pinball.md
pinball
R Documentation
Pinball Loss
Description
A generic S3 function to compute the pinball loss score for a
regression model. This function dispatches to S3 methods in pinball()
and performs no input validation. If you supply NA values or vectors of
unequal length (e.g. length(x) != length(y)), the underlying C++
code may trigger undefined behavior and crash your R session.
Defensive measures
Because pinball() operates on raw pointers, pointer-level faults (e.g.
from NA or mismatched length) occur before any R-level error handling.
Wrapping calls in try() or tryCatch() will not prevent R-session
crashes.
To guard against this, wrap pinball() in a "safe" validator that
checks for NA values and matching length, for example:
safe_pinball <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
pinball(x, y, ...)
}Apply the same pattern to any custom metric functions to ensure input
sanity before calling the underlying C++ code.
Usage
## Generic S3 method
## for Pinball Loss
pinball(...)
## Generic S3 method
## for weighted Pinball Loss
weighted.pinball(...)Arguments
...
Arguments passed on to pinball.numeric,weighted.pinball.numeric
actual,predicted
A pair of <double> vectors of length n.
alpha
A <double>-value of length 1
(default: 0.5). The slope of the pinball loss
function.
deviance
A <logical>-value of length 1 (default: FALSE). If TRUE the
function returns the D^2 loss.
w
A <double> vector of sample weights.
Value
A <double> value
References
James, Gareth, et al. An introduction to statistical learning. Vol. 112. No. 1. New York: springer, 2013.
Hastie, Trevor. "The elements of statistical learning: data mining, inference, and prediction." (2009).
Virtanen, Pauli, et al. "SciPy 1.0: fundamental algorithms for scientific computing in Python." Nature methods 17.3 (2020): 261-272.
Examples
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