Objects of various FLCore classes can be converted into other classes,
both basic R ones, like data.frame, and others defined in the package. For
the specifics of the precise calculations carried out for each pair of
classes, see below.
Object to be converted.
Name of the class to convert the object to, character.
An object of the requested class.
The six dimensions of an `FLArray` are converted into seven columns, named `quant` (or any other name given to the first dimension in the object), `year`, `unit`, `season`, `area`, `iter` and `data`. The last one contains the actual numbers stored in the array. `units` are stored as an attribute to the `data.frame`. The `year` and `data` columns are of type `numeric`, while all others are `factor`.
The two or more dimensions of an *FLPar* objects are converted into three or more columns. For a 2D objects, they are named *params*, *iter* and *data*. The last one contains the actual numbers stored in the array, in a column type `numeric`, while all others are `factor`.
base::as, base::coerce
# from FLQuant to data.frame
as(FLQuant(rnorm(100), dim=c(5, 20)), "data.frame")
#> quant year unit season area iter data
#> 1 1 1 unique all unique 1 -0.971904290
#> 2 2 1 unique all unique 1 -0.920309240
#> 3 3 1 unique all unique 1 0.112784256
#> 4 4 1 unique all unique 1 0.814959646
#> 5 5 1 unique all unique 1 -0.348145715
#> 6 1 2 unique all unique 1 1.834351127
#> 7 2 2 unique all unique 1 1.345839592
#> 8 3 2 unique all unique 1 1.075077171
#> 9 4 2 unique all unique 1 -0.916756085
#> 10 5 2 unique all unique 1 -0.388359689
#> 11 1 3 unique all unique 1 -1.765808552
#> 12 2 3 unique all unique 1 -0.575587103
#> 13 3 3 unique all unique 1 0.530274794
#> 14 4 3 unique all unique 1 0.075310115
#> 15 5 3 unique all unique 1 0.380117900
#> 16 1 4 unique all unique 1 0.629893380
#> 17 2 4 unique all unique 1 -0.269176963
#> 18 3 4 unique all unique 1 -0.040169717
#> 19 4 4 unique all unique 1 -0.473780135
#> 20 5 4 unique all unique 1 1.111027929
#> 21 1 5 unique all unique 1 -0.727567069
#> 22 2 5 unique all unique 1 0.600876811
#> 23 3 5 unique all unique 1 -0.868420752
#> 24 4 5 unique all unique 1 -0.114403554
#> 25 5 5 unique all unique 1 0.017370018
#> 26 1 6 unique all unique 1 -0.707514215
#> 27 2 6 unique all unique 1 -0.883215610
#> 28 3 6 unique all unique 1 -0.475814830
#> 29 4 6 unique all unique 1 1.011417120
#> 30 5 6 unique all unique 1 -0.326712913
#> 31 1 7 unique all unique 1 -0.164163753
#> 32 2 7 unique all unique 1 -0.144054819
#> 33 3 7 unique all unique 1 0.279944358
#> 34 4 7 unique all unique 1 -0.057611432
#> 35 5 7 unique all unique 1 0.625955675
#> 36 1 8 unique all unique 1 0.024183019
#> 37 2 8 unique all unique 1 0.937531395
#> 38 3 8 unique all unique 1 0.924879628
#> 39 4 8 unique all unique 1 0.536961692
#> 40 5 8 unique all unique 1 0.570516933
#> 41 1 9 unique all unique 1 0.349504683
#> 42 2 9 unique all unique 1 0.835901600
#> 43 3 9 unique all unique 1 -0.148895762
#> 44 4 9 unique all unique 1 -0.763061416
