Methods to compute totals over selected dimensions of FLQuant objects
These methods return an object of same dimensions as the input but with the
sums along the first (yearTotals) or second dimension
(quantTotals). Although the names might appear contradictory, it must
be noted that what each method really returns are the totals over the
selected dimension.
Examples
flq <- FLQuant(rlnorm(100), dim=c(10,10))
quantTotals(flq)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7 8 9
#> 1 29.7234 29.7234 29.7234 29.7234 29.7234 29.7234 29.7234 29.7234 29.7234
#> 2 10.4986 10.4986 10.4986 10.4986 10.4986 10.4986 10.4986 10.4986 10.4986
#> 3 12.8588 12.8588 12.8588 12.8588 12.8588 12.8588 12.8588 12.8588 12.8588
#> 4 40.1999 40.1999 40.1999 40.1999 40.1999 40.1999 40.1999 40.1999 40.1999
#> 5 22.2456 22.2456 22.2456 22.2456 22.2456 22.2456 22.2456 22.2456 22.2456
#> 6 10.3951 10.3951 10.3951 10.3951 10.3951 10.3951 10.3951 10.3951 10.3951
#> 7 10.0079 10.0079 10.0079 10.0079 10.0079 10.0079 10.0079 10.0079 10.0079
#> 8 11.3852 11.3852 11.3852 11.3852 11.3852 11.3852 11.3852 11.3852 11.3852
#> 9 7.5651 7.5651 7.5651 7.5651 7.5651 7.5651 7.5651 7.5651 7.5651
#> 10 31.4908 31.4908 31.4908 31.4908 31.4908 31.4908 31.4908 31.4908 31.4908
#> year
#> quant 10
#> 1 29.7234
#> 2 10.4986
#> 3 12.8588
#> 4 40.1999
#> 5 22.2456
#> 6 10.3951
#> 7 10.0079
#> 8 11.3852
#> 9 7.5651
#> 10 31.4908
#>
#> units: NA
# See how the values obtained by yearSums are being replicated
yearSums(flq)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1
#> 1 29.7234
#> 2 10.4986
#> 3 12.8588
#> 4 40.1999
#> 5 22.2456
#> 6 10.3951
#> 7 10.0079
#> 8 11.3852
#> 9 7.5651
#> 10 31.4908
#>
#> units: NA
# Get the proportions by quant
flq / quantTotals(flq)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7
#> 1 0.0102262 0.4740858 0.0052518 0.0399841 0.0238343 0.2528938 0.0535763
#> 2 0.0308577 0.1111179 0.0117361 0.1419445 0.1832639 0.0645143 0.0764293
#> 3 0.0661400 0.0686662 0.2943833 0.2626021 0.0742639 0.0356456 0.0683202
#> 4 0.0114894 0.0386392 0.0554091 0.1187885 0.3976189 0.0579956 0.1261062
#> 5 0.0343655 0.0072476 0.0463921 0.4746251 0.0651319 0.0514096 0.0452111
#> 6 0.0134776 0.1531387 0.1146877 0.2798408 0.0854058 0.0354065 0.0446041
#> 7 0.0893084 0.0598781 0.0407770 0.0055078 0.1314200 0.1213795 0.0752119
#> 8 0.0760796 0.0406151 0.0218903 0.0293232 0.0382301 0.1044732 0.0959325
#> 9 0.1644812 0.1840483 0.0507512 0.0550968 0.0676102 0.1854379 0.0987736
#> 10 0.0142279 0.0149324 0.0298655 0.0199528 0.0993833 0.0047344 0.0955929
#> year
#> quant 8 9 10
#> 1 0.0023086 0.0489219 0.0889172
#> 2 0.0849537 0.2456885 0.0494942
#> 3 0.0144158 0.0390966 0.0764664
#> 4 0.0209226 0.1361284 0.0369019
#> 5 0.0285275 0.2148501 0.0322396
#> 6 0.0702897 0.0685988 0.1345503
#> 7 0.0233273 0.3338459 0.1193441
#> 8 0.2744723 0.1059551 0.2130286
#> 9 0.0260751 0.1106233 0.0571024
#> 10 0.0281396 0.6744582 0.0187129
#>
#> units: NA
# or year
flq / yearTotals(flq)
#> An object of class "FLQuant"
#> , , unit = unique, season = all, area = unique
#>
#> year
#> quant 1 2 3 4 5 6 7
#> 1 0.0482686 0.6298791 0.0148693 0.0461859 0.0259501 0.4569021 0.1033083
#> 2 0.0514455 0.0521457 0.0117366 0.0579128 0.0704769 0.0411694 0.0520543
#> 3 0.1350576 0.0394682 0.3605810 0.1312272 0.0349798 0.0278609 0.0569923
#> 4 0.0733459 0.0694312 0.2121745 0.1855765 0.5855035 0.1417120 0.3288711
#> 5 0.1214002 0.0072068 0.0983053 0.4103167 0.0530733 0.0695146 0.0652462
#> 6 0.0222483 0.0711569 0.1135625 0.1130486 0.0325204 0.0223718 0.0300794
#> 7 0.1419344 0.0267863 0.0388728 0.0021421 0.0481772 0.0738371 0.0488308
#> 8 0.1375499 0.0206695 0.0237399 0.0129740 0.0159435 0.0722988 0.0708549
#> 9 0.1975992 0.0622372 0.0365721 0.0161982 0.0187355 0.0852711 0.0484755
#> 10 0.0711504 0.0210191 0.0895860 0.0244180 0.1146397 0.0090623 0.1952872
#> year
#> quant 8 9 10
#> 1 0.0088042 0.0345196 0.2133771
#> 2 0.1144333 0.0612323 0.0419517
#> 3 0.0237836 0.0119345 0.0793846
#> 4 0.1079141 0.1299083 0.1197671
#> 5 0.0814228 0.1134602 0.0579026
#> 6 0.0937477 0.0169282 0.1129221
#> 7 0.0299533 0.0793145 0.0964290
#> 8 0.4009371 0.0286368 0.1958129
#> 9 0.0253093 0.0198667 0.0348766
#> 10 0.1136946 0.5041989 0.0475761
#>
#> units: NA