tfd {tf} | R Documentation |
Constructors for vectors of "raw" functional data
Description
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 class 'matrix'
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)
## S3 method for class 'numeric'
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)
## S3 method for class 'data.frame'
tfd(
data,
id = 1,
arg = 2,
value = 3,
domain = NULL,
evaluator = tf_approx_linear,
...
)
## S3 method for class 'list'
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)
## S3 method for class 'tf'
tfd(data, arg = NULL, domain = NULL, evaluator = NULL, ...)
## Default S3 method:
tfd(data, arg = NULL, domain = NULL, evaluator = tf_approx_linear, ...)
as.tfd(data, ...)
as.tfd_irreg(data, ...)
Arguments
data |
a |
... |
not used in |
arg |
|
domain |
range of the |
evaluator |
a function accepting arguments |
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. |
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()
withna.rm = FALSE
) – this is the default, -
tf_approx_spline
for cubic spline interpolation, (i.e.,zoo::na.spline()
withna.rm = FALSE
), -
tf_approx_fill_extend
for linear interpolation and constant extrapolation (i.e.,zoo::na.fill()
withfill = "extend"
) -
tf_approx_locf
for "last observation carried forward" (i.e.,zoo::na.locf()
withna.rm = FALSE
and -
tf_approx_nocb
for "next observation carried backward" (i.e.,zoo::na.locf()
withna.rm = FALSE, fromLast = TRUE
). Seetf:::zoo_wrapper
andtf:::tf_approx_linear
, which is simplyzoo_wrapper(zoo::na.tf_approx, na.rm = FALSE)
, for examples of implementations of this.
Value
an tfd
-object (or a data.frame
/matrix
for the conversion
functions, obviously.)
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))
)
tfd(f, arg = seq(0, 1, length.out = 21))
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))
# 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))
plot(f, col = 2)
tfd(f,
arg = seq(0, 1, length.out = 151), evaluator = tf_approx_fill_extend
) |> lines()