Make a tf (more) irregular

Randomly create some irregular functional data from regular ones.

• jiggle it by randomly moving around its arg-values inside the intervals defined by its grid neighbors on the original argument grid.

• sparsify it by removing (100*dropout)% of the function values

Usage

tf_jiggle(f, amount = 0.4, ...)

# S3 method for class 'tf_mv'
tf_jiggle(f, amount = 0.4, same_arg = TRUE, ...)

tf_sparsify(f, dropout = 0.5, ...)

# S3 method for class 'tf_mv'
tf_sparsify(f, dropout = 0.5, same_arg = TRUE, ...)

Arguments

f

a tfd object.

amount

how far away from original grid points can the jiggled grid points lie, at most (relative to original distance to neighboring grid points). Defaults to at most 40% (0.4) of the original grid distances. Must be lower than 0.5.

...

additional args for the returned tfd in tf_jiggle.

same_arg

for tf_mv objects, should all components receive the same random argument-grid changes? Defaults to TRUE; use FALSE to jitter or sparsify each component independently.

dropout

what proportion of values of f to drop, on average. Defaults to half.

Value

an (irregular) tfd object.

See also

Other tidyfun RNG functions: tf_rgp()

Examples

set.seed(1)
(x <- tf_rgp(2, arg = 21L))
#> tfd[2]: [0,1] -> [-0.7703284,1.490023] based on 21 evaluations each
#> interpolation by tf_approx_linear 
#> 1: ▃▃▄▅▆▆▆▇▆▆▅▅▄▄▄▅▆▇███
#> 2: ▃▂▁▁▁▁▁▂▂▃▃▃▂▂▂▃▃▄▄▄▄
(x_jig <- tf_jiggle(x, amount = 0.2))
#> irregular tfd[2]: [0,1] -> [-0.7703284,1.490023] based on 21 to 21 (mean: 21) evaluations each
#> interpolation by tf_approx_linear 
#> 1: (0.019,-0.176);(0.040, 0.045);(0.106, 0.233); ...
#> 2: (0.023, -0.16);(0.053, -0.42);(0.093, -0.66); ...
(x_sp <- tf_sparsify(x, dropout = 0.3))
#> irregular tfd[2]: [0,1] -> [-0.7703284,1.490023] based on 13 to 18 (mean: 16) evaluations each
#> interpolation by tf_approx_linear 
#> 1: (0.00,-0.176);(0.05, 0.045);(0.15, 0.490); ...
#> 2: (0.05, -0.42);(0.10, -0.66);(0.15, -0.73); ...
c(is_irreg(x_jig), is_irreg(x_sp))
#> [1] TRUE TRUE