This PipeOp extracts features from functional data using B-spline basis functions.
The extracted features are B-spline coefficients that represent the functional data in the B-spline basis space.
For more details, see FDboost::bsignal(), which is called internally.
Parameters
The parameters are the parameters inherited from PipeOpTaskPreprocSimple,
as well as the following parameters:
inS::character(1)
Type of effect in the covariate index: one of"smooth","linear","constant". Default"smooth".knots::numeric()
Either the number of interior knots or a vector of their positions.boundary.knots::numeric(2)
Boundary points at which to anchor the B-spline basis. Lower and upper boundary points for the spline basis. Defaults to the range of the data.degree::integer(1)
The degree of the regression spline. Default is3L.differences::integer(1)
Order of difference penalty. Default is1L.df::numeric(1)
Trace of the hat matrix, controlling smoothness. Default is4.lambda::any
Smoothing parameter of the penalty term.center::logical(1)
Reparameterize the unpenalized part to zero-mean? Default isFALSE.cyclic::logical(1)
If true the fitted coefficient function coincides at the boundaries.Z::any
Custom transformation matrix for the spline design.penalty::character(1)
The penalty type:"ps"(P-spline) or"pss"(shrinkage). DEfault is"ps".check.ident::logical(1)
Use checks for identifiability of the effect. Default isFALSE.
Super classes
mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDABsignal
Methods
Method new()
Initializes a new instance of this Class.
Usage
PipeOpFDABsignal$new(id = "fda.bsignal", param_vals = list())Examples
task = tsk("fuel")
po_bsignal = po("fda.bsignal")
task_bsignal = po_bsignal$train(list(task))[[1L]]
task_bsignal$data()
#> heatan h20 NIR_bsig_1 NIR_bsig_2 NIR_bsig_3 NIR_bsig_4 NIR_bsig_5
#> <num> <num> <num> <num> <num> <num> <num>
#> 1: 26.7810 2.3000 0.30895622 3.8875914 8.25757958 9.8406292 10.8301760
#> 2: 27.4720 3.0000 0.23327867 2.6601669 5.63516646 7.0596187 7.9722599
#> 3: 23.8400 2.0002 0.00420622 0.1589861 0.46226576 0.6461524 0.6917787
#> 4: 18.1680 1.8500 -0.02755544 -0.1833916 -0.06140228 0.2132759 0.4183076
#> 5: 17.5170 2.3898 -0.10914767 -0.8979161 -0.87913829 0.1334393 0.9413606
#> ---
#> 125: 23.8340 2.1100 -0.01670794 0.1136370 0.74273390 1.3216435 1.8541903
#> 126: 11.8050 1.6200 -0.56152878 -5.9478765 -10.02857886 -9.1745939 -7.9582820
#> 127: 8.8315 1.4200 -0.71792538 -7.7929493 -13.40606760 -12.1496590 -10.6249247
#> 128: 11.3450 1.4800 -0.05080034 -0.4621451 -0.10831877 1.0637056 2.1666883
#> 129: 28.9940 2.5000 0.12943297 1.5660204 3.13869555 3.3369191 3.3178924
#> NIR_bsig_6 NIR_bsig_7 NIR_bsig_8 NIR_bsig_9 NIR_bsig_10 NIR_bsig_11
#> <num> <num> <num> <num> <num> <num>
#> 1: 11.5088941 11.9779453 12.1174704 11.8896232 11.31273620 10.3142375
#> 2: 8.7009409 9.3186404 9.7793087 10.1117548 10.30913017 10.4431357
#> 3: 0.6619125 0.5616062 0.3914156 0.1792577 -0.05370359 -0.2567235
