Package 'gdverse' reference manual

Title:	Analysis of Spatial Stratified Heterogeneity
Description:	Analyzing spatial factors and exploring spatial associations based on the concept of spatial stratified heterogeneity, and also takes into account local spatial dependencies, spatial interpretability, potential spatial interactions, and robust spatial stratification. Additionally, it supports geographical detector models established in academic literature.
Authors:	Wenbo Lv [aut, cre, cph] , Yangyang Lei [aut] , Fangmei Liu [aut] , Wufan Zhao [aut] , Yongze Song [aut] , Jianwu Yan [aut]
Maintainer:	Wenbo Lv <[email protected]>
License:	GPL-3
Version:	1.2
Built:	2024-11-08 10:26:25 UTC
Source:	CRAN

convert all discretized vectors to integer

Description

convert all discretized vectors to integer

Usage

all2int(x)
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])

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 = 3:22,
  discmethod = "quantile",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  return_disc = TRUE,
  seed = 123456789,
  ...
)
cpsd_disc(
  formula,
  data,
  wt,
  discnum = 3:22,
  discmethod = "quantile",
  strategy = 2L,
  increase_rate = 0.05,
  cores = 1,
  return_disc = TRUE,
  seed = 123456789,
  ...
)

Arguments

`formula`	A formula of optimal spatial data discretization.
`data`	A data.frame or tibble of observation data.
`wt`	The spatial weight matrix.
`discnum`	(optional) A vector of number of classes for discretization. Default is `3:22`.
`discmethod`	(optional) The discretization methods. Default all use `quantile`. Noted that `robust` will use `robust_disc()`; `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) 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.
`return_disc`	(optional) Whether or not return discretized result used the optimal parameter. Default is `TRUE`.
`seed`	(optional) Random seed number, default is `123456789`.Setting random seed is useful when the sample size is greater than `3000`(the default value for `largeN`) and the data is discretized by sampling `⁠10%⁠`(the default value for `samp_prop` in `st_unidisc()`).
`...`	(optional) Other arguments passed to `st_unidisc()`,`robust_disc()` or `rpart_disc()`.

Value

A list with the optimal parameter in the provided parameter combination with k, method and disc(when return_disc is TRUE).

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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la)
cpsd_disc(y ~ xa + xb + xc,
          data = sim,
          wt = wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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)
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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la)
xa = sim$xa
xa_disc = sdsfun::discretize_vector(xa,5)
cpsd_spade(sim$y,xa,xa_disc,wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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)
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 [email protected]

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)))

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)))

measure information loss by information entropy

Description

Function for measure information loss by shannon information entropy.

Usage

F_informationloss(xvar, xdisc)
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 [email protected]

Examples

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

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

Arguments

`y`	Variable Y, continuous numeric vector.
`x`	Covariate X, `factor`, `character` or `discrete numeric`.

Value

A list.

Q-statistic: the q statistic for factor detector
P-value: the p value for factor detector

Author(s)

Wenbo Lv [email protected]

Examples

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

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

native geographical detector(GD) model

Description

Function for native geographical detector model.

Usage

gd(formula, data, type = "factor", alpha = 0.95)
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 [email protected]

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

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

best univariate discretization based on geodetector q-statistic

Description

Function for determining the best univariate discretization based on geodetector q-statistic.

Usage

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

Arguments

`formula`	A formula of best univariate discretization.
`data`	A `data.frame` or `tibble` of observation data.
`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.
`return_disc`	(optional) Whether or not return discretized result used the optimal parameter. Default is `TRUE`.
`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
disc: optimal discretization results

Author(s)

Wenbo Lv [email protected]

Examples

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

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

generate subsets of a set

Description

generate subsets of a set

Usage

generate_subsets(set, empty = TRUE, self = TRUE)
generate_subsets(set, empty = TRUE, self = TRUE)

Arguments

`set`	A vector.
`empty`	(optional) When `empty` is `TRUE`, the generated subset includes the empty set, otherwise the empty set is removed. Default is `TRUE`.
`self`	(optional) When `self` is `TRUE`, the resulting subset includes the set itself, otherwise the set itself is removed. Default is `TRUE`.

