Help for package gdverse

Title:

Analysis of Spatial Stratified Heterogeneity

Version:

1.3-3

Description:

Detecting spatial associations via spatial stratified heterogeneity, accounting for spatial dependencies, interpretability, complex interactions, and robust stratification. In addition, it supports the spatial stratified heterogeneity family described in Lv et al. (2025)<doi:10.1111/tgis.70032>.

License:

GPL-3

Encoding:

UTF-8

RoxygenNote:

7.3.2

URL:

https://stscl.github.io/gdverse/, https://github.com/stscl/gdverse

BugReports:

https://github.com/stscl/gdverse/issues

Depends:

R (≥ 4.1.0)

Imports:

dplyr, forcats, ggplot2, magrittr, parallel, patchwork, purrr, reticulate, rpart, scatterpie, sdsfun (≥ 0.7.0), sf, stats, tibble, tidyr, utils

Suggests:

cowplot, itmsa, knitr, Rcpp, readr, rmarkdown, terra, testthat (≥ 3.0.0)

LinkingTo:

Rcpp

VignetteBuilder:

knitr

LazyData:

true

Config/testthat/edition:

NeedsCompilation:

yes

Packaged:

2025-04-02 03:54:52 UTC; 31809

Author:

Wenbo Lv

[aut, cre, cph], Yangyang Lei

[aut], Fangmei Liu

[aut], Jianwu Yan

[aut], Yongze Song

[aut], Wufan Zhao

[aut]

Maintainer:

Wenbo Lv <lyu.geosocial@gmail.com>

Repository:

CRAN

Date/Publication:

2025-04-02 09:40:14 UTC

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Value

NULL (this is the magrittr pipe operator)

measure information loss by information entropy

Description

Function for measure information loss by shannon information entropy.

Usage

F_informationloss(xvar, xdisc)

Arguments

xvar

The original undiscretized vector.

xdisc

The discretized vector.

Details

The information loss measured by information entropy formula is F = -\sum\limits_{i=1}^N p_{(i)}\log_2 p_{(i)} - \left(-\sum\limits_{h=1}^L p_{(h)}\log_2 p_{(h)}\right)

Value

A numeric value of information loss measured by information entropy.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

F_informationloss(1:7,c('x',rep('y',3),rep('z',3)))

NTDs data

Description

The data were obtained by preprocessing use sf and tidyverse.

Usage

NTDs

Format

NTDs: A tibble with 185 rows and 4 variable columns and 2 location columns, modified from geodetector package.

convert all discretized vectors to integer

Description

convert all discretized vectors to integer

Usage

all2int(x)

Arguments

x

A discretized vector.

Value

An integer vector.

Examples

all2int(factor(letters[1:3],levels = c('b','a','c')))
all2int(letters[1:3])

optimal spatial data discretization based on SPADE q-statistics

Description

Function for determining the optimal spatial data discretization based on SPADE q-statistics.

Usage

cpsd_disc(
  formula,
  data,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

formula

A formula of optimal spatial data discretization.

data

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

wt

The spatial weight matrix.

discnum

(optional) A vector of number of classes for discretization. Default is 3:8.

discmethod

(optional) The discretization methods. Default all use quantile. Noted that rpart will use rpart_disc(); Others use sdsfun::discretize_vector().

strategy

(optional) Discretization strategy. When strategy is 1L, choose the highest SPADE model 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.

seed

(optional) Random seed number, default is 123456789.

...

(optional) Other arguments passed to sdsfun::discretize_vector() or rpart_disc().

Value

A list.

x: discretization variable name
k: optimal number of spatial data discreteization
method: optimal spatial data discretization method
disc: the result of optimal spatial data discretization

Note

When the discmethod is configured to robust, it will operate at a significantly reduced speed. Consequently, the use of robust discretization is not advised.

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')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
cpsd_disc(y ~ xa + xb + xc, data = sim, wt = wt)

compensated power of spatial determinant(CPSD)

Description

Function for calculate compensated power of spatial determinant Q_s.

Usage

cpsd_spade(yobs, xobs, xdisc, wt)

Arguments

yobs

Variable Y

xobs

The original undiscretized covariable X.

xdisc

The discretized covariable X.

wt

The spatial weight matrix.

Details

The power of compensated spatial determinant formula is

Q_s = \frac{q_s}{q_{s_{inforkep}}} = \frac{1 - \frac{\sum_{h=1}^L N_h \Gamma_{kdep}}{N \Gamma_{totaldep}}}{1 - \frac{\sum_{h=1}^L N_h \Gamma_{hind}}{N \Gamma_{totalind}}}

Value

A value of compensated power of spatial determinant Q_s.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
xa = sim$xa
xa_disc = sdsfun::discretize_vector(xa,5)
cpsd_spade(sim$y,xa,xa_disc,wt)

ecological detector

Description

Compare the effects of two factors X_1 and X_2 on the spatial distribution of the attribute Y.

Usage

ecological_detector(y, x1, x2, alpha = 0.95)

Arguments

y

Dependent variable, continuous numeric vector.

x1

Covariate X_1, factor, character or discrete numeric.

x2

Covariate X_2, factor, character or discrete numeric.

alpha

(optional) Confidence level of the interval,default is 0.95.

Value

A list.

F-statistic: the result of F statistic for ecological detector
P-value: the result of P value for ecological detector
Ecological: is there a significant difference between the two factors X_1 and X_2 on the spatial distribution of the attribute Y

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

ecological_detector(y = 1:7,
                    x1 = c('x',rep('y',3),rep('z',3)),
                    x2 = c(rep('a',2),rep('b',2),rep('c',3)))

factor detector

Description

The factor detector q-statistic measures the spatial stratified heterogeneity of a variable Y, or the determinant power of a covariate X of Y.

