pit_df: Probability Integral Transformation (data.frame Format)
In nikosbosse/scoringutils2:

Description Usage Arguments Details Value References Examples

Wrapper around 'pit()' for use in data.frames

pit_df(
  data,
  plot = TRUE,
  full_output = FALSE,
  n_replicates = 20,
  num_bins = NULL,
  verbose = FALSE
)

`data`	a data.frame with the following columns: 'true_value', 'prediction', 'sample'
`plot`	logical. If TRUE, a histogram of the PIT values will be returned as well
`full_output`	return all individual p_values and computed u_t values for the randomised PIT. Usually not needed.
`n_replicates`	the number of tests to perform, each time re-randomising the PIT
`num_bins`	the number of bins in the PIT histogram (if plot == TRUE) If not given, the square root of n will be used
`verbose`	if TRUE (default is FALSE) more error messages are printed. Usually, this should not be needed, but may help with debugging.

see pit

a list with the following components:

p_value: p-value of the Anderson-Darling test on the PIT values. In case of integer forecasts, this will be the mean p_value from the 'n_replicates' replicates
sd: standard deviation of the p_value returned. In case of continuous forecasts, this will be NA as there is only one p_value returned.
hist_PIT a plot object with the PIT histogram. Only returned if plot == TRUE. Call plot(PIT(...)$hist_PIT) to display the histogram.
p_values: all p_values generated from the Anderson-Darling tests on the randomised PIT. Only returned for integer forecasts and if full_output = TRUE
u: the u_t values internally computed. Only returned if full_output = TRUE

Sebastian Funk, Anton Camacho, Adam J. Kucharski, Rachel Lowe, Rosalind M. Eggo, W. John Edmunds (2019) Assessing the performance of real-time epidemic forecasts: A case study of Ebola in the Western Area region of Sierra Leone, 2014-15, <doi:10.1371/journal.pcbi.1006785>