robust interaction detector(RID) model

Function for robust interaction detector(RID) model.

Usage

rid(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

formula: A formula of RGD model.
data: A data.frame, tibble or sf object of observation data.
discvar: Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.
discnum: A numeric vector of discretized classes of columns that need to be discretized. Default all discvar use 3:8.
minsize: (optional) The min size of each discretization group. Default all use 1.
strategy: (optional) Optimal discretization strategy. When strategy is 1L, choose the highest q-statistics to determinate optimal spatial data discretization parameters. When strategy is 2L, The optimal discrete parameters of spatial data are selected by combining LOESS model.
increase_rate: (optional) The critical increase rate of the number of discretization. Default is 5%.
cores: (optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

Value

A list.

interaction: the result of RID model

Note

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

References

Zhang, Z., Song, Y., Karunaratne, L., & Wu, P. (2024). Robust interaction detector: A case of road life expectancy analysis. Spatial Statistics, 59(100814), 100814. https://doi.org/10.1016/j.spasta.2024.100814

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

if (FALSE) { # \dontrun{
## The following code needs to configure the Python environment to run:
data('sim')
g = rid(y ~ .,
        data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
        discnum = 3:6, cores = 1)
g
} # }