rsq.numeric {SLmetrics} | R Documentation |
r^2
Description
A generic S3 function to compute the r^2
score for a regression model. This function dispatches to S3 methods in rsq()
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 rsq()
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 rsq()
in a "safe" validator that checks for NA values and matching length, for example:
safe_rsq <- function(x, y, ...) { stopifnot( !anyNA(x), !anyNA(y), length(x) == length(y) ) rsq(x, y, ...) }
Apply the same pattern to any custom metric functions to ensure input sanity before calling the underlying C++
code.
Usage
## S3 method for class 'numeric'
rsq(actual, predicted, k = 0, ...)
Arguments
actual , predicted |
|
k |
A <double>-vector of length 1 (default: 0). For adjusted |
... |
Arguments passed into other methods |
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.
Pedregosa, Fabian, et al. "Scikit-learn: Machine learning in Python." the Journal of machine Learning research 12 (2011): 2825-2830.
See Also
Other Regression:
ccc()
,
deviance.gamma()
,
deviance.poisson()
,
deviance.tweedie()
,
gmse()
,
huberloss()
,
maape()
,
mae()
,
mape()
,
mpe()
,
mse()
,
pinball()
,
rae()
,
rmse()
,
rmsle()
,
rrmse()
,
rrse()
,
smape()
Other Supervised Learning:
accuracy()
,
auc.pr.curve()
,
auc.roc.curve()
,
baccuracy()
,
brier.score()
,
ccc()
,
ckappa()
,
cmatrix()
,
cross.entropy()
,
deviance.gamma()
,
deviance.poisson()
,
deviance.tweedie()
,
dor()
,
fbeta()
,
fdr()
,
fer()
,
fmi()
,
fpr()
,
gmse()
,
hammingloss()
,
huberloss()
,
jaccard()
,
logloss()
,
maape()
,
mae()
,
mape()
,
mcc()
,
mpe()
,
mse()
,
nlr()
,
npv()
,
pinball()
,
plr()
,
pr.curve()
,
precision()
,
rae()
,
recall()
,
relative.entropy()
,
rmse()
,
rmsle()
,
roc.curve()
,
rrmse()
,
rrse()
,
shannon.entropy()
,
smape()
,
specificity()
,
zerooneloss()
Examples
## Generate actual
## and predicted values
actual_values <- c(1.3, 0.4, 1.2, 1.4, 1.9, 1.0, 1.2)
predicted_values <- c(0.7, 0.5, 1.1, 1.2, 1.8, 1.1, 0.2)
## Evaluate performance
SLmetrics::rsq(
actual = actual_values,
predicted = predicted_values
)