Package 'gdverse'

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] , Yongze Song [aut] , Wufan Zhao [aut] , Jianwu Yan [aut]
Maintainer: Wenbo Lv <[email protected]>
License: GPL-3
Version: 1.0-2
Built: 2024-09-19 07:25:14 UTC
Source: https://github.com/ausgis/gdverse

Help Index

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 = 3:22,
  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 st_unidisc(). You can try unidisc_methods().
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)

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

Qs=qsqsinforkep=1−∑h=1LNhΓkdepNΓtotaldep1−∑h=1LNhΓhindNΓtotalindQ_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$xa
xa_disc = st_unidisc(xa,5)
cpsd_spade(sim$y,xa,xa_disc,wt)

ecological detector

Description

Compare the effects of two factors X1X_1 and X2X_2 on the spatial distribution of the attribute YY.

Usage

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

Arguments

y

Dependent variable, continuous numeric vector.
x1

Covariate X1X_1, factor, character or discrete numeric.
x2

Covariate X2X_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 X1X_1 and X2X_2 on the spatial distribution of the attribute YY

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)))

enhanced stratified power(ESP) model

Description

Function for enhanced stratified power model.

Usage

esp(
  formula,
  data,
  wt = NULL,
  discvar = NULL,
  discnum = 10,
  overlaymethod = "and",
  minsize = 1,
  cores = 1,
  alpha = 0.95
)

Arguments

formula

A formula of ESP 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

A numeric vector of discretized classes of columns that need to be discretized. Default all discvar use 10.
overlaymethod

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

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

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

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

Value

A list with ESP model result.

factor

results of ESP model factor detection

interaction

results of ESP model interaction detection

risk1

whether values of the response variable between a pair of overlay zones are significantly different

risk2

risk detection result of the input data

psd

power of spatial 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 = sf::st_as_sf(sim,coords = c('lo','la'))
g = esp(y ~ ., data = sim1, discnum = 5)

## End(Not run)

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=−∑i=1Np(i)log⁡2p(i)−(−∑h=1Lp(h)log⁡2p(h))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)))

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)

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)))

native geographical detector(GD) model

Description

Function for native geographical detector 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 of the GD model result.

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

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 = 3:22,
  discmethod = c("sd", "equal", "pretty", "quantile", "fisher", "headtails", "maximum",
    "box"),
  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 3:22.
discmethod

(optional) A vector of methods for discretization,default is using c("sd","equal","pretty","quantile","fisher","headtails","maximum","box")in gdverse.
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().

Value

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

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,
               discvar = paste0('x',letters[1:3]),
               discnum = 3:6)

generate subsets of a set

Description

generate subsets of a set

Usage

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

Arguments

set

A vector including the empty set and the set itself. Default is TRUE.
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 with the subsets

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)

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 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)

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, ...)

Arguments

formula

A formula of GOZH model.
data

A data.frame, tibble or sf object 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 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 of GOZH model result.

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

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) 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

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_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 = 3:22,
  discmethod = "quantile",
  overlaymethod = "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 3:22.
discmethod

(optional) The discretization methods. Default all use quantile. Noted that robust will use robust_disc(); rpart will use rpart_disc(); Others use st_unidisc(). You can try unidisc_methods() to see supported methods in st_unidisc().
overlaymethod

(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) 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.
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 with PID values tibble under different spatial overlays and performance evaluation indicators.

interaction

the interaction result of IDSA model

risk1

whether values of the response variable between a pair of overlay zones are significantly different

risk2

risk detection result of the input data

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 2n−12^n-1 calculations when has nn 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

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 X1X_1, factor, character or discrete numeric.
x2

Covariate X2X_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)))

calculate inverse distance weight

Description

Function for calculate inverse distance weight.

Usage

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 wij=1/dijαw_{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)

locally explained heterogeneity(LESH) model

Description

Function for locally explained heterogeneity 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) 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().

Value

A list of LESH model result.

interaction

the interaction result of LESH model

spd_lesh

a tibble of the SHAP power of determinants

Note

The LESH model requires at least 2n−12^n-1 calculations when has nn 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

determine optimal spatial data discretization for individual variables

Description

Function for determining optimal spatial data discretization for individual variables based on locally estimated scatterplot smoothing (LOESS) model.

Usage

loess_optdiscnum(qvec, discnumvec, increase_rate = 0.05)

Arguments

qvec

A numeric vector of q statistics.
discnumvec

A numeric vector of break numbers corresponding to qvec.
increase_rate

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

Value

A optimal number of spatial data discretization.

Note

When increase_rate is not satisfied by the calculation, increase_rate*0.1 is used first. At this time, if increase_rate*0.1 is not satisfied again, the discrete number corresponding to the highest Q-statistic is selected as a return.