Usage

factor_detector(y, x, confintv = FALSE, alpha = 0.95)

Arguments

y

Variable Y, continuous numeric vector.

x

Covariate X, factor, character or discrete numeric.

confintv

(optional) Whether to compute the confidence interval for the q statistic, default is FALSE.

alpha

(optional) Confidence level of the interval, default is 0.95.

Value

A list.

Q-statistic: the q statistic for factor detector
P-value: the p value for factor detector
CIL: the confidence interval lower bound
CIU: the confidence interval upper bound

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

factor_detector(y = 1:7,x = c('x',rep('y',3),rep('z',3)))

native geographical detector(GD) model

Description

native geographical detector(GD) model

Usage

gd(formula, data, type = "factor", alpha = 0.95)

Arguments

formula

A formula of geographical detector model.

data

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

type

(optional) The type of geographical detector, which must be one of factor(default), interaction, risk, ecological. You can run one or more types at one time.

alpha

(optional) Specifies the size of the alpha (confidence level). Default is 0.95.

Value

A list.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Jin‐Feng Wang, Xin‐Hu Li, George Christakos, Yi‐Lan Liao, Tin Zhang, XueGu & Xiao‐Ying Zheng (2010) Geographical Detectors‐Based Health Risk Assessment and its Application in the Neural Tube Defects Study of the Heshun Region, China, International Journal of Geographical Information Science, 24:1, 107-127, DOI: 10.1080/13658810802443457

Examples

data("NTDs")
g = gd(incidence ~ watershed + elevation + soiltype,
       data = NTDs,type = c('factor','interaction'))
g

optimal univariate discretization based on geodetector q-statistic

Description

optimal univariate discretization based on geodetector q-statistic

Usage

gd_optunidisc(
  formula,
  data,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

formula

A formula.

data

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

discnum

(optional) A vector of numbers of discretization. Default is 3:8.

discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.

cores

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

seed

(optional) Random seed number, default is 123456789.

...

(optional) Other arguments passed to sdsfun::discretize_vector().

Value

A list.

x: the name of the variable that needs to be discretized
k: optimal discretization number
method: optimal discretization method
qstatistic: optimal q-statistic
disc: optimal discretization results

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

data('sim')
gd_optunidisc(y ~ xa + xb + xc,
              data = sim,
              discnum = 3:6)

generate permutations

Description

generate permutations

Usage

gen_permutations(x, seed = 123456789)

Arguments

x

A vector.

seed

(optional) Random seed number. Default is 123456789.

Value

A permutations vector.

Examples

gen_permutations(1:100,42)

geographical detector

Description

geographical detector

Usage

geodetector(formula, data, type = "factor", alpha = 0.95)

Arguments

formula

A formula of geographical detector model.

data

A data.frame or tibble of observation data.

type

(optional) The type of geographical detector, which must be one of factor(default), interaction, risk, ecological.

alpha

(optional) Specifies the size of the alpha (confidence level). Default is 0.95.

Value

A list.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Note

Note that only one type of geodetector is supported at a time in geodetector().

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))))

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'interaction')

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'risk',alpha = 0.95)

geodetector(y ~ x1 + x2,
   tibble::tibble(y = 1:7,
                  x1 = c('x',rep('y',3),rep('z',3)),
                  x2 = c(rep('a',2),rep('b',2),rep('c',3))),
   type = 'ecological',alpha = 0.95)

geographically optimal zones-based heterogeneity(GOZH) model

Description

geographically optimal zones-based heterogeneity(GOZH) model

Usage

gozh(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)

Arguments

formula

A formula of GOZH model.

data

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

cores

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

type

(optional) The type of geographical detector, which must be factor(default), interaction, risk, ecological. You can run one or more types at one time.

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to rpart_disc().

Value

A list.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Luo, P., Song, Y., Huang, X., Ma, H., Liu, J., Yao, Y., & Meng, L. (2022). Identifying determinants of spatio-temporal disparities in soil moisture of the Northern Hemisphere using a geographically optimal zones-based heterogeneity model. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing (ISPRS), 185, 111–128. https://doi.org/10.1016/j.isprsjprs.2022.01.009

Examples

data('ndvi')
g = gozh(NDVIchange ~ ., data = ndvi)
g

geographically optimal zones-based heterogeneity detector

Description

Function for geographically optimal zones-based heterogeneity detector.

Usage

gozh_detector(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)

Arguments

formula

A formula of GOZH detector.

data

A data.frame or tibble of observation data.

cores

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

type

(optional) The type of geographical detector,which must be one of factor(default), interaction, risk, ecological.

alpha

(optional) Confidence level of the interval,default is 0.95.

...

(optional) Other arguments passed to rpart_disc().

Value

A list of tibble with the corresponding result under different detector types.

factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Note

Only one type of detector is supported in a gozh_detector() run at a time.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

data('ndvi')
g = gozh_detector(NDVIchange ~ ., data = ndvi)
g

interactive detector for spatial associations(IDSA) model

Description

interactive detector for spatial associations(IDSA) model

Usage

idsa(
  formula,
  data,
  wt = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  seed = 123456789,
  alpha = 0.95,
  ...
)

Arguments

formula

A formula of IDSA model.

data

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

wt

(optional) The spatial weight matrix. When data is not an sf object, must provide wt.

discnum

(optional) Number of multilevel discretization. Default will use 3:8.

discmethod

(optional) The discretization methods. Default all use quantile. Noted that rpart will use rpart_disc(); Others use sdsfun::discretize_vector().

overlay

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

strategy

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.

seed

(optional) Random number seed, default is 123456789.

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to cpsd_disc().

