 170312.0 170953.0 113379.0 117592.0 533809.0 158259.0 137189.0
#> 6 121552.0 104036.0 103728.0 69241.0 72675.5 333787.0 99805.0
#> 7 64386.7 73564.1 63413.5 64736.1 43515.6 44971.0 204065.0
#> 8 31671.7 42047.6 48694.7 42848.4 43754.3 28686.1 28864.1
#> 9 17425.6 21773.1 29156.9 34459.9 30486.1 30610.0 19555.5
#> 10 89949.7 74801.9 67158.6 68866.5 75287.6 76834.4 76614.6
#> year
#> age 1971 1972 1973 1974 1975 1976 1977
#> 1 433599.0 367450.0 1391430.0 1074920.0 787372.0 674010.0 1033740.0
#> 2 495583.0 322847.0 274502.0 1025520.0 767410.0 534441.0 432591.0
#> 3 324931.0 328032.0 212726.0 175441.0 635424.0 469588.0 324352.0
#> 4 130830.0 198448.0 194134.0 119185.0 95486.7 354654.0 270674.0
#> 5 92885.0 80660.2 115448.0 105314.0 62528.3 51565.8 199395.0
#> 6 86638.5 57736.0 47858.4 64234.3 56842.0 34795.5 29688.2
#> 7 61697.5 54135.3 35370.6 28087.4 36940.6 33525.3 20971.2
#> 8 130295.0 39537.1 34377.0 21998.3 17263.3 22879.5 20930.5
#> 9 19273.9 85654.3 25584.3 21899.6 13875.4 10882.2 14512.6
#> 10 66652.6 57588.9 92986.6 75118.4 60677.9 46683.2 36597.8
#> year
#> age 1978 1979 1980 1981 1982 1983 1984
#> 1 879043.0 915553.0 1078660.0 999968.0 1935350.0 1375880.0 1302060.0
#> 2 640577.0 548706.0 603278.0 759629.0 728751.0 1391630.0 948596.0
#> 3 256771.0 368443.0 308663.0 338604.0 431979.0 422976.0 820717.0
#> 4 186122.0 141111.0 188898.0 147121.0 156042.0 206296.0 214367.0
#> 5 153084.0 102391.0 74240.4 94918.5 71554.5 75327.9 100486.0
#> 6 113431.0 83438.8 54395.5 39883.2 51872.3 38991.5 40129.1
#> 7 17442.0 63121.2 45292.9 30397.5 23273.3 31032.9 23210.4
#> 8 12952.3 10501.4 37093.0 26405.0 18034.5 14363.0 19675.9
#> 9 13395.2 8318.7 6668.8 23029.0 16239.1 11328.1 9269.7
#> 10 33351.6 31239.8 26643.8 22198.4 29551.0 29398.7 26037.4
#> year
#> age 1985 1986 1987 1988 1989 1990 1991
#> 1 1792220.0 4303680.0 1910200.0 1774940.0 1250510.0 1083810.0 981356.0
#> 2 864871.0 1177750.0 2866780.0 1308470.0 1251000.0 898824.0 785747.0
#> 3 559870.0 503176.0 670566.0 1602170.0 730349.0 716388.0 530410.0
#> 4 429744.0 291704.0 253291.0 320928.0 743636.0 347377.0 357105.0
#> 5 105579.0 212338.0 142492.0 120680.0 150385.0 353208.0 169944.0
#> 6 51597.9 51971.1 101004.0 66591.7 55845.9 68749.9 159986.0
#> 7 22812.3 27066.0 25237.6 47264.9 30819.2 25576.2 30985.4
#> 8 14467.2 13364.5 14671.0 12950.5 23964.9 16156.4 13837.3
#> 9 12765.7 9167.3 8060.4 8304.2 7096.0 13682.2 9708.3
#> 10 22968.3 23691.3 21446.4 18014.1 15057.6 12910.2 16297.5
#> year
