idsa {gdverse} | R Documentation |
Function for interactive detector for spatial associations model.
idsa(
formula,
data,
wt = NULL,
overlaymethod = "and",
discnum = 3:22,
discmethod = "quantile",
strategy = 2L,
increase_rate = 0.05,
cores = 1,
seed = 123456789,
alpha = 0.95,
...
)
formula |
A formula of IDSA model. |
data |
A data.frame, tibble or sf object of observation data. |
wt |
(optional) The spatial weight matrix. When |
overlaymethod |
(optional) Spatial overlay method. One of |
discnum |
(optional) Number of multilevel discretization. Default will use |
discmethod |
(optional) The discretization methods. Default all use |
strategy |
(optional) Discretization strategy. When |
increase_rate |
(optional) The critical increase rate of the number of discretization.
Default is |
cores |
(optional) A positive integer(default is 1). If cores > 1, a 'parallel' package cluster with that many cores is created and used. You can also supply a cluster object. |
seed |
(optional) Random number seed, default is |
alpha |
(optional) Specifies the size of confidence level. Default is |
... |
(optional) Other arguments passed to |
A list with PID values tibble under different spatial overlays and performance evaluation indicators.
interaction
the interaction result of IDSA model
risk1
whether values of the response variable between a pair of overlay zones are significantly different
risk2
risk detection result of the input data
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
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.
Wenbo Lv lyu.geosocial@gmail.com
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
data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g