Posterior predictive checks mean "simulating replicated data
under the fitted model and then comparing these to the observed data"
(*Gelman and Hill, 2007, p. 158*). Posterior predictive checks
can be used to "look for systematic discrepancies between real and
simulated data" (*Gelman et al. 2014, p. 169*).

**performance** provides posterior predictive check methods for a variety
of frequentist models (e.g., `lm`

, `merMod`

, `glmmTMB`

, ...). For Bayesian
models, the model is passed to `bayesplot::pp_check()`

.

## Usage

```
check_predictions(object, ...)
# S3 method for default
check_predictions(
object,
iterations = 50,
check_range = FALSE,
re_formula = NULL,
bandwidth = "nrd",
type = "density",
verbose = TRUE,
...
)
posterior_predictive_check(object, ...)
check_posterior_predictions(object, ...)
```

## Arguments

- object
A statistical model.

- ...
Passed down to

`simulate()`

.- iterations
The number of draws to simulate/bootstrap.

- check_range
Logical, if

`TRUE`

, includes a plot with the minimum value of the original response against the minimum values of the replicated responses, and the same for the maximum value. This plot helps judging whether the variation in the original data is captured by the model or not (*Gelman et al. 2020, pp.163*). The minimum and maximum values of`y`

should be inside the range of the related minimum and maximum values of`yrep`

.- re_formula
Formula containing group-level effects (random effects) to be considered in the simulated data. If

`NULL`

(default), condition on all random effects. If`NA`

or`~0`

, condition on no random effects. See`simulate()`

in**lme4**.- bandwidth
A character string indicating the smoothing bandwidth to be used. Unlike

`stats::density()`

, which used`"nrd0"`

as default, the default used here is`"nrd"`

(which seems to give more plausible results for non-Gaussian models). When problems with plotting occur, try to change to a different value.- type
Plot type for the posterior predictive checks plot. Can be

`"density"`

,`"discrete_dots"`

,`"discrete_interval"`

or`"discrete_both"`

(the`discrete_*`

options are appropriate for models with discrete - binary, integer or ordinal etc. - outcomes).- verbose
Toggle warnings.

## Details

An example how posterior predictive checks can also be used for model
comparison is Figure 6 from *Gabry et al. 2019, Figure 6*.

The model shown in the right panel (b) can simulate new data that are more
similar to the observed outcome than the model in the left panel (a). Thus,
model (b) is likely to be preferred over model (a).

## Note

Every model object that has a `simulate()`

-method should work with
`check_predictions()`

. On R 3.6.0 and higher, if **bayesplot** (or a
package that imports **bayesplot** such as **rstanarm** or **brms**)
is loaded, `pp_check()`

is also available as an alias for `check_predictions()`

.

## References

Gabry, J., Simpson, D., Vehtari, A., Betancourt, M., and Gelman, A. (2019). Visualization in Bayesian workflow. Journal of the Royal Statistical Society: Series A (Statistics in Society), 182(2), 389–402. https://doi.org/10.1111/rssa.12378

Gelman, A., and Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge; New York: Cambridge University Press.

Gelman, A., Carlin, J. B., Stern, H. S., Dunson, D. B., Vehtari, A., and Rubin, D. B. (2014). Bayesian data analysis. (Third edition). CRC Press.

Gelman, A., Hill, J., and Vehtari, A. (2020). Regression and Other Stories. Cambridge University Press.

## See also

Other functions to check model assumptions and and assess model quality:
`check_autocorrelation()`

,
`check_collinearity()`

,
`check_convergence()`

,
`check_heteroscedasticity()`

,
`check_homogeneity()`

,
`check_model()`

,
`check_outliers()`

,
`check_overdispersion()`

,
`check_singularity()`

,
`check_zeroinflation()`