form_smooth: Smooth pdqr-function

In pdqr: Work with Custom Distribution Functions

Smooth pdqr-function

Description

Smooth pdqr-function using random sampling and corresponding new_*() function.

Usage

form_smooth(f, n_sample = 10000, args_new = list())

Arguments

f	A pdqr-function.
n_sample	Number of elements to sample.
args_new	List of extra arguments for new_*() to control density().

Details

General idea of smoothing is to preserve "sampling randomness" as much as reasonably possible while creating more "smooth" probability mass or density function.

At first step, sample of size n_sample is generated from distribution represented by f. Then, based on the sample, "continuous" d-function is created with new_d() and arguments from args_new list. To account for density()'s default behavior of "stretching range" by adding small tails, support of d-function is forced to be equal to f's support (this is done with form_resupport() and method "reflect"). Output represents a "smooth" version of f as d-function.

Final output is computed by modifying "y" or "prob" column of f's "x_tbl" metadata to be proportional to values of "smooth" output at corresponding points from "x" column. This way output distribution has exactly the same "x" grid as f but "more smooth" nature.

Value

A smoothed version of f with the same class and type.

See Also

Other form functions: form_estimate(), form_mix(), form_regrid(), form_resupport(), form_retype(), form_tails(), form_trans()

Examples

set.seed(101)

# Type "discrete"
bad_dis <- new_d(
  data.frame(x = sort(runif(100)), prob = runif(100)),
  type = "discrete"
)
smoothed_dis <- form_smooth(bad_dis)
plot(bad_dis)
lines(smoothed_dis, col = "blue")

# Type "continuous"
bad_con <- new_d(
  data.frame(x = sort(runif(100)), y = runif(100)),
  type = "continuous"
)
smoothed_con <- form_smooth(bad_con)
plot(bad_con)
lines(smoothed_con, col = "blue")

pdqr documentation built on May 31, 2023, 8:48 p.m.