Assert that inputs are correct for categorical forecasts — assert_input

Function assesses whether the inputs correspond to the requirements for scoring categorical, i.e. either nominal or ordinal forecasts.

Usage

assert_input_categorical(observed, predicted, predicted_label, ordered = NA)

observed: Input to be checked. Should be a factor of length n with N levels holding the observed values. n is the number of observations and N is the number of possible outcomes the observed values can assume.
predicted: Input to be checked. Should be nxN matrix of predicted probabilities, n (number of rows) being the number of data points and N (number of columns) the number of possible outcomes the observed values can assume. If observed is just a single number, then predicted can just be a vector of size N. Values represent the probability that the corresponding value in observed will be equal to the factor level referenced in predicted_label.
predicted_label: Factor of length N with N levels, where N is the number of possible outcomes the observed values can assume.
ordered: Value indicating whether factors have to be ordered or not. Defaults to NA, which means that the check is not performed.

Returns NULL invisibly if the assertion was successful and throws an error otherwise.