Access a distribution's quantiles.
Usage
eval_quantile(distribution, at)
enframe_quantile(..., at, arg_name = ".arg", fn_prefix = "quantile", sep = "_")Arguments
- distribution, ...
A distribution, or possibly multiple distributions in the case of
....- at
Vector of values to evaluate the representation at.
- arg_name
For
enframe_, name of the column containing the function arguments. Length 1 character vector.- fn_prefix
For
enframe_, name of the function to appear in the column(s). Length 1 character vector.- sep
When
enframe'ing more than one distribution, the character that will be separating thefn_nameand the distribution name. Length 1 character vector.
Value
The evaluated representation in vector form (for eval_)
with length matching the length of at, and data frame
or tibble form (for enframe_) with number of rows matching the
length of at. The at input occupies the first column,
named .arg by default, or the specification in arg_name;
the evaluated representations for each distribution in ...
go in the subsequent columns (one column per distribution). For a
single distribution, this column is named according to the
representation by default (cdf, survival, quantile, etc.),
or the value in fn_prefix. For multiple distributions, unnamed
distributions are auto-named, and columns are named
<fn_prefix><sep><distribution_name> (e.g., cdf_distribution1).
Details
When a quantile function does not exist, an algorithm is deployed that calculates the left inverse of the CDF. This algorithm works by progressively cutting the specified range in half, moving into the left or right half depending on where the solution is. The algorithm is not currently fast and is subject to improvement, and is a simple idea that has been passed around on the internet here and there. Tolerance is less than 1e-9, unless the maximum number of iterations (200) is reached.
See also
Other distributional representations:
eval_cdf(),
eval_chf(),
eval_density(),
eval_hazard(),
eval_odds(),
eval_pmf(),
eval_return(),
eval_survival()
Examples
d <- dst_unif(0, 4)
eval_quantile(d, at = 1:9 / 10)
#> [1] 0.4 0.8 1.2 1.6 2.0 2.4 2.8 3.2 3.6
enframe_quantile(d, at = 1:9 / 10)
#> # A tibble: 9 × 2
#> .arg quantile
#> <dbl> <dbl>
#> 1 0.1 0.4
#> 2 0.2 0.8
#> 3 0.3 1.2
#> 4 0.4 1.6
#> 5 0.5 2
#> 6 0.6 2.4
#> 7 0.7 2.8
#> 8 0.8 3.2
#> 9 0.9 3.6