quantile_pred()
is a special vector class used to efficiently store
predictions from a quantile regression model. It requires the same quantile
levels for each row being predicted.
Arguments
- values
A matrix of values. Each column should correspond to one of the quantile levels.
- quantile_levels
A vector of probabilities corresponding to
values
.- x
An object produced by
quantile_pred()
.- ...
Not currently used.
- .rows, .name_repair, rownames
Arguments not used but required by the original S3 method.
Value
quantile_pred()
returns a vector of values associated with the quantile levels.extract_quantile_levels()
returns a numeric vector of levels.as_tibble()
returns a tibble with rows".pred_quantile"
,".quantile_levels"
, and".row"
.as.matrix()
returns an unnamed matrix with rows as samples, columns as quantile levels, and entries are predictions.
Examples
.pred_quantile <- quantile_pred(matrix(rnorm(20), 5), c(.2, .4, .6, .8))
unclass(.pred_quantile)
#> [[1]]
#> [1] -1.448205 1.102298 -1.167619 1.449496
#>
#> [[2]]
#> [1] 0.5747557 -0.4755931 -2.1800396 -1.0686427
#>
#> [[3]]
#> [1] -1.0236557 -0.7094400 -1.3409932 -0.8553646
#>
#> [[4]]
#> [1] -0.0151383 -0.5012581 -0.2942939 -0.2806230
#>
#> [[5]]
#> [1] -0.9359486 -1.6290935 -0.4658975 -0.9943401
#>
#> attr(,"quantile_levels")
#> [1] 0.2 0.4 0.6 0.8
# Access the underlying information
extract_quantile_levels(.pred_quantile)
#> [1] 0.2 0.4 0.6 0.8
# Matrix format
as.matrix(.pred_quantile)
#> [,1] [,2] [,3] [,4]
#> [1,] -1.4482049 1.1022975 -1.1676193 1.4494963
#> [2,] 0.5747557 -0.4755931 -2.1800396 -1.0686427
#> [3,] -1.0236557 -0.7094400 -1.3409932 -0.8553646
#> [4,] -0.0151383 -0.5012581 -0.2942939 -0.2806230
#> [5,] -0.9359486 -1.6290935 -0.4658975 -0.9943401
# Tidy format
library(tibble)
as_tibble(.pred_quantile)
#> # A tibble: 20 × 3
#> .pred_quantile .quantile_levels .row
#> <dbl> <dbl> <int>
#> 1 -1.45 0.2 1
#> 2 1.10 0.4 1
#> 3 -1.17 0.6 1
#> 4 1.45 0.8 1
#> 5 0.575 0.2 2
#> 6 -0.476 0.4 2
#> 7 -2.18 0.6 2
#> 8 -1.07 0.8 2
#> 9 -1.02 0.2 3
#> 10 -0.709 0.4 3
#> 11 -1.34 0.6 3
#> 12 -0.855 0.8 3
#> 13 -0.0151 0.2 4
#> 14 -0.501 0.4 4
#> 15 -0.294 0.6 4
#> 16 -0.281 0.8 4
#> 17 -0.936 0.2 5
#> 18 -1.63 0.4 5
#> 19 -0.466 0.6 5
#> 20 -0.994 0.8 5