Value

A list.

Examples

generate_subsets(letters[1:3])
generate_subsets(letters[1:3],empty = FALSE)
generate_subsets(letters[1:3],self = FALSE)
generate_subsets(letters[1:3],empty = FALSE,self = FALSE)

generate_subsets(letters[1:3])
generate_subsets(letters[1:3],empty = FALSE)
generate_subsets(letters[1:3],self = FALSE)
generate_subsets(letters[1:3],empty = FALSE,self = FALSE)

geographical detector

Description

geographical detector

Usage

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

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

Author(s)

Wenbo Lv [email protected]

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)

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

Function for geographically optimal zones-based heterogeneity(GOZH) model

Usage

gozh(formula, data, cores = 1, type = "factor", alpha = 0.95, ...)
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 [email protected]

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

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, ...)
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) 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.
`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 [email protected]

References

Examples

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

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

interactive detector for spatial associations(IDSA) model

Description

Function for interactive detector for spatial associations 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,
  ...
)
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 `robust` will use `robust_disc()`; `rpart` will use `rpart_disc()`; Others use `sdsfun::discretize_vector()`.
`overlay`	(optional) Spatial overlay method. One of `and`, `or`, `intersection`. Default is `and`.
`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 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 [email protected]

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

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)
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 [email protected]

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)))

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)))

calculate inverse distance weight

Description

Function for calculate inverse distance weight.

Usage

inverse_distance_weight(locx, locy, power = 1, is_arc = FALSE)
inverse_distance_weight(locx, locy, power = 1, is_arc = FALSE)

Arguments

`locx`	The x axis location.
`locy`	The y axis location.
`power`	(optional) Default is 1. Set to 2 for gravity weights.
`is_arc`	(optional) `FALSE` (default) or `TRUE`, whether to compute arc distance.

Details

The inverse distance weight formula is $w_{ij} = 1 / d_{ij}^\alpha$

Value

A inverse distance weight matrices with class of matrix.

Author(s)

Wenbo Lv [email protected]

Examples

x = 1:10
y = 1:10
inverse_distance_weight(x,y)
inverse_distance_weight(x,y,is_arc = TRUE)

x = 1:10
y = 1:10
inverse_distance_weight(x,y)
inverse_distance_weight(x,y,is_arc = TRUE)

interpretable stratified power(ISP) model

Description

Function for interpretable stratified power model.

Usage

isp(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  minsize = 1,
  cores = 1,
  alpha = 0.95
)
isp(
  formula,
  data,
  discvar = NULL,
  discnum = 3:8,
  overlay = "and",
  strategy = 2L,
  increase_rate = 0.05,
  minsize = 1,
  cores = 1,
  alpha = 0.95
)

Arguments

`formula`	A formula of ISP model.
`data`	A `data.frame`, `tibble` or `sf` object of observation data.
`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`	A numeric vector of discretized classes of columns that need to be discretized. Default all `discvar` use `3:8`.
`overlay`	(optional) Spatial overlay method. One of `and`, `or`, `intersection`. Default is `and`.
`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%⁠`.
`minsize`	(optional) The min size of each discretization group. Default all use `1`.
`cores`	(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.
`alpha`	(optional) Specifies the size of confidence level. Default is `0.95`.

Value

A list.

factor: factor detect results
interaction: interaction detect results
optdisc: independent variable optimal spatial discretization
risk1: whether values of the response variable between a pair of overlay zones are significantly different
risk2: risk detection result of the input data
rpd: robust power of determinants
spd: shap power of determinants
determination: determination of the optimal interaction of variables
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 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 [email protected]

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
sim1 = dplyr::select(sim,-dplyr::any_of(c('lo','la')))
g = isp(y ~ ., data = sim1, discnum = 3:8, cores = 6)
g

## End(Not run)
## Not run: 
## The following code needs to configure the Python environment to run:
data('sim')
sim1 = dplyr::select(sim,-dplyr::any_of(c('lo','la')))
g = isp(y ~ ., data = sim1, discnum = 3:8, cores = 6)
g

## End(Not run)

locally explained heterogeneity(LESH) model

Description

Function for locally explained heterogeneity model.