#> age 1992 1993 1994 1995 1996 1997 1998
#> 1 854841.0 550376.0 566448.0 932162.0 893056.0 2431310.0 778427.0
#> 2 709583.0 614021.0 398693.0 423980.0 722992.0 708162.0 1941390.0
#> 3 465814.0 412376.0 355244.0 239117.0 261727.0 433954.0 398394.0
#> 4 268732.0 230658.0 199926.0 173831.0 115825.0 114808.0 163844.0
#> 5 177919.0 133599.0 113279.0 96739.9 81895.9 51781.9 48217.9
#> 6 77446.0 82752.7 63227.4 53666.9 45201.5 37345.9 22897.6
#> 7 70809.8 33854.3 36415.8 28660.1 24870.8 20578.7 16297.6
#> 8 16300.8 33881.2 15013.9 16696.8 14022.1 12186.0 9697.5
#> 9 8133.4 8552.4 15995.5 7339.9 8872.5 7570.6 6374.5
#> 10 15761.0 13206.8 11093.7 14408.1 12542.4 12623.6 11589.9
#> year
#> age 1999 2000 2001 2002 2003 2004 2005
#> 1 683151.0 857525.0 634808.0 NA NA NA 863893.0
#> 2 617294.0 531403.0 653888.0 NA NA NA 894667.0
#> 3 1091830.0 381345.0 349511.0 NA NA NA 238706.0
#> 4 151199.0 510105.0 205661.0 NA NA NA 365617.0
#> 5 67219.3 64650.9 232564.0 NA NA NA 66113.3
#> 6 20631.8 28114.9 27483.5 NA NA NA 57107.9
#> 7 9911.4 9398.3 13484.8 NA NA NA 29565.3
#> 8 7927.6 5445.6 5587.2 NA NA NA 16720.1
#> 9 5219.9 4778.1 3568.0 NA NA NA 20533.5
#> 10 10285.7 9459.5 9463.7 NA NA NA 12764.9
#> year
#> age 2006 2007 2008 2009 2010 2011 2012
#> 1 875191.0 1379750.0 1135050.0 1088820.0 1444570.0 1608190.0 1278010.0
#> 2 618023.0 651007.0 1067080.0 879336.0 826492.0 1109460.0 1299830.0
#> 3 552652.0 393107.0 420759.0 719298.0 627121.0 613580.0 837139.0
#> 4 136058.0 326369.0 234965.0 266894.0 493740.0 448410.0 436124.0
#> 5 219194.0 87128.7 219954.0 163268.0 188123.0 349991.0 318435.0
#> 6 41505.0 148406.0 62707.2 164048.0 123503.0 142101.0 261180.0
#> 7 37777.6 29245.7 109232.0 48044.5 129629.0 98702.9 112594.0
#> 8 21233.8 28310.9 22299.1 85182.6 38534.8 106302.0 81974.3
#> 9 12941.5 17026.1 23120.2 18460.8 71434.6 32718.3 91209.7
#> 10 27199.2 33875.1 43959.2 58555.1 67452.5 121698.0 135291.0
#> year
#> age 2013 2014 2015 2016 2017 2018
#> 1 1455050.0 1640700.0 895620.0 1211320.0 1823000.0 NA
#> 2 1063130.0 1193580.0 1286330.0 689025.0 973599.0 NA
#> 3 980853.0 793858.0 880738.0 948914.0 515997.0 NA
#> 4 582663.0 681616.0 559741.0 626799.0 672732.0 NA
#> 5 309658.0 413410.0 483694.0 398150.0 447979.0 NA
#> 6 234879.0 228655.0 308142.0 362736.0 298156.0 NA
#> 7 204189.0 183635.0 180488.0 244483.0 286575.0 NA
#> 8 93937.9 169885.0 151867.0 148885.0 202450.0 NA
#> 9 70713.1 80901.8 145583.0 130001.0 128177.0 NA
#> 10 198423.0 235842.0 277893.0 372467.0 443492.0 NA
#>
#> units: 1000