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): 89.97% [85.01%, 94.24%]
#> 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): 92.50% [76.25%, 100.00%]
#> Method: Area under Curve
```