Value

A list.

interaction: the interaction result of IDSA model
risk: whether values of the response variable between a pair of overlay zones are significantly different
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

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.

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')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = idsa(y ~ ., data = sim1)
g

interaction detector

Description

Identify the interaction between different risk factors, that is, assess whether factors X1 and X2 together increase or decrease the explanatory power of the dependent variable Y, or whether the effects of these factors on Y are independent of each other.

Usage

interaction_detector(y, x1, x2)

Arguments

y

Dependent variable, continuous numeric vector.

x1

Covariate X_1, factor, character or discrete numeric.

x2

Covariate X_2, factor, character or discrete numeric.

Value

A list.

Variable1 Q-statistics: Q-statistics for variable1
Variable2 Q-statistics: Q-statistics for variable2
Variable1 and Variable2 interact Q-statistics: Q-statistics for variable1 and variable2 interact
Interaction: the interact result type

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

interaction_detector(y = 1:7,
                     x1 = c('x',rep('y',3),rep('z',3)),
                     x2 = c(rep('a',2),rep('b',2),rep('c',3)))

locally explained stratified heterogeneity(LESH) model

Description

locally explained stratified heterogeneity(LESH) model

Usage

lesh(formula, data, cores = 1, ...)

Arguments

formula

A formula of LESH model.

data

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

cores

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

...

(optional) Other arguments passed to rpart_disc().

Value

A list.

interaction: the interaction result of LESH model
spd_lesh: a tibble of the shap power of determinants

Note

The LESH 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

Li, Y., Luo, P., Song, Y., Zhang, L., Qu, Y., & Hou, Z. (2023). A locally explained heterogeneity model for examining wetland disparity. International Journal of Digital Earth, 16(2), 4533–4552. https://doi.org/10.1080/17538947.2023.2271883

Examples

data('ndvi')
g = lesh(NDVIchange ~ ., data = ndvi)
g

determine optimal spatial data analysis scale

Description

Function for determining optimal spatial data analysis scale based on locally estimated scatter plot smoothing (LOESS) model.

Usage

loess_optscale(qvec, spscalevec, increase_rate = 0.05)

Arguments

qvec

A numeric vector of q statistics.

spscalevec

A numeric vector of spatial scales corresponding to qvec.

increase_rate

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

Value

A numeric vector about optimal number of spatial scale and the critical increase rate of q value.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
qv1000 = factor_detector(fvc1000$fvc,
                         sdsfun::discretize_vector(fvc1000$premax,10,'quantile'))[[1]]
qv5000 = factor_detector(fvc5000$fvc,
                         sdsfun::discretize_vector(fvc5000$premax,10,'quantile'))[[1]]
loess_optscale(c(qv1000,qv5000),c(1000,5000))

## End(Not run)

dataset of NDVI changes and its influencing factors

Description

dataset of NDVI changes and its influencing factors, modified from GD package.

Usage

ndvi

Format

ndvi: A tibble with 713 rows and 7 variables

Author(s)

Yongze Song yongze.song@outlook.com

optimal parameters-based geographical detector(OPGD) model

Description

optimal parameters-based geographical detector(OPGD) model

Usage

opgd(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  type = "factor",
  alpha = 0.95,
  ...
)

Arguments

formula

A formula of OPGD model.

data

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

discvar

Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.

discnum

(optional) A vector of number of classes for discretization. Default is 3:8.

discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.

cores

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

type

(optional) The type of geographical detector, which must be factor(default), interaction, risk, ecological. You can run one or more types at one time.

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to gd_optunidisc(). A useful parameter is seed, which is used to set the random number seed.

Value

A list.

opt_param: optimal discretization parameter
factor: the result of factor detector
interaction: the result of interaction detector
risk: the result of risk detector
ecological: the result of ecological detector

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Song, Y., Wang, J., Ge, Y. & Xu, C. (2020) An optimal parameters-based geographical detector model enhances geographic characteristics of explanatory variables for spatial heterogeneity analysis: Cases with different types of spatial data, GIScience & Remote Sensing, 57(5), 593-610. doi: 10.1080/15481603.2020.1760434.

Examples

data('sim')
opgd(y ~ xa + xb + xc, data = sim,
     discvar = paste0('x',letters[1:3]),
     discnum = 3:6)

IDSA Q-saistics `PID`

Description

IDSA Q-saistics PID

Usage

pid_idsa(formula, rawdata, discdata, wt, overlaymethod = "and")

Arguments

formula

A formula for IDSA Q-saistics

rawdata

Raw observation data

discdata

Observed data with discrete explanatory variables

wt

Spatial weight matrix

overlaymethod

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

Details

Q_{IDSA} = \frac{\theta_r}{\phi}

Value

The value of IDSA Q-saistics PID.

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
sim1 = dplyr::mutate(sim,dplyr::across(xa:xc,\(.x) sdsfun::discretize_vector(.x,5)))
pid_idsa(y ~ xa + xb + xc, rawdata = sim,
         discdata = sim1, wt = wt)

plot ecological detector

Description

S3 method to plot output for ecological detector in geodetector().

Usage

## S3 method for class 'ecological_detector'
plot(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot factor detector result

Description

S3 method to plot output for factor detector in geodetector().

Usage

## S3 method for class 'factor_detector'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, qlabelsize = 3.88, ...)

Arguments

x

Return by geodetector().

slicenum

(optional) The number of labels facing inward. Default is 2.

alpha

(optional) Confidence level. Default is 0.95.

keep

(optional) Whether to keep Q-value results for insignificant variables, default is TRUE.

qlabelsize

(optional) Set the font size of the q-value text labels in the plot.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot GD result

Description

S3 method to plot output for GD model result in gd().

