This function calculates the predictive accuracy of linear or logistic regression models.
Usage
performance_accuracy(
model,
method = c("cv", "boot"),
k = 5,
n = 1000,
ci = 0.95,
verbose = TRUE
)Arguments
- model
A linear or logistic regression model. A mixed-effects model is also accepted.
- method
Character string, indicating whether cross-validation (
method = "cv") or bootstrapping (method = "boot") is used to compute the accuracy values.- k
The number of folds for the k-fold cross-validation.
- n
Number of bootstrap-samples.
- ci
The level of the confidence interval.
- verbose
Toggle warnings.
Value
A list with three values: The Accuracy of the model
predictions, i.e. the proportion of accurately predicted values from the
model, its standard error, SE, and the Method used to compute
the accuracy.
Details
For linear models, the accuracy is the correlation coefficient
between the actual and the predicted value of the outcome. For
logistic regression models, the accuracy corresponds to the
AUC-value, calculated with the bayestestR::auc()-function.
The accuracy is the mean value of multiple correlation resp.
AUC-values, which are either computed with cross-validation
or non-parametric bootstrapping (see argument method).
The standard error is the standard deviation of the computed
correlation resp. AUC-values.
Examples
model <- lm(mpg ~ wt + cyl, data = mtcars)
performance_accuracy(model)
#> # Accuracy of Model Predictions
#>
#> Accuracy (95% CI): 95.79% [92.14%, 99.11%]
#> Method: Correlation between observed and predicted
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
performance_accuracy(model)
#> # Accuracy of Model Predictions
#>
#> Accuracy (95% CI): 87.56% [78.00%, 100.00%]
#> Method: Area under Curve