Note that gdverse sorts discnumvec from smallest to largest and keeps qvec in one-to-one correspondence with discnumvec.

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')
3:10 %>%
  purrr::map_dbl(\(.k) st_unidisc(sim$xa,.k) %>%
               factor_detector(sim$y,.) %>%
               {.[[1]]}) %>%
 loess_optdiscnum(3:10)

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 optimal number of spatial scale

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$fvc,
                         st_unidisc(fvc1000$premax,10,'quantile'))[[1]]
qv5000 = factor_detector(fvc5000$fvc,
                         st_unidisc(fvc5000$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 [email protected]

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.

optimal parameters-based geographical detector(OPGD) model

Description

Function for optimal parameters-based geographical detector(OPGD) model.

Usage

opgd(
  formula,
  data,
  discvar = NULL,
  discnum = 3:22,
  discmethod = c("sd", "equal", "pretty", "quantile", "fisher", "headtails", "maximum",
    "box"),
  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 3:22.
discmethod

(optional) A vector of methods for discretization, default is using c("sd","equal","pretty","quantile","fisher","headtails","maximum","box").
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 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 of the OPGD model result.

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)

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

QIDSA=θrϕQ_{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) st_unidisc(.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 [email protected]

plot ESP result

Description

S3 method to plot output for ESP result in esp().

Usage

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

Arguments

x

Return by esp().
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 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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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 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 [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, ...)

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, ...)

Arguments

x

Return by rgd().
...

(optional) Other arguments passed to patchwork::wrap_plots().

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

Arguments

x

Return by geodetector().
...

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

Value

Formatted string output

Author(s)

Wenbo Lv [email protected]

print ESP result

Description

S3 method to format output for ESP model from esp().

Usage

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

Arguments

x

Return by esp().
...

(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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

Arguments

x

Return by geodetector().
...

(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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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, ...)

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)

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

ϕ=1−∑i=1m∑k=1niNi,kτi,k∑i=1mNiτi\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) st_unidisc(.x,5)))
sz = st_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 qsq_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 qs=1−∑h=1LNhΓhNΓq_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$y,st_unidisc(sim$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

qs=1−∑h=1LNhΓhNΓq_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$y,st_unidisc(sim$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,
  ...
)

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 st_unidisc(). 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 st_unidisc(),robust_disc() or rpart_disc().

Details

The power of spatial and multilevel discretization determinant formula is PSMDQs=MEAN(Qs)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)

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,
  ...
)

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 st_unidisc(). 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 st_unidisc(),robust_disc() or rpart_disc().

Details

The power of spatial and multilevel discretization determinant formula is PSMDQs=MEAN(Qs)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)

rescale continuous vector to specified minimum and maximum

Description

rescale continuous vector to specified minimum and maximum

Usage

rescale_vector(x, to_left = 0, to_right = 1)

Arguments

x

A continuous numeric vector.
to_left

(optional) Specified minimum. Default is 0.
to_right

(optional) Specified maximum. Default is 1.

Value

A continuous vector.

Examples

rescale_vector(c(-5,1,5),0.01,0.99)

robust geographical detector(RGD) model

Description

Function for robust geographical detector(RGD) model.

Usage

rgd(
  formula,
  data,
  discvar = NULL,
  discnum = 10,
  minsize = 1,
  cores = 1,
  type = "factor",
  alpha = 0.95
)

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 10.
minsize

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

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

Value

A list of the RGD model result.

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

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('ndvi')
g = rgd(NDVIchange ~ ., data = ndvi, discvar = names(ndvi)[-1:-3],
        cores = 6, type =c('factor','interaction'))

## End(Not run)

robust interaction detector(RID) model

Description

Function for robust interaction detector(RID) model.

Usage

rid(
  formula,
  data,
  discvar = NULL,
  discnum = 10,
  overlaymethod = "and",
  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.
overlaymethod

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

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

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

Value

A list of the RID model result.

interaction

the result of RID model

Note

For bivariate spatial interactions, use the RGD function and specify the type parameter as interaction.

The RID model requires at least 2n−12^n-1 calculations when has nn 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.

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 = sim %>% dplyr::select(-dplyr::any_of(c('lo','la'))),
        discvar = c("xa","xb","xc"), 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)

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)))

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 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)

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 [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')
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,
  ...
)

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) 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.
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 with SESU GOZH results.

sesu

a tibble representing size effects of spatial units

optsu

optimal spatial unit

strategy

the optimal analytical scale selection strategy

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.

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

## 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 = NULL,
  discmethod = NULL,
  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 3:22.
discmethod

(optional) A vector of methods for discretization,default is used c("sd","equal","pretty","quantile","fisher","headtails","maximum","box")in gdverse.
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.
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 connection strength under this spatial unit), and then the loess_optscale function is used to select the optimal spatial analysis scale.

