,
Yongze Song [aut] ,
Rui Qu [aut] ,
Wufan Zhao [aut] ,
Nan Jia [aut] | Title: | A Correlation Indicator Based on Spatial Patterns |
|---|---|
| Description: | Use the spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns. |
| Authors: | Wenbo Lv [aut, cre, cph] ,
Yongze Song [aut] ,
Rui Qu [aut] ,
Wufan Zhao [aut] ,
Nan Jia [aut] |
| Maintainer: | Wenbo Lv <[email protected]> |
| License: | GPL-3 |
| Version: | 0.2.0 |
| Built: | 2025-01-24 15:25:48 UTC |
| Source: | https://github.com/stscl/cisp |
spatial pattern correlation
spc( data, overlay = "and", discnum = 3:8, discmethod = c("sd", "equal", "geometric", "quantile", "natural"), cores = 1 )spc( data, overlay = "and", discnum = 3:8, discmethod = c("sd", "equal", "geometric", "quantile", "natural"), cores = 1 )
data |
A |
overlay |
(optional) Spatial overlay method. One of |
discnum |
(optional) A vector of number of classes for discretization. Default is |
discmethod |
(optional) A vector of methods for discretization, default is using
|
cores |
(optional) Positive integer (default is 1). When cores are greater than 1, use parallel computing. |
A list.
cor_tblA tibble with power of spatial pattern correlation
cor_matA matrix with power of spatial pattern correlation
sim1 = sf::st_as_sf(gdverse::sim,coords = c('lo','la')) sim1 g = spc(sim1, discnum = 3:6, cores = 1) g plot(g,"matrix")sim1 = sf::st_as_sf(gdverse::sim,coords = c('lo','la')) sim1 g = spc(sim1, discnum = 3:6, cores = 1) g plot(g,"matrix")
spatial association marginal contributions derived from spatial stratified heterogeneity
ssh_marginalcontri(formula, data, overlay = "and", cores = 1)ssh_marginalcontri(formula, data, overlay = "and", cores = 1)
formula |
A formula of ISP model. |
data |
A |
overlay |
(optional) Spatial overlay method. One of |
cores |
(optional) Positive integer (default is 1). When cores are greater than 1, use parallel computing. |
A list.
pdrobust power of determinants
spdshap power of determinants
determinationdetermination of the optimal interaction of variables
NTDs1 = sf::st_as_sf(gdverse::NTDs, coords = c('X','Y')) g = ssh_marginalcontri(incidence ~ ., data = NTDs1, cores = 1) g plot(g)NTDs1 = sf::st_as_sf(gdverse::NTDs, coords = c('X','Y')) g = ssh_marginalcontri(incidence ~ ., data = NTDs1, cores = 1) g plot(g)