View source: R/performance_accuracy.R
performance_accuracy | R Documentation |
This function calculates the predictive accuracy of linear or logistic regression models.
performance_accuracy(
model,
method = c("cv", "boot"),
k = 5,
n = 1000,
ci = 0.95,
verbose = TRUE
)
model |
A linear or logistic regression model. A mixed-effects model is also accepted. |
method |
Character string, indicating whether cross-validation
( |
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. |
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.
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.
model <- lm(mpg ~ wt + cyl, data = mtcars)
performance_accuracy(model)
model <- glm(vs ~ wt + mpg, data = mtcars, family = "binomial")
performance_accuracy(model)
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