Title: | A Correlation Indicator Based on Spatial Patterns |
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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] , Nan Jia [aut] |
Maintainer: | Wenbo Lv <[email protected]> |
License: | GPL-3 |
Version: | 0.2.0 |
Built: | 2024-12-04 06:29:50 UTC |
Source: | https://github.com/stscl/cisp |
spatial pattern correlation
spc( data, overlay = "and", discnum = 3:8, minsize = 1, strategy = 2L, increase_rate = 0.05, cores = 1 )
spc( data, overlay = "and", discnum = 3:8, minsize = 1, strategy = 2L, increase_rate = 0.05, cores = 1 )
data |
A |
overlay |
(optional) Spatial overlay method. One of |
discnum |
A numeric vector of discretized classes of columns that need to be discretized.
Default all |
minsize |
(optional) The min size of each discretization group. Default all use |
strategy |
(optional) Optimal 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. |
A list.
correlation_tbl
A tibble with power of spatial pattern correlation
correlation_mat
A matrix with power of spatial pattern correlation
## Not run: ## The following code needs to configure the Python environment to run: sim1 = sf::st_as_sf(gdverse::sim,coords = c('lo','la')) g = spc(sim1, discnum = 3:6, cores = 1) g ## End(Not run)
## Not run: ## The following code needs to configure the Python environment to run: sim1 = sf::st_as_sf(gdverse::sim,coords = c('lo','la')) g = spc(sim1, discnum = 3:6, cores = 1) g ## End(Not run)
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 multi-core parallel computing. |
A list.
pd
robust power of determinants
spd
shap power of determinants
determination
determination 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
NTDs1 = sf::st_as_sf(gdverse::NTDs, coords = c('X','Y')) g = ssh_marginalcontri(incidence ~ ., data = NTDs1, cores = 1) g