Title: A Correlation Indicator Based on Spatial Patterns
Version: 0.1.0
Description: Use the spatial association marginal contributions derived from spatial stratified heterogeneity to capture the degree of correlation between spatial patterns.
License: GPL-3
Encoding: UTF-8
RoxygenNote: 7.3.2
URL: https://stscl.github.io/cisp/, https://github.com/stscl/cisp
BugReports: https://github.com/stscl/cisp/issues
Depends: R (≥ 4.1.0)
Imports: dplyr, forcats, gdverse (≥ 1.3), ggplot2, ggraph, igraph, magrittr, parallel, purrr, RColorBrewer, sdsfun (≥ 0.4.3), sf, tibble, tidyr
Suggests: knitr, rmarkdown
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2024-11-20 13:40:22 UTC; dell
Author: Wenbo Lv ORCID iD [aut, cre, cph], Yongze Song ORCID iD [aut], Nan Jia ORCID iD [aut]
Maintainer: Wenbo Lv <lyu.geosocial@gmail.com>
Repository: CRAN
Date/Publication: 2024-11-21 08:00:06 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

NULL (this is the magrittr pipe operator)

spatial pattern correlation

Description

spatial pattern correlation

Usage

spc(
  data,
  overlay = "and",
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

data

A data.frame, tibble or sf object of observation data.

overlay

(optional) Spatial overlay method. One of and, or, intersection. Default is and.

discnum

A numeric vector of discretized classes of columns that need to be discretized. Default all discvar use 3:8.

minsize

(optional) The min size of each discretization group. Default all use 1.

strategy

(optional) Optimal discretization strategy. When strategy is 1L, choose the highest q-statistics to determinate optimal spatial data discretization parameters. When strategy is 2L, The optimal discrete parameters of spatial data are selected by combining LOESS model.

increase_rate

(optional) The critical increase rate of the number of discretization. Default is ⁠5%⁠.

cores

(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

Value

A list.

correlation_tbl

A tibble with power of spatial pattern correlation

correlation_mat

A matrix with power of spatial pattern correlation

Examples

## 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

Description

spatial association marginal contributions derived from spatial stratified heterogeneity

Usage

ssh_marginalcontri(formula, data, overlay = "and", cores = 1)

Arguments

formula

A formula of ISP model.

data

A data.frame, tibble or sf object of observation data.

overlay

(optional) Spatial overlay method. One of and, or, intersection. Default is and.

cores

(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

Value

A list.

pd

robust power of determinants

spd

shap power of determinants

determination

determination of the optimal interaction of variables

Examples

NTDs1 = sf::st_as_sf(gdverse::NTDs, coords = c('X','Y'))
g = ssh_marginalcontri(incidence ~ ., data = NTDs1, cores = 1)
g