Value

A list with SESU OPGD results

sesu

a tibble representing size effects of spatial units

optsu

optimal spatial unit

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

## 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)

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)

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 [email protected]

spatial association detector (SPADE) model

Description

Function for spatial association detector (SPADE) model.

Usage

spade(
  formula,
  data,
  wt = NULL,
  discvar = NULL,
  discnum = 3:22,
  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 3:22.
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 st_unidisc().
cores

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

Value

A list of the SPADE model result.

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

SHAP power of determinants (SPD)

Description

Function for calculate SHAP power of determinants SPDSPD.

Usage

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

Arguments

formula

A formula of calculate SHAP power of determinants SPDSPD.
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

θxj(S)=∑s∈M∖{xj}∣S∣!(∣M∣−∣S∣−1)!∣M∣!(v(S∪{xj})−v(S))\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 xjx_j to the power of determinants.

Value

A tibble with variable and its corresponding SPDSPD value.

Note

The SHAP power of determinants (SPD) requires at least 2n−12^n-1 calculations when has nn 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 = spd_lesh(NDVIchange ~ ., data = ndvi)
g

spatial variance

Description

Function for calculate inverse distance weight.

Usage

spvar(yn, wtn)

Arguments

yn

The numerical vector of a response variable.
wtn

The spatial weight matrix.

Details

The spatial variance formula is Γ=∑i∑j≠iωij(yi−yj)22∑i∑j≠iωij\Gamma = \frac{\sum_i \sum_{j \neq i} \omega_{ij}\frac{(y_i-y_j)^2}{2}}{\sum_i \sum_{j \neq i} \omega_{ij}}

Value

A value of spatial variance.

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

y = c(42,56,73)
wt1 = inverse_distance_weight(1:length(y),1:length(y))
wt2 = matrix(1,ncol = length(y),nrow = length(y))
diag(wt2) = 0
spvar(y,wt1)
spvar(y,wt2)
var(y)

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 X1X_1, factor, character or discrete numeric.
x2

Covariate X2X_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 X1X_1 play a more important role than X2X_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)

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 [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_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 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')

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 X1X_1, factor, character or discrete numeric.
x2

Covariate X2X_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

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_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

Function for spatial rough set-based geographical detector 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 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

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

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

spatial fuzzy overlay

Description

Function for spatial fuzzy overlay.

Usage

st_fuzzyoverlay(formula, data, method = "and")

Arguments

formula

A formula of spatial fuzzy overlay.
data

A data.frame or tibble of discretized data.
method

(optional) Overlay methods. When method is and, use min to do fuzzy overlay; and when method is or,use max to do fuzzy overlay. Default is and.

Value

A spatial fuzzy overlay vector.

Note

Independent variables in the data provided to st_fuzzyoverlay() must be discretized variables, and dependent variable are continuous variable.

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')
sim = sim %>%
  dplyr::mutate(dplyr::across(4:6,\(.x) st_unidisc(.x,4,"quantile")))
fo1 = st_fuzzyoverlay(y~xa+xb+xc,data = sim, method = 'and')
fo2 = st_fuzzyoverlay(y~xa+xb+xc,data = sim, method = 'or')
fo1
fo2

univariate discretization

Description

Function to classify univariate vector to interval, a wrapper of classInt::classify_intervals().

Usage

st_unidisc(x, k, method = "quantile", factor = FALSE, seed = 123456789, ...)

Arguments

x

A continuous numerical variable.
k

(optional) Number of classes required, if missing, grDevices::nclass.Sturges() is used; see also the "dpih" and "headtails" styles for automatic choice of the number of classes. k must greater than 3.
method

Chosen classify style: one of "fixed", "sd", "equal", "pretty", "quantile", "kmeans", "hclust", "bclust", "fisher", "jenks", "dpih", "headtails", "maximum", or "box". Default is quantile.
factor

(optional) Default is FALSE, if TRUE returns cols as a factor with intervals as labels rather than integers.
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).
...

(optional) Other arguments passed to classInt::classify_intervals(), see ?classInt::classify_intervals().

Value

A discrete vector after being discretized.

Author(s)

Wenbo Lv [email protected]

Examples

xvar = c(22361, 9573, 4836, 5309, 10384, 4359, 11016, 4414, 3327, 3408,
         17816, 6909, 6936, 7990, 3758, 3569, 21965, 3605, 2181, 1892,
         2459, 2934, 6399, 8578, 8537, 4840, 12132, 3734, 4372, 9073,
         7508, 5203)
st_unidisc(xvar, k = 6, method = 'sd')

all discretization methods that can be used in st_unidisc

Description

A comprehensive vector of all discretization methods that can be employed within st_unidisc().

Usage

unidisc_methods()

Value

A character vector

Examples

unidisc_methods()

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)