idsa {gdverse} | R Documentation |
interactive detector for spatial associations(IDSA) model
Description
interactive detector for spatial associations(IDSA) 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,
...
)
Arguments
formula |
A formula of IDSA model. |
data |
A |
wt |
(optional) The spatial weight matrix. When |
discnum |
(optional) Number of multilevel discretization. Default will use |
discmethod |
(optional) The discretization methods. Default all use |
overlay |
(optional) Spatial overlay method. One of |
strategy |
(optional) Discretization strategy. When |
increase_rate |
(optional) The critical increase rate of the number of discretization.
Default is |
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 |
alpha |
(optional) Specifies the size of confidence level. Default is |
... |
(optional) Other arguments passed to |
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
Note
Please note that all variables in the IDSA model need to be continuous data.
The IDSA model requires at least 2^n-1
calculations when has n
explanatory variables.
When there are more than 10 explanatory variables, carefully consider the computational burden of this model.
When there are a large number of explanatory variables, the data dimensionality reduction method can be used
to ensure the trade-off between analysis results and calculation speed.
Author(s)
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
data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g