get_deviates: Return a set of vectors with chosen shapes
In flatness: Indices and Tests for Assessing the Flatness of (Rank) Histograms

Description Usage Arguments Details Value References Examples

This function returns an rkhist object containing vectors with chosen shapes or trends. This is intended to be used to apply the Jolliffe-Primo flatness tests of rank histograms (see Jolliffe and Primo, 2008).

1	get_deviates(k, shapes = c("linear", "U", "wave"), constrain = FALSE)

`k`	an integer. The number of possible ranks.
`shapes`	a vector of character strings. The required shapes of the vectors.
`constrain`	a logical. If `TRUE` the returned set of vectors is constrained to be orthonormal, with each vector having components summing to 0. This is required to use the vectors in the Jolliffe-Primo flatness test.

The convention is that each row of the rkhist object contains a vector. It is not required that the set be a basis.

For each shape in shapes this function calls a function named 'deviate_shape' with one argument k. Some pre-coded functions already exist but the user can easily add its own by following this naming convention. The added function must have only one argument k and return an rkhist object. It is advised that the returned deviate vector's components should sum to 0 and have a unit module. But this can be imposed by setting the argument constrain to TRUE.

If constrain == TRUE the vector set is modified to have the right properties to be used in the Jolliffe-Primo test, through the Grahm-Schmidt method. It is strongly advised to plot the resulting set with function flatness::plot, since this transformation may greatly change the shape of the original vectors.

An rkhist object with each row representing a vector of deviation from flatness.

Jolliffe, Ian T., and Cristina Primo. "Evaluating rank histograms using decompositions of the chi-square test statistic." Monthly Weather Review 136.6 (2008): 2133-2139. doi:https://doi.org/10.1175/2007MWR2219.1
Zamo, Michaël, Liliane Bel, and Olivier Mestre. "Sequential aggregation of probabilistic forecasts—application to wind speed ensemble forecasts." Journal of the Royal Statistical Society: Series C (Applied Statistics) 70.1 (2021): 202-225. doi:https://doi.org/10.1111/rssc.12455
Zamo, Michaël. Statistical Post-processing of Deterministic and Ensemble Wind Speed Forecasts on a Grid. Diss. Université Paris-Saclay (ComUE), 2016.

deviates <- get_deviates(k = 36, shapes = c("linear", "U", "V", "ends", "wave"))
plot(deviates)
isJPOK <- is_JP_ready(deviates)
JPdeviates <- make_JP_ready(deviates)
plot(JPdeviates)
JPcheck <- is_JP_ready(JPdeviates)