Usage

lesh(formula, data, cores = 1, ...)
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 [email protected]

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

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)
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 [email protected]

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)
## 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
ndvi

Format

ndvi: A tibble with 713 rows and 7 variables

Author(s)

Yongze Song [email protected]

NTDs data

Description

The data were obtained by preprocessing use sf and tidyverse.

Usage

NTDs
NTDs

Format

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

optimal parameters-based geographical detector(OPGD) model

Description

Function for 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,
  ...
)
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_bestunidisc()`. 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 [email protected]

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)

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")
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 = inverse_distance_weight(sim$lo,sim$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)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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, ...)
## 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 [email protected]

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

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

Value

A ggplot2 layer.

Author(s)

Wenbo Lv [email protected]

plot GD result

Description

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

Usage

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

Arguments

`x`	Return by `gd()`.
`...`	(optional) Other arguments passed to `patchwork::wrap_plots()`.

Value

A ggplot2 layer

Author(s)

Wenbo Lv [email protected]

plot GOZH result

Description

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

Usage

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

Arguments

`x`	Return by `gozh()`.
`...`	(optional) Other arguments passed to `patchwork::wrap_plots()`.

Value

A ggplot2 layer

Author(s)

Wenbo Lv [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

plot ISP result

Description

S3 method to plot output for ISP result in isp().

Usage

## S3 method for class 'isp_result'
plot(x, low_color = "#6600CC", high_color = "#FFCC33", ...)
## S3 method for class 'isp_result'
plot(x, low_color = "#6600CC", high_color = "#FFCC33", ...)

Arguments

`x`	Return by `isp()`.
`low_color`	(optional) The low color of the color gradient, default is `⁠#6600CC⁠`.
`high_color`	(optional) The high color of the color gradient, default is `⁠#FFCC33⁠`.
`...`	(optional) Other arguments passed to `ggplot2::theme()`.

Value

A ggplot2 layer

Author(s)

Wenbo Lv [email protected]

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,
  ...
)
## 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 [email protected]

plot OPGD result

Description

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

Usage

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

Arguments

`x`	Return by `opgd()`.
`...`	(optional) Other arguments passed to `patchwork::wrap_plots()`.

Value

A ggplot2 layer

Author(s)

Wenbo Lv [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

plot risk detector

Description

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

Usage

## S3 method for class 'risk_detector'
plot(x, ...)
## 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 [email protected]

plot gozh sesu

Description

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

Usage

## S3 method for class 'sesu_gozh'
plot(x, ...)
## 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 [email protected]

plot opgd sesu

Description

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

Usage

## S3 method for class 'sesu_opgd'
plot(x, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

plot SRSGD result

Description

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

Usage

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

Arguments

`x`	Return by `srsgd()`.
`...`	(optional) Other arguments passed to `patchwork::wrap_plots()`.

Value

A ggplot2 layer

Author(s)

Wenbo Lv [email protected]

print ecological detector

Description

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

Usage

## S3 method for class 'ecological_detector'
print(x, ...)
## 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 [email protected]

print factor detector

Description

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

Usage

## S3 method for class 'factor_detector'
print(x, ...)
## 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 [email protected]

print GD result

Description

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

Usage

## S3 method for class 'gd_result'
print(x, ...)
## 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 [email protected]

print GOZH result

Description

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

Usage

## S3 method for class 'gozh_result'
print(x, ...)
## 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 [email protected]

print IDSA result

Description

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

Usage

## S3 method for class 'idsa_result'
print(x, ...)
## 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 [email protected]

print interaction detector

Description

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

Usage

## S3 method for class 'interaction_detector'
print(x, ...)
## 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 [email protected]

print ISP result

Description

S3 method to format output for ISP model from isp().

