idsa {gdverse} | R Documentation |
interactive detector for spatial associations(IDSA) model
Description
Function for interactive detector for spatial associations model.
Usage
idsa(
formula,
data,
wt = NULL,
overlaymethod = "and",
locations = NULL,
discnum = NULL,
discmethod = NULL,
strategy = 2L,
increase_rate = 0.05,
cores = 1,
seed = 123456789,
alpha = 0.95,
...
)
Arguments
formula |
A formula of IDSA model. |
data |
A data.frame or tibble of observation data. |
wt |
(optional) The spatial weight matrix. When |
overlaymethod |
(optional) Spatial overlay method. One of |
locations |
(optional) The spatial location coordinate columns name in |
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 |
Value
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
Note
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')
g = idsa(y ~ ., data = sim,
locations = c('lo','la'),
discvar = c("xa","xb","xc"))
g