Utility functions for tf-objects

A bunch of methods & utilities that do what they say: get or set the respective attributes of a tf-object.

Usage

tf_arg(f)

tf_evaluations(f)

tf_count(f)

tf_domain(f)

tf_domain(x) <- value

tf_evaluator(f)

tf_evaluator(x) <- value

tf_basis(f, as_tfd = FALSE)

tf_arg(x) <- value

# S3 method for class 'tfd_irreg'
tf_arg(x) <- value

# S3 method for class 'tfd_reg'
tf_arg(x) <- value

# S3 method for class 'tfb'
tf_arg(x) <- value

# S3 method for class 'tfb'
coef(object, ...)

# S3 method for class 'tf'
rev(x)

# S3 method for class 'tf'
is.na(x)

# S3 method for class 'tfd_irreg'
is.na(x)

is_tf(x)

is_tfd(x)

is_reg(x)

is_tfd_reg(x)

is_irreg(x)

is_tfd_irreg(x)

is_tfb(x)

is_tfb_spline(x)

is_tfb_fpc(x)

Arguments

f: an tf object.
x: an tf object.
value: for tf_evaluator<-: (bare or quoted) name of a function that can be used to interpolate an tfd. Needs to accept vector arguments x, arg, evaluations and return evaluations of the function defined by arg, evaluations at x.
for tf_arg<-: (list of) new arg-values.
for tf_domain<-: sorted numeric vector with the 2 new endpoints of the domain.
as_tfd: should the basis be returned as a tfd-vector evaluated on tf_arg(f)? Defaults to FALSE, which returns the matrix of basis functions (columns) evaluated on tf_arg(f) (rows).
object: as usual
...: dots

Value

either the respective attribute or, for setters (assignment functions), the input object with modified properties.

Examples

x <- tf_rgp(3)
tf_arg(x)
#>  [1] 0.00 0.02 0.04 0.06 0.08 0.10 0.12 0.14 0.16 0.18 0.20 0.22 0.24 0.26 0.28
#> [16] 0.30 0.32 0.34 0.36 0.38 0.40 0.42 0.44 0.46 0.48 0.50 0.52 0.54 0.56 0.58
#> [31] 0.60 0.62 0.64 0.66 0.68 0.70 0.72 0.74 0.76 0.78 0.80 0.82 0.84 0.86 0.88
#> [46] 0.90 0.92 0.94 0.96 0.98 1.00
tf_evaluations(x)
#> $`1`
#>  [1] 1.5135408 1.5465073 1.5369858 1.5697465 1.5714709 1.6353928 1.5856153
#>  [8] 1.6094901 1.6662783 1.6314549 1.6336818 1.6367116 1.6020364 1.6594355
#> [15] 1.6166639 1.5656236 1.5335329 1.4620264 1.4026727 1.3300888 1.2009638
#> [22] 1.0988050 1.0189181 0.8900833 0.8087505 0.7486157 0.6528027 0.6329539
#> [29] 0.5611478 0.5332983 0.5330818 0.5270663 0.5633167 0.6239479 0.6427727
#> [36] 0.6866764 0.6817430 0.7708453 0.8179473 0.8246502 0.9028411 0.9460575
#> [43] 0.9629997 1.0684018 1.0659101 1.1363187 1.1895048 1.1904337 1.2702224
#> [50] 1.3571844 1.4087177
#> 
#> $`2`
#>  [1] 0.2460432 0.2323935 0.1399692 0.1154920 0.1175801 0.1329389 0.1320968
#>  [8] 0.1587012 0.1729748 0.2901721 0.3747257 0.5225041 0.6283878 0.7327980
#> [15] 0.9377491 1.2112754 1.3482925 1.5220939 1.6580685 1.8164518 2.0045371
#> [22] 2.0933358 2.1465323 2.2523857 2.2900022 2.3150377 2.3020997 2.2706018
#> [29] 2.2904270 2.2112746 2.1441833 2.1267001 2.1022045 2.0561951 2.0289924
#> [36] 1.9719107 1.9896746 1.9883448 2.0120667 1.9932549 2.0345371 2.0324733
#> [43] 2.0865893 2.1161068 2.1931689 2.2061105 2.1754907 2.2082579 2.2096478
#> [50] 2.1557483 2.1170930
#> 
#> $`3`
#>  [1] -0.75374233 -0.77775792 -0.76041419 -0.81515851 -0.79056274 -0.77815907
#>  [7] -0.76566509 -0.72777650 -0.74994751 -0.73561565 -0.67613458 -0.65074704
#> [13] -0.59298930 -0.61799721 -0.55300108 -0.50651394 -0.45639877 -0.38024743
#> [19] -0.30507751 -0.27440137 -0.19265989 -0.11675818 -0.02096315  0.03127692
#> [25]  0.11841039  0.23665467  0.29647397  0.36968536  0.43486651  0.52443404
#> [31]  0.57151509  0.66628899  0.68893369  0.77520118  0.80359573  0.86428402
#> [37]  0.86900635  0.91164892  0.87635657  0.83792940  0.78790633  0.70212418
#> [43]  0.62222874  0.49693137  0.40665924  0.25253816  0.07406338 -0.07633862
#> [49] -0.17161658 -0.34688935 -0.48290160
#> 
tf_count(x)
#> [1] 51
tf_domain(x)
#> [1] 0 1
tf_evaluator(x)
#> function (x, arg, evaluations) 
#> {
#>     x_arg <- sort_unique(c(x, arg))
#>     x_arg_match <- match(x_arg, arg, nomatch = length(arg) + 
#>         1)
#>     requested <- x_arg %in% x
#>     dots[[length(dots) + 1]] <- zoo(evaluations[x_arg_match], 
#>         x_arg)
#>     ret <- do.call(f, dots)
#>     coredata(ret)[requested]
#> }
#> <bytecode: 0x55ab758716b8>
#> <environment: 0x55ab7586e998>
tf_evaluate(x, 0.25)
#> $`1`
#> [1] 1.630736
#> 
#> $`2`
#> [1] 0.6805929
#> 
#> $`3`
#> [1] -0.6054933
#> 
tf_evaluator(x) <- tf_approx_none
tf_evaluate(x, 0.25)
#> $`1`
#> [1] NA
#> 
#> $`2`
#> [1] NA
#> 
#> $`3`
#> [1] NA
#> 
c(is_tf(x), is_tfd(x), is_reg(x), is_irreg(x))
#> [1]  TRUE  TRUE  TRUE FALSE

xb <- tfb(x, k = 4, penalized = FALSE, verbose = FALSE)
tf_basis(xb)
#> function (arg) 
#> {
#>     Predict.matrix(object = spec, data = data_frame0(arg = arg))
#> }
#> <bytecode: 0x55ab77896448>
#> <environment: 0x55ab77645c40>
tf_basis(xb)(c(0, .1, .2))
#>        [,1]   [,2]    [,3]   [,4]
#> [1,] 1.0000 0.0000  0.0000 0.0000
#> [2,] 0.6272 0.4638 -0.1092 0.0182
#> [3,] 0.2976 0.8304 -0.1536 0.0256
c(is_tfb(xb), is_tfb_spline(xb), is_tfb_fpc(xb))
#> [1]  TRUE  TRUE FALSE

Utility functions for `tf`-objects

Usage

Arguments

Value

See also

Examples