Usage

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

Arguments

`x`	Return by `isp()`.
`...`	(optional) Other arguments passed to `knitr::kable()`.

Value

Formatted string output

Author(s)

Wenbo Lv [email protected]

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, ...)
## 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 [email protected]

print OPGD result

Description

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

Usage

## S3 method for class 'opgd_result'
print(x, ...)
## 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 [email protected]

print RGD result

Description

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

Usage

## S3 method for class 'rgd_result'
print(x, ...)
## 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 [email protected]

print RID result

Description

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

Usage

## S3 method for class 'rid_result'
print(x, ...)
## 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 [email protected]

print risk detector

Description

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

Usage

## S3 method for class 'risk_detector'
print(x, ...)
## 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 [email protected]

print gozh sesu

Description

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

Usage

## S3 method for class 'sesu_gozh'
print(x, ...)
## 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 [email protected]

print opgd sesu

Description

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

Usage

## S3 method for class 'sesu_opgd'
print(x, ...)
## 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 [email protected]

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

Arguments

`x`	Return by `spade()`.
`...`	Other arguments.

Value

Formatted string output

Author(s)

Wenbo Lv [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

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, ...)
## 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 [email protected]

print SRSGD result

Description

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

Usage

## S3 method for class 'srsgd_result'
print(x, ...)
## 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 [email protected]

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)
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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$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)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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)
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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la,power = 2)
psd_pseudop(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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)
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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la,power = 2)
psd_spade(sim$y,sdsfun::discretize_vector(sim$xa,5),wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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 = 3:22,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  permutations = 0,
  ...
)
psmd_pseudop(
  yobs,
  xobs,
  wt,
  discnum = 3:22,
  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 `3:22`.
`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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la)
psmd_pseudop(sim$y,sim$xa,wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$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 = 3:22,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  ...
)
psmd_spade(
  yobs,
  xobs,
  wt,
  discnum = 3:22,
  discmethod = "quantile",
  cores = 1,
  seed = 123456789,
  ...
)

Arguments

`yobs`	Variable Y
`xobs`	The original undiscretized covariable X.
`wt`	The spatial weight matrix.
`discnum`	(optional) Number of multilevel discretization. Default will use `3:22`.
`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`.
`...`	(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 [email protected]

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 = inverse_distance_weight(sim$lo,sim$la)
psmd_spade(sim$y,sim$xa,wt)

data('sim')
wt = inverse_distance_weight(sim$lo,sim$la)
psmd_spade(sim$y,sim$xa,wt)

robust geographical detector(RGD) model

Description

Function for robust geographical detector(RGD) model.

Usage

rgd(formula, data, discvar = NULL, discnum = 3:8, minsize = 1, cores = 1)
rgd(formula, data, discvar = NULL, discnum = 3:8, minsize = 1, 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`.
`cores`	(optional) Positive integer (default is 1). When cores are greater than 1, use multi-core parallel computing.

Value

A list.

factor: the result of RGD model
disc: 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 [email protected]

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 = 6)
g

## End(Not run)
## 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 = 6)
g

## End(Not run)

robust interaction detector(RID) model

Description

Function for robust interaction detector(RID) model.

Usage

rid(formula, data, discvar = NULL, discnum = 10, minsize = 1, cores = 1)
rid(formula, data, discvar = NULL, discnum = 10, minsize = 1, cores = 1)

Arguments

`formula`	A formula of RID 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 for the number of discretized classes of columns that need to be discretized. Default all `discvar` use `10`.
`minsize`	(optional) The min size of each discretization group. Default all use `1`.
`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 [email protected]

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 = 4, cores = 6)
g

## End(Not run)
## 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 = 4, cores = 6)
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)
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. contains different combinations of covariate X level and student t-test statistics, degrees of freedom, p-values, and whether has risk (Yes or No).

Author(s)

Wenbo Lv [email protected]

Examples

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

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)
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 of discretized columns which need to be discretized.

