PIT Histogram

Make a simple histogram of the probability integral transformed values to visually check whether a uniform distribution seems likely.

Usage

plot_pit(pit, num_bins = "auto", breaks = NULL)

Arguments

pit

either a vector with the PIT values of size n, or a data.frame as produced by pit()

num_bins

the number of bins in the PIT histogram, default is "auto". When num_bins == "auto", plot_pit() will either display 10 bins, or it will display a bin for each available quantile in case you passed in data in a quantile-based format. You can control the number of bins by supplying a number. This is fine for sample-based pit histograms, but may fail for quantile-based formats. In this case it is preferred to supply explicit breaks points using the breaks argument.

breaks

numeric vector with the break points for the bins in the PIT histogram. This is preferred when creating a PIT histogram based on quantile-based data. Default is NULL and breaks will be determined by num_bins.

Value

vector with the scoring values

Examples

# \dontshow{
  data.table::setDTthreads(2) # restricts number of cores used on CRAN
# }

# PIT histogram in vector based format
true_values <- rnorm(30, mean = 1:30)
predictions <- replicate(200, rnorm(n = 30, mean = 1:30))
pit <- pit_sample(true_values, predictions)
plot_pit(pit)


# quantile-based pit
pit <- pit(example_quantile,by = "model")
#> The following messages were produced when checking inputs:
#> 1.  144 values for `prediction` are NA in the data provided and the corresponding rows were removed. This may indicate a problem if unexpected.
plot_pit(pit, breaks = seq(0.1, 1, 0.1))


# sample-based pit
pit <- pit(example_integer,by = "model")
#> The following messages were produced when checking inputs:
#> 1.  144 values for `prediction` are NA in the data provided and the corresponding rows were removed. This may indicate a problem if unexpected.
plot_pit(pit)