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Running Quantile Calculation

Source: R/vec-running-quantile.R
running_quantile.Rd

The running_quantile function calculates the quantile of a vector over a sliding window, allowing for various alignment and rule options.

Usage

running_quantile(
  .x,
  .window,
  .probs = 0.5,
  .type = 7,
  .rule = "quantile",
  .align = "center"
)

Arguments

.x

A numeric vector for which the running quantile is to be calculated.

.window

An integer specifying the size of the sliding window.

.probs

A numeric value between 0 and 1 indicating the desired quantile probability (default is 0.50).

.type

An integer from 1 to 9 specifying the quantile algorithm type (default is 7).

.rule

A character string indicating the rule to apply at the edges of the window. Possible choices are:

  • "quantile": Standard quantile calculation.

  • "trim": Trims the output to remove values outside the window.

  • "keep": Keeps the original values at the edges of the window.

  • "constant": Fills the edges with the constant value from the nearest valid quantile.

  • "NA": Fills the edges with NA values.

  • "func": Applies a custom function to the values in the window (default is "quantile").

.align

A character string specifying the alignment of the window ("center", "left", or "right"; default is "center").

Value

A numeric vector containing the running quantile values.

Details

This function computes the running quantile of a numeric vector using a specified window size and probability.

See also

Other Utility Functions: convert_snake_to_title_case(), generate_caption(), get_attributes(), rand_walk_column_names(), rand_walk_helper(), std_cum_max_augment(), std_cum_mean_augment(), std_cum_min_augment(), std_cum_prod_augment(), std_cum_sum_augment()

Author

Steven P. Sanderson II, MPH

Examples

# Example usage of running_quantile
set.seed(123)
data <- cumsum(rnorm(50))
result <- running_quantile(data, .window = 3, .probs = 0.5)
print(result)
#>  [1] -0.6755644 -0.5604756  0.7680552  0.8385636  0.9678513  2.6829163
#>  [7]  2.6829163  1.8787713  1.1919184  1.1919184  1.9703382  2.3301521
#> [13]  2.7309235  2.7309235  2.8416062  4.0726782  4.0726782  3.3052675
#> [19]  2.8324760  2.8324760  1.7646523  1.5466774  0.5206730 -0.2082182
#> [25] -0.8332575 -1.6821638 -1.6821638 -1.6821638 -1.5287907 -1.4131127
#> [31] -1.2817199 -0.9866485 -0.3865943  0.4915392  1.3131203  2.0017605
#> [37]  2.4937665  2.4937665  2.1878038  1.8073328  1.1126258  0.9047086
#> [43]  0.9047086  1.8082682  1.8931216  1.8931216  1.4902368  1.4902368
#> [49]  1.7201775  1.7618620
#> attr(,"window")
#> [1] 3
#> attr(,"probs")
#> [1] 0.5
#> attr(,"type")
#> [1] 7
#> attr(,"rule")
#> [1] "quantile"
#> attr(,"align")
#> [1] "center"

plot(data, type = "l")
lines(result, col = "red")