Usage

## S3 method for class 'gd_result'
plot(x, ...)

Arguments

x

Return by gd().

...

(optional) Other arguments.

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot GOZH result

Description

S3 method to plot output for GOZH model result in gozh().

Usage

## S3 method for class 'gozh_result'
plot(x, ...)

Arguments

x

Return by gozh().

...

(optional) Other arguments.

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot IDSA risk result

Description

S3 method to plot output for IDSA risk result in idsa().

Usage

## S3 method for class 'idsa_result'
plot(x, ...)

Arguments

x

Return by idsa().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot interaction detector result

Description

S3 method to plot output for interaction detector in geodetector().

Usage

## S3 method for class 'interaction_detector'
plot(x, alpha = 1, ...)

Arguments

x

Return by geodetector().

alpha

(optional) Picture transparency. Default is 1.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot LESH model result

Description

S3 method to plot output for LESH model interaction result in lesh().

Usage

## S3 method for class 'lesh_result'
plot(
  x,
  pie = TRUE,
  scatter = FALSE,
  scatter_alpha = 1,
  pieradius_factor = 15,
  pielegend_x = 0.99,
  pielegend_y = 0.1,
  pielegend_num = 3,
  ...
)

Arguments

x

x Return by lesh().

pie

(optional) Whether to draw the interaction contributions. Default is TRUE.

scatter

(optional) Whether to draw the interaction direction diagram. Default is FALSE.

scatter_alpha

(optional) Picture transparency. Default is 1.

pieradius_factor

(optional) The radius expansion factor of interaction contributions pie plot. Default is 15.

pielegend_x

(optional) The X-axis relative position of interaction contributions pie plot legend. Default is 0.99.

pielegend_y

(optional) The Y-axis relative position of interaction contributions pie plot legend. Default is 0.1.

pielegend_num

(optional) The number of interaction contributions pie plot legend. Default is 3.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Note

When both scatter and pie are set to TRUE in RStudio, enlarge the drawing frame for normal display.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot OPGD result

Description

S3 method to plot output for OPGD model result in opgd().

Usage

## S3 method for class 'opgd_result'
plot(x, ...)

Arguments

x

Return by opgd().

...

(optional) Other arguments.

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot RGD result

Description

S3 method to plot output for RGD model result in rgd().

Usage

## S3 method for class 'rgd_result'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, ...)

Arguments

x

Return by rgd().

slicenum

(optional) The number of labels facing inward. Default is 2.

alpha

(optional) Confidence level. Default is 0.95.

keep

(optional) Whether to keep Q-value results for insignificant variables, default is TRUE.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot RID result

Description

S3 method to plot output for RID model from rid().

Usage

## S3 method for class 'rid_result'
plot(x, alpha = 1, ...)

Arguments

x

Return by rid().

alpha

(optional) Picture transparency. Default is 1.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot risk detector

Description

S3 method to plot output for risk detector in geodetector().

Usage

## S3 method for class 'risk_detector'
plot(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot gozh sesu

Description

S3 method to plot output for gozh sesu in sesu_gozh().

Usage

## S3 method for class 'sesu_gozh'
plot(x, ...)

Arguments

x

Return by sesu_gozh().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot opgd sesu

Description

S3 method to plot output for opgd sesu in sesu_opgd().

Usage

## S3 method for class 'sesu_opgd'
plot(x, ...)

Arguments

x

Return by sesu_opgd().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot SPADE power of spatial and multilevel discretization determinant

Description

S3 method to plot output for SPADE power of spatial and multilevel discretization determinant from spade().

Usage

## S3 method for class 'spade_result'
plot(x, slicenum = 2, alpha = 0.95, keep = TRUE, ...)

Arguments

x

Return by spade().The number of labels facing inward.

slicenum

(optional) The number of labels facing inward. Default is 2.

alpha

(optional) Confidence level.Default is 0.95.

keep

(optional) Whether to keep Q-value results for insignificant variables, default is TRUE.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot spatial rough set-based ecological detector

Description

S3 method to plot output for spatial rough set-based ecological detector in srsgd().

Usage

## S3 method for class 'srs_ecological_detector'
plot(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot spatial rough set-based factor detector result

Description

S3 method to plot output for spatial rough set-based factor detector in srsgd().

Usage

## S3 method for class 'srs_factor_detector'
plot(x, slicenum = 2, ...)

Arguments

x

Return by srsgd().

slicenum

(optional) The number of labels facing inward. Default is 2.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot spatial rough set-based interaction detector result

Description

S3 method to plot output for spatial rough set-based interaction detector in srsgd().

Usage

## S3 method for class 'srs_interaction_detector'
plot(x, alpha = 1, ...)

Arguments

x

Return by srsgd().

alpha

(optional) Picture transparency. Default is 1.

...

(optional) Other arguments passed to ggplot2::theme().

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

plot SRSGD result

Description

S3 method to plot output for SRSGD model result in srsgd().

Usage

## S3 method for class 'srsgd_result'
plot(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments.

Value

A ggplot2 layer

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print ecological detector

Description

S3 method to format output for ecological detector in geodetector().

Usage

## S3 method for class 'ecological_detector'
print(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print factor detector

Description

S3 method to format output for factor detector in geodetector().

Usage

## S3 method for class 'factor_detector'
print(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print GD result

Description

S3 method to format output for GD model from gd().

Usage

## S3 method for class 'gd_result'
print(x, ...)

Arguments

x

Return by gd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print GOZH result

Description

S3 method to format output for GOZH model from gozh().

Usage

## S3 method for class 'gozh_result'
print(x, ...)

Arguments

x

Return by gozh().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print IDSA result

Description

S3 method to format output for IDSA model from idsa().