#> 45 5 9 unique all unique 1 1.930156525
#> 46 1 10 unique all unique 1 0.618235224
#> 47 2 10 unique all unique 1 0.168878103
#> 48 3 10 unique all unique 1 -1.879957768
#> 49 4 10 unique all unique 1 0.040721634
#> 50 5 10 unique all unique 1 -0.988549352
#> 51 1 11 unique all unique 1 -1.034635369
#> 52 2 11 unique all unique 1 -0.503174445
#> 53 3 11 unique all unique 1 0.478393318
#> 54 4 11 unique all unique 1 0.209380248
#> 55 5 11 unique all unique 1 -0.808264817
#> 56 1 12 unique all unique 1 1.312909089
#> 57 2 12 unique all unique 1 1.579021303
#> 58 3 12 unique all unique 1 0.006925585
#> 59 4 12 unique all unique 1 -0.077691086
#> 60 5 12 unique all unique 1 0.946595282
#> 61 1 13 unique all unique 1 0.324063803
#> 62 2 13 unique all unique 1 0.773310943
#> 63 3 13 unique all unique 1 0.399921554
#> 64 4 13 unique all unique 1 -1.354917785
#> 65 5 13 unique all unique 1 -0.024216296
#> 66 1 14 unique all unique 1 0.194480430
#> 67 2 14 unique all unique 1 -0.240357670
#> 68 3 14 unique all unique 1 2.103750277
#> 69 4 14 unique all unique 1 -1.643410992
#> 70 5 14 unique all unique 1 0.251751922
#> 71 1 15 unique all unique 1 0.402445563
#> 72 2 15 unique all unique 1 -1.563393477
#> 73 3 15 unique all unique 1 0.387807703
#> 74 4 15 unique all unique 1 -0.226803822
#> 75 5 15 unique all unique 1 0.398417982
#> 76 1 16 unique all unique 1 0.062722277
#> 77 2 16 unique all unique 1 -1.054667408
#> 78 3 16 unique all unique 1 -0.996071714
#> 79 4 16 unique all unique 1 0.727946839
#> 80 5 16 unique all unique 1 0.832254114
#> 81 1 17 unique all unique 1 -1.293235374
#> 82 2 17 unique all unique 1 2.834481167
#> 83 3 17 unique all unique 1 0.110986298
#> 84 4 17 unique all unique 1 -1.611360820
#> 85 5 17 unique all unique 1 0.338446429
#> 86 1 18 unique all unique 1 -0.661012400
#> 87 2 18 unique all unique 1 -1.565615294
#> 88 3 18 unique all unique 1 0.619077438
#> 89 4 18 unique all unique 1 0.396163021
#> 90 5 18 unique all unique 1 0.464085846
#> 91 1 19 unique all unique 1 -1.052961468
#> 92 2 19 unique all unique 1 -1.069160603
#> 93 3 19 unique all unique 1 -0.188303536
#> 94 4 19 unique all unique 1 2.077527159
#> 95 5 19 unique all unique 1 0.783749767
#> 96 1 20 unique all unique 1 0.962378322
#> 97 2 20 unique all unique 1 0.516531552
#> 98 3 20 unique all unique 1 -0.164192086
#> 99 4 20 unique all unique 1 0.273754909
#> 100 5 20 unique all unique 1 -0.851713993
# from FLPar to data.frame
as(FLPar(phi=rnorm(10), rho=rlnorm(10)), "data.frame")
#> params iter data
#> 1 phi 1 0.230041462
#> 2 rho 1 1.250644437
#> 3 phi 2 -0.916979988
#> 4 rho 2 0.891909492
#> 5 phi 3 -0.153234163
#> 6 rho 3 1.159200049
#> 7 phi 4 -1.200725783
#> 8 rho 4 1.227119744
#> 9 phi 5 -0.002197989
#> 10 rho 5 2.869683545
#> 11 phi 6 0.705192144
#> 12 rho 6 0.842707296
#> 13 phi 7 -0.084626146
#> 14 rho 7 1.325474034
#> 15 phi 8 -0.227795958
#> 16 rho 8 0.830076993
#> 17 phi 9 0.274141643
#> 18 rho 9 0.281066891
#> 19 phi 10 -1.648381367
#> 20 rho 10 0.806694042