Normality test of a numeric vector

Applies a Shapiro-Wilks test to a numeric vector, and plots the qq plot and the histogram.

Usage

residuals_diagnostics(residuals, predictions)

Arguments

residuals

Numeric vector, model residuals.

predictions

Numeric vector, model predictions.

Value

A list with four slots:

• /item w W statistic returned by shapiro.test(). /item p.value p-value of the Shapiro test. /item interpretation Character vector, one of "x is normal", "x is not normal". /item plot A patchwork plot with the qq plot and the histogram of x.

Details

The function shapiro.test() has a hard limit of 5000 cases. If the model residuals have more than 5000 cases, then sample(x = residuals, size = 5000) is applied to the model residuals before the test.

See also

ggplot,aes,geom_qq_line,ggtheme,labs,geom_freqpoly,geom_abline plot_annotation

Other spatial_analysis: filter_spatial_predictors(), mem(), mem_multithreshold(), moran(), moran_multithreshold(), pca(), pca_multithreshold(), rank_spatial_predictors(), residuals_test(), select_spatial_predictors_recursive(), select_spatial_predictors_sequential()

Examples

data(plants_rf)

y <- residuals_diagnostics(
  residuals = get_residuals(plants_rf),
  predictions = get_predictions(plants_rf)
)
y
#> $shapiro.w
#> [1] 0.7976006
#> 
#> $p.value
#> [1] 1.87131e-16
#> 
#> $interpretation
#> [1] "Residuals are not normal"
#> 
#> $plot

#>