Usage

## S3 method for class 'idsa_result'
print(x, ...)

Arguments

x

Return by idsa().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print interaction detector

Description

S3 method to format output for interaction detector in geodetector().

Usage

## S3 method for class 'interaction_detector'
print(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print LESH model interaction result

Description

S3 method to format output for LESH model interaction result in lesh().

Usage

## S3 method for class 'lesh_result'
print(x, ...)

Arguments

x

Return by lesh().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print OPGD result

Description

S3 method to format output for OPGD model from opgd().

Usage

## S3 method for class 'opgd_result'
print(x, ...)

Arguments

x

Return by opgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print RGD result

Description

S3 method to format output for RGD model from rgd().

Usage

## S3 method for class 'rgd_result'
print(x, ...)

Arguments

x

Return by rgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print RID result

Description

S3 method to format output for RID model from rid().

Usage

## S3 method for class 'rid_result'
print(x, ...)

Arguments

x

Return by rid().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print risk detector

Description

S3 method to format output for risk detector in geodetector().

Usage

## S3 method for class 'risk_detector'
print(x, ...)

Arguments

x

Return by geodetector().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print gozh sesu

Description

S3 method to format output for gozh sesu from sesu_gozh().

Usage

## S3 method for class 'sesu_gozh'
print(x, ...)

Arguments

x

Return by sesu_gozh().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print opgd sesu

Description

S3 method to format output for opgd sesu from sesu_opgd().

Usage

## S3 method for class 'sesu_opgd'
print(x, ...)

Arguments

x

Return by sesu_opgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print SPADE power of spatial and multilevel discretization determinant

Description

S3 method to format output for SPADE power of spatial and multilevel discretization determinant from spade().

Usage

## S3 method for class 'spade_result'
print(x, ...)

Arguments

x

Return by spade().

...

Other arguments.

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print spatial rough set-based ecological detector

Description

S3 method to format output for spatial rough set-based ecological detector in srsgd().

Usage

## S3 method for class 'srs_ecological_detector'
print(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print spatial rough set-based factor detector

Description

S3 method to format output for spatial rough set-based factor detector in srsgd().

Usage

## S3 method for class 'srs_factor_detector'
print(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print spatial rough set-based interaction detector

Description

S3 method to format output for spatial rough set-based interaction detector in srsgd().

Usage

## S3 method for class 'srs_interaction_detector'
print(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

print SRSGD result

Description

S3 method to format output for SRSGD model from srsgd().

Usage

## S3 method for class 'srsgd_result'
print(x, ...)

Arguments

x

Return by srsgd().

...

(optional) Other arguments passed to knitr::kable().

Value

Formatted string output

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

PSD of an interaction of explanatory variables (PSD-IEV)

Description

PSD of an interaction of explanatory variables (PSD-IEV)

Usage

psd_iev(discdata, spzone, wt)

Arguments

discdata

Observed data with discrete explanatory variables. A tibble or data.frame .

spzone

Fuzzy overlay spatial zones. Returned from st_fuzzyoverlay().

wt

Spatial weight matrix

Details

\phi = 1 - \frac{\sum_{i=1}^m \sum_{k=1}^{n_i}N_{i,k}\tau_{i,k}}{\sum_{i=1}^m N_i \tau_i}

Value

The Value of PSD-IEV

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')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
sim1 = dplyr::mutate(sim,dplyr::across(xa:xc,\(.x) sdsfun::discretize_vector(.x,5)))
sz = sdsfun::fuzzyoverlay(y ~ xa + xb + xc, data = sim1)
psd_iev(dplyr::select(sim1,xa:xc),sz,wt)

calculate power of spatial determinant(PSD) and the corresponding pseudo-p value

Description

Function for calculate power of spatial determinant q_s.

Usage

psd_pseudop(y, x, wt, cores = 1, seed = 123456789, permutations = 0)

Arguments

y

Variable Y, continuous numeric vector.

x

Covariable X, factor, character or discrete numeric.

wt

The spatial weight matrix.

cores

(optional) A positive integer(default is 1). If cores > 1, use parallel computation.

seed

(optional) Random seed number, default is 123456789.

permutations

(optional) The number of permutations for the PSD computation. Default is 0, which means no pseudo-p values are calculated.

Details

The power of spatial determinant formula is q_s = 1 - \frac{\sum_{h=1}^L N_h \Gamma_h}{N \Gamma}

Value

A tibble of power of spatial determinant and the corresponding pseudo-p value.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')),
                                  power = 2)
psd_pseudop(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

power of spatial determinant(PSD)

Description

Function for calculate power of spatial determinant q_s

Usage

psd_spade(y, x, wt)

Arguments

y

Variable Y, continuous numeric vector.

x

Covariable X, factor, character or discrete numeric.

wt

The spatial weight matrix.

Details

The power of spatial determinant formula is

q_s = 1 - \frac{\sum_{h=1}^L N_h \Gamma_h}{N \Gamma}

Value

A value of power of spatial determinant q_s.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')),
                                  power = 2)
psd_spade(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

power of spatial and multilevel discretization determinant(PSMD) and the corresponding pseudo-p value

Description

Function for calculate power of spatial and multilevel discretization determinant and the corresponding pseudo-p value.

Usage

psmd_pseudop(
  yobs,
  xobs,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  permutations = 0,
  ...
)

Arguments

yobs

Variable Y

xobs

The original undiscretized covariable X.

wt

The spatial weight matrix.

discnum

(optional) Number of multilevel discretization. Default will use 3:8.

discmethod

(optional) The discretization methods. Default will use quantile. If discmethod is set to robust, the function robust_disc() will be used. Conversely, if discmethod is set to rpart, the rpart_disc() function will be used. Others use sdsfun::discretize_vector(). Currently, only one discmethod can be used at a time.

cores

(optional) A positive integer(default is 1). If cores > 1, use parallel computation.

seed

(optional) Random seed number, default is 123456789.

permutations

(optional) The number of permutations for the PSD computation. Default is 0, which means no pseudo-p values are calculated.

