idsa: interactive detector for spatial associations(IDSA) model

Description

Function for interactive detector for spatial associations model.

Usage

idsa(
  formula,
  data,
  wt = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  alpha = 0.95,
  ...
)

Value

A list.

interaction: the interaction result of IDSA model
risk: whether values of the response variable between a pair of overlay zones are significantly different
number_individual_explanatory_variables: the number of individual explanatory variables used for examining the interaction effects
number_overlay_zones: the number of overlay zones
percentage_finely_divided_zones: the percentage of finely divided zones that are determined by the interaction of variables

Arguments

formula: A formula of IDSA model.
data: A data.frame, tibble or sf object of observation data.
wt: (optional) The spatial weight matrix. When data is not an sf object, must provide wt.
discnum: (optional) Number of multilevel discretization. Default will use 3:8.
discmethod: (optional) The discretization methods. Default all use quantile. Noted that robust will use robust_disc(); rpart will use rpart_disc(); Others use sdsfun::discretize_vector().
overlay: (optional) Spatial overlay method. One of and, or, intersection. Default is and.
strategy: (optional) Discretization strategy. When strategy is 1L, choose the highest SPADE model 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.
seed: (optional) Random number seed, default is 123456789.
alpha: (optional) Specifies the size of confidence level. Default is 0.95.
...: (optional) Other arguments passed to cpsd_disc().

Author

Wenbo Lv lyu.geosocial@gmail.com

References

Yongze Song & Peng Wu (2021) An interactive detector for spatial associations, International Journal of Geographical Information Science, 35:8, 1676-1701, DOI:10.1080/13658816.2021.1882680

Examples

Run this code

data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g

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