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 [email protected]

Examples

## Not run: 
## The following code needs to configure the Python environment to run:
data('ndvi')
robust_disc(NDVIchange ~ GDP,data = ndvi,discnum = 5)
robust_disc(NDVIchange ~ .,
            data = dplyr::select(ndvi,-c(Climatezone,Mining)),
            discnum = 10,cores = 6)

## End(Not run)
## Not run: 
## The following code needs to configure the Python environment to run:
data('ndvi')
robust_disc(NDVIchange ~ GDP,data = ndvi,discnum = 5)
robust_disc(NDVIchange ~ .,
            data = dplyr::select(ndvi,-c(Climatezone,Mining)),
            discnum = 10,cores = 6)

## End(Not run)

discretization of variables based on recursive partitioning

Description

discretization of variables based on recursive partitioning

Usage

rpart_disc(formula, data, ...)
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 [email protected]

References

Examples

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

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

comparison of size effects of spatial units based on GOZH

Description

Function for comparison of size effects of spatial units in spatial heterogeneity analysis based on geographically optimal zones-based heterogeneity(GOZH) model.

Usage

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

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)
## 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

Function for comparison of size effects of spatial units in spatial heterogeneity analysis based on optimal parameters geographical detector(OPGD) model.

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,
  ...
)
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 [email protected]

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)
## 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)

randomly shuffling vector

Description

randomly shuffling vector

Usage

shuffle_vector(x, shuffle_rate, seed = 123456789)
shuffle_vector(x, shuffle_rate, seed = 123456789)

Arguments

`x`	A vector.
`shuffle_rate`	The shuffling rate.
`seed`	(optional) Random seed number. Default is `123456789`.

Value

A shuffled vector.

Examples

shuffle_vector(1:100,0.15)

shuffle_vector(1:100,0.15)

Simulation data.

Description

Simulation data.

Usage

sim
sim

Format

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

Author(s)

Yongze Song [email protected]

spatial association detector (SPADE) model

Description

Function for spatial association detector (SPADE) model.

Usage

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

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

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

SHAP power of determinants (SPD)

Description

Function for calculate SHAP power of determinants $SPD$ .

Usage

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

Arguments

`formula`	A formula of calculate SHAP power of determinants $SPD$ .
`data`	A data.frame or tibble of observation data.
`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.
`...`	(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 [email protected]

References

Examples

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

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)
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 [email protected]

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)

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)
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 [email protected]

References

Examples

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

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)
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 of tibble with the corresponding result under different detector types.

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 [email protected]

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')

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)
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 [email protected]

References

Examples

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

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
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
srs_wt

Format

srs_wt: A matrix with 11rows and 11cols.

spatial rough set-based geographical detector(SRSGD) model

Description

Function for spatial rough set-based geographical detector model.

Usage

srsgd(formula, data, wt = NULL, type = "factor", alpha = 0.95)
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 [email protected]

References

Examples

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

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)
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)

weight_assign(0.875,1:3)

Package 'gdverse'

Help Index

convert all discretized vectors to integer

Description

Usage

Arguments

Value

Examples

optimal spatial data discretization based on SPADE q-statistics

Description

Usage

Arguments

Value

Note

Author(s)

References

Examples

compensated power of spatial determinant(CPSD)

Description

Usage

Arguments

Details

Value

Author(s)

References

Examples

ecological detector

Description

Usage

Arguments

Value

Author(s)

Examples

measure information loss by information entropy

Description

Usage

Arguments

Details

Value

Author(s)

Examples

factor detector

Description

Usage

Arguments

Value

Author(s)

Examples

native geographical detector(GD) model

Description

Usage

Arguments

Value

Author(s)

References

Examples

best univariate discretization based on geodetector q-statistic

Description

Usage

Arguments

Value

Author(s)

Examples

generate subsets of a set

Description

Usage

Arguments

Value

Examples

geographical detector

Description

Usage

Arguments

Value

Note

Author(s)

Examples

geographically optimal zones-based heterogeneity(GOZH) model

Description

Usage