...

(optional) Other arguments passed to sdsfun::discretize_vector(),robust_disc() or rpart_disc().

Details

The power of spatial and multilevel discretization determinant formula is PSMDQ_s = MEAN(Q_s)

Value

A tibble of power of spatial and multilevel discretization determinant and the corresponding pseudo-p value.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
psmd_pseudop(sim$y,sim$xa,wt)

power of spatial and multilevel discretization determinant(PSMD)

Description

Function for calculate power of spatial and multilevel discretization determinant PSMDQ_s.

Usage

psmd_spade(
  yobs,
  xobs,
  wt,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

yobs

Variable Y

xobs

The original continuous covariable X.

wt

The spatial weight matrix.

discnum

(optional) Number of multilevel discretizations. Default will use 3:8.

discmethod

(optional) The discretize methods. Default will use quantile. If discmethod is set to robust, the function robust_disc() will be used. Conversely, if discmethod is set to rpart, the rpart_disc() function will be used. Others use sdsfun::discretize_vector(). Currently, only one discmethod can be used at a time.

cores

(optional) A positive integer(default is 1). If cores > 1, use parallel computation.

seed

(optional) Random seed number, default is 123456789.

...

(optional) Other arguments passed to sdsfun::discretize_vector(),robust_disc() or rpart_disc().

Details

The power of spatial and multilevel discretization determinant formula is PSMDQ_s = MEAN(Q_s)

Value

A value of power of spatial and multilevel discretization determinant PSMDQ_s.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
wt = sdsfun::inverse_distance_swm(sf::st_as_sf(sim,coords = c('lo','la')))
psmd_spade(sim$y,sim$xa,wt)

robust geographical detector(RGD) model

Description

robust geographical detector(RGD) model

Usage

rgd(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

formula

A formula of RGD model.

data

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

discvar

Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.

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.

factor: robust power of determinant
opt_disc: optimal robust discrete results
allfactor: factor detection results corresponding to different number of robust discreteizations
alldisc: all robust discrete results

Note

Please set up python dependence and configure GDVERSE_PYTHON environment variable if you want to run rgd(). See vignette('rgdrid',package = 'gdverse') for more details.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Zhang, Z., Song, Y.*, & Wu, P., 2022. Robust geographical detector. International Journal of Applied Earth Observation and Geoinformation. 109, 102782. DOI: 10.1016/j.jag.2022.102782.

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
g = rgd(y ~ .,
        data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
        discnum = 3:6, cores = 1)
g

## End(Not run)

robust interaction detector(RID) model

Description

robust interaction detector(RID) model

Usage

rid(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  minsize = 1,
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1
)

Arguments

formula

A formula of RGD model.

data

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

discvar

Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.

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

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.

interaction: the result of RID model

Note

Please set up python dependence and configure GDVERSE_PYTHON environment variable if you want to run rid(). See vignette('rgdrid',package = 'gdverse') for more details.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Zhang, Z., Song, Y., Karunaratne, L., & Wu, P. (2024). Robust interaction detector: A case of road life expectancy analysis. Spatial Statistics, 59(100814), 100814. https://doi.org/10.1016/j.spasta.2024.100814

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
g = rid(y ~ .,
        data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
        discnum = 3:6, cores = 1)
g

## End(Not run)

risk detector

Description

Determine whether there is a significant difference between the attribute means of two sub regions.

Usage

risk_detector(y, x, alpha = 0.95)

Arguments

y

Variable Y, continuous numeric vector.

x

Covariate X, factor, character or discrete numeric.

alpha

(optional) Confidence level of the interval, default is 0.95.

Value

A tibble.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

risk_detector(y = 1:7,
              x = c('x',rep('y',3),rep('z',3)))

univariate discretization based on offline change point detection

Description

Determines discretization interval breaks using an optimization algorithm for variance-based change point detection.

Usage

robust_disc(formula, data, discnum, minsize = 1, cores = 1)

Arguments

formula

A formula of univariate discretization.

data

A data.frame or tibble of observation data.

discnum

A numeric vector of discretized classes of columns that need to be discretized.

minsize

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

cores

(optional) A positive integer(default is 1). If cores > 1, use python joblib package to parallel computation.

Value

A tibble.

Note

Please set up python dependence and configure GDVERSE_PYTHON environment variable if you want to run robust_disc(). See vignette('rgdrid',package = 'gdverse') for more details.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
robust_disc(y ~ xa, data = sim, discnum = 5)
robust_disc(y ~ .,
            data = dplyr::select(sim,-dplyr::any_of(c('lo','la'))),
            discnum = 5, cores = 3)

## End(Not run)

discretization of variables based on recursive partitioning

Description

discretization of variables based on recursive partitioning

Usage

rpart_disc(formula, data, ...)

Arguments

formula

A formula.

data

A data.frame or tibble of observation data.

...

(optional) Other arguments passed to rpart::rpart().

Value

A vector that being discretized.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

data('ndvi')
rpart_disc(NDVIchange ~ ., data = ndvi)

comparison of size effects of spatial units based on GOZH

Description

comparison of size effects of spatial units based on GOZH

Usage

sesu_gozh(
  formula,
  datalist,
  su,
  cores = 1,
  strategy = 2L,
  increase_rate = 0.05,
  alpha = 0.95,
  ...
)

Arguments

formula

A formula of comparison of size effects of spatial units.

datalist

A list of data.frame or tibble.

su

A vector of sizes of spatial units.

cores

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

strategy

(optional) Calculation strategies of Q statistics at different scales. Default is 2L, see details for more contents.

increase_rate

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

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to rpart_disc().

