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Extend a FLR object along the year dimension and set future assumed values

Source: R/genericMethods.R, R/FLStock.R
fwdWindow.Rd

Objects to be projected into the future are extended until an end year, and the values of certain quantities, usually assume constant, are set following different mechanisms.

Usage

fwdWindow(x, y, ...)

# S4 method for FLStock,missing
fwdWindow(
  x,
  end = dims(x)$maxyear,
  nsq = 3,
  fun = c("mean", "sample"),
  years = list(wt = nsq, mat = nsq, m = nsq, spwn = nsq, discards.ratio = nsq, catch.sel
    = nsq)
)

Arguments

x

The FLR object to extend.

y

A second object from which information is taken.

Value

An object of the same class as 'x'.

Details

For 'FLStock'

See also

window()

Author

The FLR Team.

Examples

data(ple4)
# Use mean of last three years and extend until 2020
fut <- fwdWindow(ple4, end=2020)
# Check values on catch.wt
catch.wt(fut)[, ac(2015:2020)]
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#> 
#>     year
#> age  2015     2016     2017     2018     2019     2020    
#>   1  0.026000 0.048000 0.051034 0.041678 0.041678 0.041678
#>   2  0.080271 0.083793 0.085895 0.083320 0.083320 0.083320
#>   3  0.161845 0.157402 0.158552 0.159266 0.159266 0.159266
#>   4  0.257901 0.243039 0.218409 0.239783 0.239783 0.239783
#>   5  0.325621 0.298878 0.314393 0.312964 0.312964 0.312964
#>   6  0.393707 0.352469 0.386271 0.377482 0.377482 0.377482
#>   7  0.461250 0.422309 0.437717 0.440425 0.440425 0.440425
#>   8  0.480815 0.465186 0.531501 0.492501 0.492501 0.492501
#>   9  0.581978 0.555971 0.641979 0.593309 0.593309 0.593309
#>   10 0.599938 0.683933 0.734886 0.672919 0.672919 0.672919
#> 
#> units:  kg 
# Use mean of the 2010:2015 period
fut <- fwdWindow(ple4, end=2020, years=2010:2015)
# Use last three years mean, but last five for 'wt'
fut <- fwdWindow(ple4, end=2020, nsq=3, years=list(wt=5))
stock.wt(fut)[, ac(2013:2020)]
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#> 
#>     year
#> age  2013   2014   2015   2016   2017   2018   2019   2020  
#>   1  0.0430 0.0480 0.0240 0.0300 0.0320 0.0354 0.0354 0.0354
#>   2  0.1070 0.1040 0.0650 0.0660 0.0690 0.0822 0.0822 0.0822
#>   3  0.1530 0.1580 0.1200 0.1170 0.1320 0.1360 0.1360 0.1360
#>   4  0.2080 0.2020 0.2070 0.1980 0.1810 0.1992 0.1992 0.1992
#>   5  0.3200 0.3120 0.2790 0.2600 0.2700 0.2882 0.2882 0.2882
#>   6  0.3540 0.3800 0.3230 0.3290 0.3330 0.3438 0.3438 0.3438
#>   7  0.4340 0.4390 0.3790 0.3800 0.3590 0.3982 0.3982 0.3982
#>   8  0.4930 0.4840 0.4350 0.4340 0.4580 0.4608 0.4608 0.4608
#>   9  0.6620 0.4580 0.4650 0.4790 0.4760 0.5080 0.5080 0.5080
#>   10 0.4680 0.6150 0.4570 0.5140 0.5570 0.5222 0.5222 0.5222
#> 
#> units:  kg 
catch.sel(fut)[, ac(2013:2020)]
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#> 
#>     year
#> age  2013     2014     2015     2016     2017     2018     2019     2020    
#>   1  0.371582 0.574650 0.674177 0.485580 0.268807 0.472493 0.472493 0.472493
#>   2  0.727648 0.817662 0.848662 0.775415 0.659347 0.759736 0.759736 0.759736
#>   3  1.000000 1.000000 0.997872 1.000000 1.000000 1.000000 1.000000 1.000000
#>   4  0.921248 0.974274 1.000000 0.966840 0.901408 0.955721 0.955721 0.955721
#>   5  0.770034 0.777282 0.780296 0.775554 0.763304 0.773407 0.773407 0.773407
#>   6  0.553585 0.547440 0.546097 0.556122 0.570028 0.558043 0.558043 0.558043
#>   7  0.317959 0.360592 0.384355 0.363367 0.320835 0.355771 0.355771 0.355771
#>   8  0.187146 0.217980 0.230463 0.203954 0.163486 0.198758 0.198758 0.198758
#>   9  0.121437 0.123710 0.117807 0.101872 0.083357 0.100739 0.100739 0.100739
#>   10 0.121437 0.123710 0.117807 0.101872 0.083357 0.100739 0.100739 0.100739
#> 
#> units:  NA 
# Resample from last years for 'wt'
fut <- fwdWindow(ple4, end=2020, nsq=3, fun=c(wt='sample'))
# Years to resample can be different for 'catch.sel'
fut <- fwdWindow(ple4, end=2020, nsq=3,
  fun=c(wt='sample', catch.sel='sample'), years=c(wt=10, catch.sel=5))
# 'wt' slot has been resampled,
stock.wt(fut)[, ac(2015:2020)]
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#> 
#>     year
#> age  2015  2016  2017  2018  2019  2020 
#>   1  0.024 0.030 0.032 0.024 0.053 0.053
#>   2  0.065 0.066 0.069 0.065 0.116 0.116
#>   3  0.120 0.117 0.132 0.120 0.179 0.179
#>   4  0.207 0.198 0.181 0.207 0.340 0.340
#>   5  0.279 0.260 0.270 0.279 0.361 0.361
#>   6  0.323 0.329 0.333 0.323 0.401 0.401
#>   7  0.379 0.380 0.359 0.379 0.448 0.448
#>   8  0.435 0.434 0.458 0.435 0.572 0.572
#>   9  0.465 0.479 0.476 0.465 0.568 0.568
#>   10 0.457 0.514 0.557 0.457 0.655 0.655
#> 
#> units:  kg 
# while others have used a 3 year average
catch.sel(fut)[, ac(2015:2020)]
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#> 
#>     year
#> age  2015     2016     2017     2018     2019     2020    
#>   1  0.674177 0.485580 0.268807 0.674177 0.574650 0.371582
#>   2  0.848662 0.775415 0.659347 0.848662 0.817662 0.727648
#>   3  0.997872 1.000000 1.000000 0.997872 1.000000 1.000000
#>   4  1.000000 0.966840 0.901408 1.000000 0.974274 0.921248
#>   5  0.780296 0.775554 0.763304 0.780296 0.777282 0.770034
#>   6  0.546097 0.556122 0.570028 0.546097 0.547440 0.553585
#>   7  0.384355 0.363367 0.320835 0.384355 0.360592 0.317959
#>   8  0.230463 0.203954 0.163486 0.230463 0.217980 0.187146
#>   9  0.117807 0.101872 0.083357 0.117807 0.123710 0.121437
#>   10 0.117807 0.101872 0.083357 0.117807 0.123710 0.121437
#> 
#> units:  NA