Interpolate Functional Columns

Interpolate functional features (e.g. all individuals are observed at different time-points) to a common grid. This is useful if you want to compare functional features across observations. The interpolation is done using the tf package. See tfd() for details.

Parameters

The parameters are the parameters inherited from PipeOpTaskPreprocSimple, as well as the following parameters:

• grid :: character(1) | numeric()
  The grid to use for interpolation. If grid is numeric, it must be a sequence of values to use for the grid or a single value that specifies the number of points to use for the grid, requires left and right to be specified in the latter case. If grid is a character, it must be one of:

  • "union": This option creates a grid based on the union of all argument points from the provided functional features. This means that if the argument points across features are \(t_1, t_2, ..., t_n\), then the grid will be the combined unique set of these points. This option is generally used when the argument points vary across observations and a common grid is needed for comparison or further analysis.

  • "intersect": Creates a grid using the intersection of all argument points of a feature. This grid includes only those points that are common across all functional features, facilitating direct comparison on a shared set of points.

  • "minmax": Generates a grid within the range of the maximum of the minimum argument points to the minimum of the maximum argument points across features. This bounded grid encapsulates the argument point range common to all features. Note: For regular functional data this has no effect as all argument points are the same. Initial value is "union".

• method :: character(1)
  Defaults to "linear". One of:

  • "linear": applies linear interpolation without extrapolation (see tf::tf_approx_linear()).

  • "spline": applies cubic spline interpolation (see tf::tf_approx_spline()).

  • "fill_extend": applies linear interpolation with constant extrapolation (see tf::tf_approx_fill_extend()).

  • "locf": applies "last observation carried forward" interpolation (see tf::tf_approx_locf()).

  • "nocb": applies "next observation carried backward" interpolation (see tf::tf_approx_nocb()).

• left :: numeric()
  The left boundary of the window. The window is specified such that the all values >=left and <=right are kept for the computations.

• right :: numeric()
  The right boundary of the window.

Super classes

mlr3pipelines::PipeOp -> mlr3pipelines::PipeOpTaskPreproc -> mlr3pipelines::PipeOpTaskPreprocSimple -> PipeOpFDAInterpol

Methods

Public methods

• PipeOpFDAInterpol$new()

• PipeOpFDAInterpol$clone()

Inherited methods

• mlr3pipelines::PipeOp$help()
• mlr3pipelines::PipeOp$predict()
• mlr3pipelines::PipeOp$print()
• mlr3pipelines::PipeOp$train()

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Method new()

Initializes a new instance of this Class.

Usage

PipeOpFDAInterpol$new(id = "fda.interpol", param_vals = list())

Arguments

id

(character(1))
Identifier of resulting object, default "fda.interpol".

param_vals

(named list)
List of hyperparameter settings, overwriting the hyperparameter settings that would otherwise be set during construction. Default list().

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Method clone()

The objects of this class are cloneable with this method.

Usage

PipeOpFDAInterpol$clone(deep = FALSE)

Arguments

deep

Whether to make a deep clone.

Examples

library(mlr3pipelines)

task = tsk("fuel")
pop = po("fda.interpol")
task_interpol = pop$train(list(task))[[1L]]
task_interpol$data()
#>       heatan    h20                                         NIR
#>        <num>  <num>                                   <tfd_reg>
#>   1: 26.7810 2.3000  [1]: (1,   0.2);(2,   0.3);(3,   0.3); ...
#>   2: 27.4720 3.0000  [2]: (1,   0.2);(2,   0.3);(3,   0.2); ...
#>   3: 23.8400 2.0002  [3]: (1, -0.05);(2,  0.05);(3, -0.08); ...
#>   4: 18.1680 1.8500  [4]: (1, -0.08);(2, -0.08);(3,  0.06); ...
#>   5: 17.5170 2.3898  [5]: (1, -0.23);(2, -0.12);(3, -0.04); ...
#>  ---                                                           
#> 125: 23.8340 2.1100  [6]: (1, -0.04);(2, -0.02);(3, -0.06); ...
#> 126: 11.8050 1.6200  [7]: (1,  -0.6);(2,  -0.6);(3,  -0.7); ...
#> 127:  8.8315 1.4200  [8]: (1,  -0.7);(2,  -0.8);(3,  -0.8); ...
#> 128: 11.3450 1.4800  [9]: (1,-0.058);(2,-0.046);(3,-0.005); ...
#> 129: 28.9940 2.5000 [10]: (1,   0.1);(2,   0.1);(3,   0.2); ...
#>                                         UVVIS
#>                                     <tfd_reg>
#>   1:  [1]: (1,  0.9);(2,  0.7);(3,  0.8); ...
#>   2:  [2]: (1, -0.9);(2, -1.3);(3, -0.8); ...
#>   3:  [3]: (1,-0.08);(2,-0.29);(3,-0.20); ...
#>   4:  [4]: (1, -0.6);(2, -0.5);(3, -0.3); ...
#>   5:  [5]: (1, -0.6);(2, -1.1);(3, -0.7); ...
#>  ---                                         
#> 125:  [6]: (1, -0.5);(2, -0.7);(3, -0.6); ...
#> 126:  [7]: (1, -1.0);(2, -0.8);(3, -1.1); ...
#> 127:  [8]: (1, -0.9);(2, -0.6);(3, -1.0); ...
#> 128:  [9]: (1,  0.5);(2,  0.6);(3,  0.6); ...
#> 129: [10]: (1, -0.5);(2, -1.3);(3, -0.8); ...