Details

When strategy is 1, use the same process as sesu_opgd().If not, all explanatory variables are used to generate a unique Q statistic corresponding to the data in the datalist based on rpart_disc() and gd(), and then loess_optscale()is used to determine the optimal analysis scale.

Value

A list.

sesu: a tibble representing size effects of spatial units
optsu: optimal spatial unit
strategy: the optimal analytical scale selection strategy
increase_rate: the critical increase rate of q value

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
sesu_gozh(fvc ~ .,
          datalist = list(fvc1000,fvc5000),
          su = c(1000,5000),
          cores = 6)

## End(Not run)

comparison of size effects of spatial units based on OPGD

Description

comparison of size effects of spatial units based on OPGD

Usage

sesu_opgd(
  formula,
  datalist,
  su,
  discvar,
  discnum = 3:8,
  discmethod = c("sd", "equal", "geometric", "quantile", "natural"),
  cores = 1,
  increase_rate = 0.05,
  alpha = 0.95,
  ...
)

Arguments

formula

A formula of comparison of size effects of spatial units.

datalist

A list of data.frame or tibble.

su

A vector of sizes of spatial units.

discvar

Name of continuous variable columns that need to be discretized.Noted that when formula has discvar, data must have these columns.

discnum

(optional) A vector of number of classes for discretization. Default is 3:8.

discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","geometric","quantile","natural") by invoking sdsfun.

cores

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

increase_rate

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

alpha

(optional) Specifies the size of confidence level. Default is 0.95.

...

(optional) Other arguments passed to gd_bestunidisc().

Details

Firstly, the OPGD model is executed for each data in the datalist (all significant Q statistic of each data are averaged to represent the spatial association strength under this spatial unit), and then the loess_optscale function is used to select the optimal spatial analysis scale.

Value

A list.

sesu: a tibble representing size effects of spatial units
optsu: optimal spatial unit
increase_rate: the critical increase rate of q value

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

## Not run: 
## The following code takes a long time to run:
library(tidyverse)
fvcpath = "https://github.com/SpatLyu/rdevdata/raw/main/FVC.tif"
fvc = terra::rast(paste0("/vsicurl/",fvcpath))
fvc1000 = fvc %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
fvc5000 = fvc %>%
  terra::aggregate(fact = 5) %>%
  terra::as.data.frame(na.rm = T) %>%
  as_tibble()
sesu_opgd(fvc ~ .,
          datalist = list(fvc1000,fvc5000),
          su = c(1000,5000),
          discvar = names(select(fvc5000,-c(fvc,lulc))),
          cores = 6)

## End(Not run)

Simulation data.

Description

Simulation data.

Usage

sim

Format

sim: A tibble with 80 rows and 6 variables, modified from IDSA package.

Author(s)

Yongze Song yongze.song@outlook.com

spatial association detector (SPADE) model

Description

spatial association detector (SPADE) model

Usage

spade(
  formula,
  data,
  wt = NULL,
  discvar = NULL,
  discnum = 3:8,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  permutations = 0,
  ...
)

Arguments

formula

A formula of spatial association detector (SPADE) model.

data

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

wt

(optional) The spatial weight matrix. When data is not an sf object, must provide wt.

discvar

(optional) Name of continuous variable columns that need to be discretized. Noted that when formula has discvar, data must have these columns. By default, all independent variables are used as discvar.

discnum

(optional) Number of multilevel discretization. Default will use 3:8.

discmethod

(optional) The discretization methods. Default all use quantile. Note that when using different discmethod for discvar, please ensure that the lengths of both are consistent. Noted that robust will use robust_disc(); rpart will use rpart_disc(); Others use sdsfun::discretize_vector().

cores

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

seed

(optional) Random number seed, default is 123456789.

permutations

(optional) The number of permutations for the PSD computation. Default is 0, which means no pseudo-p values are calculated.

...

(optional) Other arguments passed to sdsfun::discretize_vector(),robust_disc() or rpart_disc().

Value

A list.

factor: the result of SPADE model

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Xuezhi Cang & Wei Luo (2018) Spatial association detector (SPADE),International Journal of Geographical Information Science, 32:10, 2055-2075, DOI: 10.1080/13658816.2018.1476693

Examples

data('sim')
sim1 = sf::st_as_sf(sim,coords = c('lo','la'))
g = spade(y ~ ., data = sim1)
g

shap power of determinants

Description

Function for calculate shap power of determinants SPD.

Usage

spd_lesh(formula, data, cores = 1, ...)

Arguments

formula

A formula of calculate shap power of determinants.

data

A data.frame or tibble of observation data.

cores

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

...

(optional) Other arguments passed to rpart_disc().

Details

The power of shap power of determinants formula is

\theta_{x_j} \left( S \right) = \sum\limits_{s \in M \setminus \{x_j\}} \frac{|S|! \left(|M| - |S| - 1\right)!}{|M|!}\left(v \left(S \cup \left\{x_j\right\} \right) - v\left(S\right)\right).

shap power of determinants (SPD) is the contribution of variable x_j to the power of determinants.

Value

A tibble with variable and its corresponding SPD value.

