robust_disc: univariate discretization based on offline change point...

In gdverse: Analysis of Spatial Stratified Heterogeneity

univariate discretization based on offline change point detection

Description

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.

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(s)

Wenbo Lv [e-mail]

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
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)

## End(Not run)

gdverse documentation built on April 3, 2025, 8:40 p.m.