Accuracy of predictions from model fit
This function calculates the predictive accuracy of linear or logistic regression models.
performance_accuracy(model, method = c("cv", "boot"), k = 5, n = 1000)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. |
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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