Extrapolate the quantiles to new quantile levels

This both interpolates between quantile levels already defined in x and extrapolates quantiles outside their bounds. The interpolation method is determined by the quantile argument middle, which can be either "cubic" for a (hyman) cubic spline interpolation, or "linear" for simple linear interpolation.

Usage

extrapolate_quantiles(x, probs, replace_na = TRUE, ...)

Arguments

x

A vector of class quantile_pred.

probs

a vector of probabilities at which to calculate quantiles

replace_na

logical. If x contains NA's, these are imputed if possible (if TRUE) or retained (if FALSE).

...

additional arguments passed on to the quantile method

Value

a quantile_pred vector. Each element of x will now have a superset of the original quantile_values (the union of those and probs).

Details

There is only one extrapolation method for values greater than the largest known quantile level or smaller than the smallest known quantile level. It assumes a roughly exponential tail, whose decay rate and offset is derived from the slope of the two most extreme quantile levels on a logistic scale. See the internal function tail_extrapolate() for the exact implementation.

This function takes a quantile_pred vector and returns the same type of object, expanded to include additional quantiles computed at probs. If you want behaviour more similar to stats::quantile(), then quantile(x,...) may be more appropriate.

Examples

dstn <- quantile_pred(rbind(1:4, 8:11), c(.2, .4, .6, .8))
# extra quantiles are appended
as_tibble(extrapolate_quantiles(dstn, probs = c(0.25, 0.5, 0.75)))
#> # A tibble: 14 × 3
#>    .pred_quantile .quantile_levels  .row
#>             <dbl>            <dbl> <int>
#>  1           1                0.2      1
#>  2           1.25             0.25     1
#>  3           2                0.4      1
#>  4           2.5              0.5      1
#>  5           3                0.6      1
#>  6           3.75             0.75     1
#>  7           4                0.8      1
#>  8           8                0.2      2
#>  9           8.25             0.25     2
#> 10           9                0.4      2
#> 11           9.5              0.5      2
#> 12          10                0.6      2
#> 13          10.8              0.75     2
#> 14          11                0.8      2

extrapolate_quantiles(dstn, probs = c(0.0001, 0.25, 0.5, 0.75, 0.99999))
#> <quantiles[2]>
#> [1] [2.5] [9.5]
#> # Quantile levels: 0.0001 0.2000 0.2500 0.4000 0.5000 0.6000 0.7500 0.8000 1.0000