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.
Arguments
- object
Object to be converted.
- Class
Name of the class to convert the object to,
character
.
FLArray to data.frame
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
.
FLPar to data.frame
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
.
Examples
# 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 1.78763033
#> 2 2 1 unique all unique 1 1.29512026
#> 3 3 1 unique all unique 1 1.34458461
#> 4 4 1 unique all unique 1 -0.50866954
#> 5 5 1 unique all unique 1 -1.12067009
#> 6 1 2 unique all unique 1 2.15080494
#> 7 2 2 unique all unique 1 -0.56171975
#> 8 3 2 unique all unique 1 -1.31150938
#> 9 4 2 unique all unique 1 0.28638227
#> 10 5 2 unique all unique 1 0.48502761
#> 11 1 3 unique all unique 1 -0.71824346
#> 12 2 3 unique all unique 1 0.16284173
#> 13 3 3 unique all unique 1 0.10048785
#> 14 4 3 unique all unique 1 0.37686006
#> 15 5 3 unique all unique 1 0.22373575
#> 16 1 4 unique all unique 1 1.51603757
#> 17 2 4 unique all unique 1 1.26931582
#> 18 3 4 unique all unique 1 -0.13913783
#> 19 4 4 unique all unique 1 0.60704860
#> 20 5 4 unique all unique 1 -0.25928567
#> 21 1 5 unique all unique 1 -0.59147634
#> 22 2 5 unique all unique 1 -0.94550205
#> 23 3 5 unique all unique 1 0.23658792
#> 24 4 5 unique all unique 1 -0.09734917
#> 25 5 5 unique all unique 1 2.04488154
#> 26 1 6 unique all unique 1 1.20847318
#> 27 2 6 unique all unique 1 -1.83053363
#> 28 3 6 unique all unique 1 0.64945194
#> 29 4 6 unique all unique 1 1.00227863
#> 30 5 6 unique all unique 1 -1.35915129
#> 31 1 7 unique all unique 1 0.19702464
#> 32 2 7 unique all unique 1 -2.15306430
#> 33 3 7 unique all unique 1 -1.77982626
#> 34 4 7 unique all unique 1 -1.25637544
#> 35 5 7 unique all unique 1 1.28399957
#> 36 1 8 unique all unique 1 0.70016193
#> 37 2 8 unique all unique 1 0.41712779
#> 38 3 8 unique all unique 1 -0.09821147
#> 39 4 8 unique all unique 1 1.23387018
#> 40 5 8 unique all unique 1 0.38769108
#> 41 1 9 unique all unique 1 -0.16425606
#> 42 2 9 unique all unique 1 -0.41694062
#> 43 3 9 unique all unique 1 -0.01955970
#> 44 4 9 unique all unique 1 -1.28679411
#> 45 5 9 unique all unique 1 -1.06172725
#> 46 1 10 unique all unique 1 -0.65346943
#> 47 2 10 unique all unique 1 0.54605449
#> 48 3 10 unique all unique 1 -0.55388324
#> 49 4 10 unique all unique 1 1.08120000
#> 50 5 10 unique all unique 1 0.61657147
#> 51 1 11 unique all unique 1 0.10733408
#> 52 2 11 unique all unique 1 -0.00991494
#> 53 3 11 unique all unique 1 -0.77790812
#> 54 4 11 unique all unique 1 1.16752702
#> 55 5 11 unique all unique 1 0.43972735
#> 56 1 12 unique all unique 1 -0.79019806
#> 57 2 12 unique all unique 1 -1.23396101
#> 58 3 12 unique all unique 1 0.43384015
#> 59 4 12 unique all unique 1 0.28184582
#> 60 5 12 unique all unique 1 -1.73771157
#> 61 1 13 unique all unique 1 -0.85059636
#> 62 2 13 unique all unique 1 1.86252316
#> 63 3 13 unique all unique 1 1.23862928
#> 64 4 13 unique all unique 1 -0.59566744
#> 65 5 13 unique all unique 1 0.91076876
#> 66 1 14 unique all unique 1 -0.26576998
#> 67 2 14 unique all unique 1 0.31774589
#> 68 3 14 unique all unique 1 0.37823141
#> 69 4 14 unique all unique 1 -0.90150371
#> 70 5 14 unique all unique 1 1.21432212
#> 71 1 15 unique all unique 1 -0.22544166
#> 72 2 15 unique all unique 1 -0.21746329
#> 73 3 15 unique all unique 1 1.04560399
#> 74 4 15 unique all unique 1 1.79337756
#> 75 5 15 unique all unique 1 -0.10441185
#> 76 1 16 unique all unique 1 0.78948099
#> 77 2 16 unique all unique 1 -2.07292184
#> 78 3 16 unique all unique 1 2.21463109
#> 79 4 16 unique all unique 1 1.57844773
#> 80 5 16 unique all unique 1 1.22404338
#> 81 1 17 unique all unique 1 -0.04916270
#> 82 2 17 unique all unique 1 0.34662763
#> 83 3 17 unique all unique 1 2.19509878
#> 84 4 17 unique all unique 1 0.57898798
#> 85 5 17 unique all unique 1 -0.69747658
#> 86 1 18 unique all unique 1 0.44808265
#> 87 2 18 unique all unique 1 0.33083346
#> 88 3 18 unique all unique 1 -0.99741189
#> 89 4 18 unique all unique 1 -0.14125862
#> 90 5 18 unique all unique 1 -0.90833732
#> 91 1 19 unique all unique 1 0.55064754
#> 92 2 19 unique all unique 1 -0.80809562
#> 93 3 19 unique all unique 1 -1.83893557
#> 94 4 19 unique all unique 1 0.90553241
#> 95 5 19 unique all unique 1 -1.45875228
#> 96 1 20 unique all unique 1 -0.80164698
#> 97 2 20 unique all unique 1 -0.91078482
#> 98 3 20 unique all unique 1 -1.40536731
#> 99 4 20 unique all unique 1 2.19468463
#> 100 5 20 unique all unique 1 -1.13869430
# from FLPar to data.frame
as(FLPar(phi=rnorm(10), rho=rlnorm(10)), "data.frame")
#> params iter data
#> 1 phi 1 0.1822600
#> 2 rho 1 3.6417114
#> 3 phi 2 -0.3206102
#> 4 rho 2 1.0118828
#> 5 phi 3 0.4063791
#> 6 rho 3 1.9171083
#> 7 phi 4 -0.6228697
#> 8 rho 4 0.3915606
#> 9 phi 5 -0.7322529
#> 10 rho 5 0.7894674
#> 11 phi 6 0.2540571
#> 12 rho 6 0.1592711
#> 13 phi 7 0.1513072
#> 14 rho 7 1.2529434
#> 15 phi 8 -0.4464694
#> 16 rho 8 1.0286380
#> 17 phi 9 0.2632531
#> 18 rho 9 2.6425528
#> 19 phi 10 1.7090840
#> 20 rho 10 0.8634691