#> 4: 0.5895871 0.6671312 0.6518124 0.5860669 0.46229636 0.3720952
#> 5: 1.5466583 2.0786524 2.4936812 2.7882146 2.94989453 3.0272675
#> ---
#> 125: 2.2003123 2.4912186 2.7998685 3.1179365 3.40765873 3.6379200
#> 126: -6.7631558 -5.5856955 -4.4352248 -3.3389506 -2.30337614 -1.2999487
#> 127: -9.0919723 -7.6142066 -6.1450498 -4.7293855 -3.39252094 -2.1272856
#> 128: 3.1888364 4.0518420 4.7226769 5.0945124 5.05592522 4.5462792
#> 129: 3.2478410 3.0527724 2.8059147 2.4792764 2.11115502 1.7602283
#> NIR_bsig_12 NIR_bsig_13 NIR_bsig_14 UVVIS_bsig_1 UVVIS_bsig_2
#> <num> <num> <num> <num> <num>
#> 1: 8.6066847 3.9933853 0.3114637655 0.4585002 4.890468
#> 2: 10.1912504 5.4302742 0.4637101954 -0.5708314 -6.053981
#> 3: -0.2807225 -0.0953887 -0.0004178588 -0.1310900 -1.802917
#> 4: 0.4691400 0.4674304 0.0680371347 -0.2848928 -3.032630
#> 5: 3.0416345 1.8975896 0.2049782592 -0.4715812 -5.115183
#> ---
#> 125: 3.5969034 1.9189905 0.1661176880 -0.3497554 -3.722387
#> 126: -0.2804047 0.5021154 0.1130349769 -0.5268493 -5.421564
#> 127: -0.8090823 0.4545185 0.1383324083 -0.4829044 -5.405378
#> 128: 3.4893505 1.6835206 0.1703526458 0.3369005 3.698122
#> 129: 1.5024626 0.7557343 0.0642844968 -0.4782980 -5.062383
#> UVVIS_bsig_3 UVVIS_bsig_4 UVVIS_bsig_5 UVVIS_bsig_6 UVVIS_bsig_7
#> <num> <num> <num> <num> <num>
#> 1: 9.167148 9.672084 9.520349 9.498991 9.937508
#> 2: -11.287193 -12.208947 -11.981817 -11.501893 -10.789400
#> 3: -3.872221 -4.627530 -4.102450 -3.643340 -3.417431
#> 4: -5.662221 -6.373050 -6.577991 -6.650986 -6.384284
#> 5: -9.748116 -10.629157 -10.448650 -10.270920 -9.875425
#> ---
#> 125: -6.699699 -7.113708 -7.247388 -7.355720 -7.292643
#> 126: -9.953552 -11.201759 -12.193262 -12.507825 -12.440546
#> 127: -10.712849 -12.383363 -13.638517 -14.476519 -14.740431
#> 128: 6.766475 6.744222 5.625725 4.769732 4.403105
#> 129: -8.954765 -9.530065 -9.225618 -8.259241 -7.506374
#> UVVIS_bsig_8 UVVIS_bsig_9 UVVIS_bsig_10 UVVIS_bsig_11 UVVIS_bsig_12
#> <num> <num> <num> <num> <num>
#> 1: 10.999917 12.339975 13.586039 14.647183 15.086214
#> 2: -9.819931 -8.794243 -7.763370 -6.892750 -5.887550
#> 3: -3.067908 -2.649421 -2.298166 -2.229685 -2.168885
#> 4: -5.963247 -5.467640 -4.858720 -4.333341 -3.707709
#> 5: -9.268580 -8.357216 -7.413833 -6.783725 -6.242212
#> ---
#> 125: -6.974161 -6.675450 -6.358119 -5.881807 -5.364075
#> 126: -12.210762 -11.820169 -11.289500 -10.798583 -9.726063
#> 127: -14.392768 -13.694971 -12.866094 -11.971076 -10.668189
#> 128: 4.745962 5.537319 6.473936 7.326784 7.793231
#> 129: -6.790662 -6.016780 -5.296986 -4.987682 -4.529120
#> UVVIS_bsig_13 UVVIS_bsig_14
#> <num> <num>
#> 1: 8.531411 0.7990913
#> 2: -3.206092 -0.3343597
#> 3: -1.300891 -0.1382287
#> 4: -2.155522 -0.2086386
#> 5: -3.087076 -0.2551800
#> ---
#> 125: -3.066321 -0.2856197
#> 126: -5.054930 -0.4535036
#> 127: -5.445452 -0.4886222
#> 128: 4.382803 0.4038778
#> 129: -2.582181 -0.2778649