Standardize a numeric vector using quantiles — standardize

A convenience function to standardize a numeric vector using quantiles. Defaults to centering on the median and scaling on the interquartile range. $$\frac{x - \text{median}(x)}{\text{quantile}(x, 0.75) - \text{quantile}(x, 0.25)}$$

Usage

standardize_quantile(
  x,
  center = function(x) median(x, na.rm = TRUE),
  scale_lower = function(x) quantile(x, 0.25, na.rm = TRUE),
  scale_upper = function(x) quantile(x, 0.75, na.rm = TRUE)
)

Arguments

x: A numeric vector.
center: A function or numeric constant.
scale_lower: A function or numeric constant.
scale_upper: A function or numeric constant.

Value

numeric vector

Examples

#----------------------------------------------------------------------------
# standardize_quantile() examples
#----------------------------------------------------------------------------
library(bkstat)

x <- rnorm(100, mean = 30, sd = 20)
standardize_quantile(x)
#>   [1]  0.448618491  0.183573984 -1.063691965 -0.787956024 -1.036671326
#>   [6]  1.289943651 -0.277940015 -0.068024957 -0.046939546 -0.074627631
#>  [11] -0.577207364 -0.001830774  0.461018663  0.095744245  1.421679876
#>  [16]  1.026151717  0.121370161 -0.525375903 -0.228910460 -1.005580002
#>  [21]  0.898624813  0.298835423 -0.620936369  0.324742714 -0.247377308
#>  [26]  0.202559002 -0.622114215  0.457296849  0.935418232  0.095556808
#>  [31] -1.182260120 -1.470524406  1.048335453 -0.129200842 -0.508372110
#>  [36]  1.082827079 -0.197549790  0.343986634  0.534295457  0.439385495
#>  [41] -0.106130362 -0.389691284 -1.781363910 -0.421707969  0.385602417
#>  [46] -0.834371664  1.166872018  0.239357527  0.741235165 -0.453188913
#>  [51] -0.658043394 -0.495129364  0.209223063 -0.633476535 -1.975903929
#>  [56]  0.223160024 -0.684340530  0.127080676 -0.105055291  1.002754431
#>  [61]  0.787171574  1.297980676 -0.104629866  0.494178171 -0.893224310
#>  [66]  0.083440023 -0.389633254 -0.396996062  0.715204715 -1.459641675
#>  [71]  0.161841728  1.729517646  0.304225940 -0.269006030 -1.082063656
#>  [76] -0.848195544 -0.491714752  0.548976283  1.095140335  0.198405735
#>  [81]  0.944969916  1.022796414  0.580484696  0.899611994  1.285224245
#>  [86] -1.613659120  0.001830774 -1.880233754  0.153354318 -0.546638131
#>  [91] -0.098289347 -0.943403930 -0.503410465  0.048940604  1.143103934
#>  [96]  0.346822691 -1.815910265 -0.297430910 -1.282304865  1.092199281