Note

The shap power of determinants (SPD) 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

Examples

data('ndvi')
g = spd_lesh(NDVIchange ~ ., data = ndvi)
g

spatial rough set-based ecological detector

Description

spatial rough set-based ecological detector

Usage

srs_ecological_detector(y, x1, x2, wt, alpha = 0.95)

Arguments

y

Dependent variable, factor, character or discrete numeric.

x1

Covariate X_1, factor, character or discrete numeric.

x2

Covariate X_2, factor, character or discrete numeric.

wt

Spatial adjacency matrix.

alpha

(optional) Confidence level of the interval,default is 0.95.

Value

A list.

T-statistic: the result of T statistic for spatial rough set-based ecological detector
P-value: the result of P value for spatial rough set-based ecological detector
Ecological: does one spatial feature X_1 play a more important role than X_2

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Bai, H., Li, D., Ge, Y., Wang, J., & Cao, F. (2022). Spatial rough set-based geographical detectors for nominal target variables. Information Sciences, 586, 525–539. https://doi.org/10.1016/j.ins.2021.12.019

Examples

data('srs_table')
data('srs_wt')
srs_ecological_detector(srs_table$d,srs_table$a1,srs_table$a2,srs_wt)

spatial rough set-based factor detector

Description

spatial rough set-based factor detector

Usage

srs_factor_detector(y, x, wt)

Arguments

y

Variable Y, factor, character or discrete numeric.

x

Covariate X, factor, character or discrete numeric.

wt

Spatial adjacency matrix.

Value

A list.

PD: the average local explanatory power
SE_PD: the degree of spatial heterogeneity of the local explanatory power

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

data('srs_table')
data('srs_wt')
srs_factor_detector(srs_table$d,srs_table$a1,srs_wt)

spatial rough set-based geographical detector

Description

spatial rough set-based geographical detector

Usage

srs_geodetector(formula, data, wt = NULL, type = "factor", alpha = 0.95)

Arguments

formula

A formula of spatial rough set-based geographical detector model.

data

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

wt

Spatial adjacency matrix. If data is a sf polygon object, the queen adjacency matrix is used when no wt object is provided. In other cases, you must provide a wt object.

type

(optional) The type of geographical detector, which must be one of factor(default), interaction and ecological.

alpha

(optional) Specifies the size of the alpha (confidence level). Default is 0.95.

Value

A list.

factor: the result of spatial rough set-based factor detector
interaction: the result of spatial rough set-based interaction detector
ecological: the result of spatial rough set-based ecological detector

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

Examples

data('srs_table')
data('srs_wt')
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table, wt = srs_wt)
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table,
                wt = srs_wt, type = 'interaction')
srs_geodetector(d ~ a1 + a2 + a3, data = srs_table,
                wt = srs_wt, type = 'ecological')

spatial rough set-based interaction detector

Description

spatial rough set-based interaction detector

Usage

srs_interaction_detector(y, x1, x2, wt)

Arguments

y

Dependent variable, factor, character or discrete numeric.

x1

Covariate X_1, factor, character or discrete numeric.

x2

Covariate X_2, factor, character or discrete numeric.

wt

Spatial adjacency matrix.

Value

A list.

Variable1 PD: the average local explanatory power for variable1
Variable2 PD: the average local explanatory power for variable2
Variable1 and Variable2 interact PD: the average local explanatory power for variable1 and variable2 interact
Variable1 SE_PD: the degree of spatial heterogeneity of the local explanatory power for variable1
Variable2 SE_PD: the degree of spatial heterogeneity of the local explanatory power for variable2
Variable1 and Variable2 SE_PD: the degree of spatial heterogeneity of the local explanatory power for variable1 and variable2 interact
Interaction: the interact result type

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

data('srs_table')
data('srs_wt')
srs_interaction_detector(srs_table$d,srs_table$a1,srs_table$a2,srs_wt)

example of spatial information system table

Description

example of spatial information system table

Usage

srs_table

Format

srs_table: A tibble with 11 rows and 5 variables(one ID column).

example of spatial information system spatial adjacency matrix

Description

example of spatial information system spatial adjacency matrix

Usage

srs_wt

Format

srs_wt: A matrix with 11rows and 11cols.

spatial rough set-based geographical detector(SRSGD) model

Description

spatial rough set-based geographical detector(SRSGD) model

Usage

srsgd(formula, data, wt = NULL, type = "factor", alpha = 0.95)

Arguments

formula

A formula of spatial rough set-based geographical detector model.

data

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

wt

Spatial adjacency matrix. If data is a sf polygon object, the queen adjacency matrix is used when no wt object is provided. In other cases, you must provide a wt object.

type

(optional) The type of geographical detector, which must be one of factor(default), interaction and ecological.

alpha

(optional) Specifies the size of the alpha (confidence level). Default is 0.95.

Value

A list.

factor: the result of spatial rough set-based factor detector
interaction: the result of spatial rough set-based interaction detector
ecological: the result of spatial rough set-based ecological detector

Note

The Spatial Rough Set-based Geographical Detector Model (SRSGD) conducts spatial hierarchical heterogeneity analysis utilizing a geographical detector for data where the dependent variable is discrete. Given the complementary relationship between SRSGD and the native version of geographical detector, I strive to maintain consistency with gd() function when establishing srsgd() function. This implies that all input variable data in srsgd must be discretized prior to use.

Author(s)

Wenbo Lv lyu.geosocial@gmail.com

References

Examples

data('srs_table')
data('srs_wt')
srsgd(d ~ a1 + a2 + a3, data = srs_table, wt = srs_wt,
      type = c('factor','interaction','ecological'))

assign values by weight

Description

assign values by weight

Usage

weight_assign(x, w, list = FALSE)

Arguments

x

A numeric value

w

A weight vector

list

(optional) Return list or not. if list is TRUE, return a list, otherwise return a vector. Default is FALSE.

Value

A numeric Vector.

Examples

weight_assign(0.875,1:3)