Test linear regression assumptions easily with a nice summary table.

## Arguments

- model
The

`lm()`

object to be passed to the function.

## Value

A dataframe, with p-value results for the Shapiro-Wilk, Breusch-Pagan, and Durbin-Watson tests, as well as a diagnostic column reporting how many assumptions are not respected for a given model. Shapiro-Wilk is set to NA if n < 3 or n > 5000.

## Details

Interpretation: (p) values < .05 imply assumptions are not respected. Diagnostic is how many assumptions are not respected for a given model or variable.

## See also

Other functions useful in assumption testing:
`nice_density`

, `nice_normality`

,
`nice_qq`

, `nice_varplot`

,
`nice_var`

. Tutorial:
https://rempsyc.remi-theriault.com/articles/assumptions

## Examples

```
# Create a regression model (using data available in R by default)
model <- lm(mpg ~ wt * cyl + gear, data = mtcars)
nice_assumptions(model)
#> Model Normality (Shapiro-Wilk)
#> 1 mpg ~ wt * cyl + gear 0.615
#> Homoscedasticity (Breusch-Pagan) Autocorrelation of residuals (Durbin-Watson)
#> 1 0.054 0.525
#> Diagnostic
#> 1 0
# Multiple dependent variables at once
model2 <- lm(qsec ~ disp + drat * carb, mtcars)
my.models <- list(model, model2)
nice_assumptions(my.models)
#> Model Normality (Shapiro-Wilk)
#> 1 mpg ~ wt * cyl + gear 0.615
#> 2 qsec ~ disp + drat * carb 0.013
#> Homoscedasticity (Breusch-Pagan) Autocorrelation of residuals (Durbin-Watson)
#> 1 0.054 0.525
#> 2 0.947 0.003
#> Diagnostic
#> 1 0
#> 2 2
```