Skip to contents

Constructors for vectors of "raw" functional data

Source: R/tfd-class.R
tfd.Rd

Various constructor methods for tfd-objects.

tfd.matrix accepts a numeric matrix with one function per row (!). If arg is not provided, it tries to guess arg from the column names and falls back on 1:ncol(data) if that fails.

tfd.data.frame uses the first 3 columns of data for function information by default: (id, arg, value)

tfd.list accepts a list of vectors of identical lengths containing evaluations or a list of 2-column matrices/data.frames with arg in the first and evaluations in the second column

tfd.default returns class prototype when argument to tfd() is NULL or not a recognised class

Usage

tfd(data, ...)

# S3 method for matrix
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)

# S3 method for numeric
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)

# S3 method for data.frame
tfd(
  data,
  id = 1,
  arg = 2,
  value = 3,
  domain = NULL,
  evaluator = tf_approx_linear,
  ...
)

# S3 method for list
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)

# S3 method for tf
tfd(data, arg = NULL, domain = NULL, evaluator = NULL, ...)

# S3 method for default
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)

as.tfd(data, ...)

as.tfd_irreg(data, ...)

Arguments

data

a matrix, data.frame or list of suitable shape, or another tf-object. when this argument is NULL (i.e. when calling tfd()) this returns a prototype of class tfd

...

not used in tfd, except for tfd.tf -- specify arg and ìnterpolate = TRUE to turn an irregular tfd into a regular one, see examples.

arg

numeric, or list of numerics. The evaluation grid. For the data.frame-method: the name/number of the column defining the evaluation grid. The matrix method will try to guess suitable arg-values from the column names of data if arg is not supplied. Other methods fall back on integer sequences (1:<length of data>) as the default if not provided.

domain

range of the arg.

evaluator

a function accepting arguments x, arg, evaluations. See details for tfd().

id

The name or number of the column defining which data belong to which function.

value

The name or number of the column containing the function evaluations.

Value

an tfd-object (or a data.frame/matrix for the conversion functions, obviously.)

Details

evaluator: must be the (quoted or bare) name of a function with signature function(x, arg, evaluations) that returns the functions' (approximated/interpolated) values at locations x based on the function evaluations available at locations arg.
Available evaluator-functions:

  • tf_approx_linear for linear interpolation without extrapolation (i.e., zoo::na.approx() with na.rm = FALSE) -- this is the default,

  • tf_approx_spline for cubic spline interpolation, (i.e., zoo::na.spline() with na.rm = FALSE),

  • tf_approx_fill_extend for linear interpolation and constant extrapolation (i.e., zoo::na.fill() with fill = "extend")

  • tf_approx_locf for "last observation carried forward" (i.e., zoo::na.locf() with na.rm = FALSE and

  • tf_approx_nocb for "next observation carried backward" (i.e., zoo::na.locf() with na.rm = FALSE, fromLast = TRUE). See tf:::zoo_wrapper and tf:::tf_approx_linear, which is simply zoo_wrapper(zoo::na.tf_approx, na.rm = FALSE), for examples of implementations of this.

Examples

# turn irregular to regular tfd by evaluating on a common grid:

f <- c(
  tf_rgp(1, arg = seq(0, 1, length.out = 11)),
  tf_rgp(1, arg = seq(0, 1, length.out = 21))
)
#> Warning: combining incompatible <tfd_reg> with <tfd_reg> by casting to <tfd_irreg>.
tfd(f, arg = seq(0, 1, length.out = 21))
#> New names:
#> • `1` -> `1...1`
#> • `1` -> `1...2`
#> tfd[2] on (0,1) based on 21 evaluations each
#> interpolation by tf_approx_linear 
#> 1...1: (0.00,-0.32);(0.05,-0.24);(0.10,-0.16); ...
#> 1...2: (0.00,-0.84);(0.05,-1.05);(0.10,-1.22); ...

set.seed(1213)
f <- tf_rgp(3, arg = seq(0, 1, length.out = 51)) |> tf_sparsify(0.9)
# does not yield regular data because linear extrapolation yields NAs
#   outside observed range:
tfd(f, arg = seq(0, 1, length.out = 101))
#> Warning: 86 evaluations were NA, returning irregular tfd.
#> irregular tfd[3] on (0,1) based on 59 to 87 (mean: 72) evaluations each
#> interpolation by tf_approx_linear 
#> 1: (0.20, 0.50);(0.21, 0.48);(0.22, 0.46); ...
#> 2: (0.12,-0.34);(0.13,-0.30);(0.14,-0.26); ...
#> 3: (0.04,-0.43);(0.05,-0.43);(0.06,-0.42); ...
# this "works" (but may not yield sensible values..!!) for
#   e.g. constant extrapolation:
tfd(f, evaluator = tf_approx_fill_extend, arg = seq(0, 1, length.out = 101))
#> tfd[3] on (0,1) based on 101 evaluations each
#> interpolation by tf_approx_fill_extend 
#> 1: (0.00,  0.5);(0.01,  0.5);(0.02,  0.5); ...
#> 2: (0.00,-0.34);(0.01,-0.34);(0.02,-0.34); ...
#> 3: (0.00,-0.43);(0.01,-0.43);(0.02,-0.43); ...
plot(f, col = 2)
tfd(f,
  arg = seq(0, 1, length.out = 151), evaluator = tf_approx_fill_extend
) |> lines()