robust geographical detector(RGD) model

Function for robust geographical detector(RGD) model.

Usage

rgd(
  formula,
  data,
  discvar = NULL,
  discnum = NULL,
  minsize = NULL,
  cores = 1,
  type = "factor",
  alpha = 0.95
)

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 10.
minsize: (optional) The min size of each discretization group. Default all use 1.
cores: (optional) Positive integer(default is 1). If cores > 1, use python joblib package to parallel computation.
type: (optional) The type of geographical detector, which must be factor(default), interaction, risk, ecological.You can run one or more types at one time.
alpha: (optional) Specifies the size of confidence level. Default is 0.95.

Value

A list of the RGD model result.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Note

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

References

Zhang, Z., Song, Y.*, & Wu, P., 2022. Robust geographical detector. International Journal of Applied Earth Observation and Geoinformation. 109, 102782. DOI: 10.1016/j.jag.2022.102782.

Author

Wenbo Lv lyu.geosocial@gmail.com

Examples

if (FALSE) { # \dontrun{
## The following code needs to configure the Python environment to run:
data('ndvi')
g = rgd(NDVIchange ~ ., data = ndvi, discvar = names(ndvi)[-1:-3],
        cores = 6, type =c('factor','interaction'))
} # }