rrmse.md
rrmse
R Documentation
Relative Root Mean Squared Error
Description
A generic S3 function to compute the relative root mean squared error
score for a regression model. This function dispatches to S3 methods inrrmse() 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 rrmse() 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 rrmse() in a "safe" validator that checks
for NA values and matching length, for example:
safe_rrmse <- function(x, y, ...) {
stopifnot(
!anyNA(x), !anyNA(y),
length(x) == length(y)
)
rrmse(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 Relative Root Mean Squared Error
rrmse(...)
## Generic S3 method
## for weighted Concordance Correlation Coefficient
weighted.rrmse(...)Arguments
...
Arguments passed on to rrmse.numeric,weighted.rrmse.numeric
actual,predicted
A pair of <double> vectors of length n.
normalization
A <double>-value of length 1
(default: 1). 0:
mean-normalization, 1: range-normalization,2: IQR-normalization.
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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