univariate discretization based on offline change point detection

Determines discretization interval breaks using an optimization algorithm for variance-based change point detection.

Usage

robust_disc(formula, data, discnum, minsize = 1, cores = 1)

Arguments

formula

A formula of univariate discretization.

data

A data.frame or tibble of observation data.

discnum

A numeric vector of discretized classes of columns that need to be discretized.

minsize

(optional) The min size of each discretization group. Default all use 1.

cores

(optional) A positive integer(default is 1). If cores > 1, use python joblib package to parallel computation.

Value

A tibble of discretized columns which need to be discretized.

Note

Please set up python dependence and configure GDVERSE_PYTHON environment variable if you want to run robust_disc(). See vignette('rgdrid',package = 'gdverse') for more details.

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

if (FALSE) { # \dontrun{
## The following code needs to configure the Python environment to run:
data('ndvi')
robust_disc(NDVIchange ~ GDP,data = ndvi,discnum = 5)
robust_disc(NDVIchange ~ .,
            data = dplyr::select(ndvi,-c(Climatezone,Mining)),
            discnum = 10,cores = 6)
} # }