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.

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

data('sim')
# \donttest{
tryCatch({
  robust_disc(y ~ xa, data = sim, discnum = 5)
  robust_disc(y ~ .,
              data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
              discnum = 5, cores = 3)
}, error = \(e) message("Skipping Python-dependent example: ", e$message))
#> Skipping Python-dependent example: cannot coerce class ‘c("pandas.DataFrame", "pandas.core.generic.NDFrame", "pandas.core.base.PandasObject", ’ to a data.frame
# }