Package 'sdsfun' reference manual

Title:	Spatial Data Science Complementary Features
Description:	Wrapping and supplementing commonly used functions in the R ecosystem related to spatial data science, while serving as a basis for other packages maintained by Wenbo Lv.
Authors:	Wenbo Lv [aut, cre, cph]
Maintainer:	Wenbo Lv <[email protected]>
License:	GPL-3
Version:	0.5.0
Built:	2024-11-25 19:33:31 UTC
Source:	CRAN

check for NA values in a tibble

Description

check for NA values in a tibble

Usage

check_tbl_na(tbl)
check_tbl_na(tbl)

Arguments

tbl

A tibble

Value

A logical value.

Examples

demotbl = tibble::tibble(x = c(1,2,3,NA,1),
                         y = c(NA,NA,1:3),
                         z = 1:5)
demotbl
check_tbl_na(demotbl)

demotbl = tibble::tibble(x = c(1,2,3,NA,1),
                         y = c(NA,NA,1:3),
                         z = 1:5)
demotbl
check_tbl_na(demotbl)

discretization

Description

discretization

Usage

discretize_vector(
  x,
  n,
  method = "natural",
  breakpoint = NULL,
  sampleprob = 0.15,
  seed = 123456789
)
discretize_vector(
  x,
  n,
  method = "natural",
  breakpoint = NULL,
  sampleprob = 0.15,
  seed = 123456789
)

Arguments

`x`	A continuous numeric vector.
`n`	(optional) The number of discretized classes.
`method`	(optional) The method of discretization, default is `natural`.
`breakpoint`	(optional) Break points for manually splitting data. When `method` is `manual`, `breakpoint` is required.
`sampleprob`	(optional) When the data size exceeds `3000`, perform sampling for discretization, applicable only to natural breaks. Default is `0.15`.
`seed`	(optional) Random seed number, default is `123456789`.

Value

A discretized integer vector

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)
discretize_vector(xvar, n = 5, method = 'natural')

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)
discretize_vector(xvar, n = 5, method = 'natural')

transforming a category tibble into the corresponding dummy variable tibble

Description

transforming a category tibble into the corresponding dummy variable tibble

Usage

dummy_tbl(tbl)
dummy_tbl(tbl)

Arguments

tbl

A tibble or data.frame.

Value

A tibble

Examples

a = tibble::tibble(x = 1:3,y = 4:6)
dummy_tbl(a)

a = tibble::tibble(x = 1:3,y = 4:6)
dummy_tbl(a)

transforming a categorical variable into dummy variables

Description

transforming a categorical variable into dummy variables

Usage

dummy_vec(x)
dummy_vec(x)

Arguments

`x`	An integer vector or can be converted into an integer vector.

Value

A matrix.

Examples

dummy_vec(c(1,1,3,2,4,6))

dummy_vec(c(1,1,3,2,4,6))

get variable names in a formula and data

Description

get variable names in a formula and data

Usage

formula_varname(formula, data)
formula_varname(formula, data)

Arguments

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

Value

A list.

yname: Independent variable name
xname: Dependent variable names

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
formula_varname(PS_Score ~ EL_Score + OH_Score, gzma)
formula_varname(PS_Score ~ ., gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
formula_varname(PS_Score ~ EL_Score + OH_Score, gzma)
formula_varname(PS_Score ~ ., gzma)

spatial fuzzy overlay

Description

spatial fuzzy overlay

Usage

fuzzyoverlay(formula, data, method = "and")
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 numeric vector.

Note

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

Examples

sim = tibble::tibble(y = stats::runif(7,0,10),
                     x1 = c(1,rep(2,3),rep(3,3)),
                     x2 = c(rep(1,2),rep(2,2),rep(3,3)))
fo1 = fuzzyoverlay(y~x1+x2,data = sim, method = 'and')
fo1
fo2 = fuzzyoverlay(y~x1+x2,data = sim, method = 'or')
fo2

sim = tibble::tibble(y = stats::runif(7,0,10),
                     x1 = c(1,rep(2,3),rep(3,3)),
                     x2 = c(rep(1,2),rep(2,2),rep(3,3)))
fo1 = fuzzyoverlay(y~x1+x2,data = sim, method = 'and')
fo1
fo2 = fuzzyoverlay(y~x1+x2,data = sim, method = 'or')
fo2

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)

only geodetector q-value

Description

only geodetector q-value

Usage

geodetector_q(y, hs)
geodetector_q(y, hs)

Arguments

`y`	Dependent variable
`hs`	Independent variable

Value

A numeric value

Examples

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

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

hierarchical clustering with spatial soft constraints

Description

hierarchical clustering with spatial soft constraints

Usage

hclustgeo_disc(data, n, alpha = 0.5, D1 = NULL, scale = TRUE, wt = NULL, ...)
hclustgeo_disc(data, n, alpha = 0.5, D1 = NULL, scale = TRUE, wt = NULL, ...)

Arguments

`data`	An `sf` object, `tibble`, `data.frame`, `matrix` or `vector` of observations data.
`n`	The number of hierarchical clustering classes, which can be a numeric value or vector.
`alpha`	(optional) A positive value between `0` and `1`. This mixing parameter gives the relative importance of "feature" space and "constraint" space. Default is `0.5`.
`D1`	(optional) A `matrix` with other dissimilarities between the same observations data. if `data` is an `sf` object and alpha is not `0`, the `D1` will be generated by `sdsfun::sf_distance_matrix()`, others will use a `matrix` with all elements equal to `0`.
`scale`	(optional) Whether to scaled the dissimilarities matrix, default is `TRUE`.
`wt`	(optional) Vector with the weights of the observations. By default, `wt` is `NULL`.
`...`	(optional) Other arguments passed to `stats::dist()`.

Value

A vector with grouped memberships if n are scalar, otherwise a matrix with grouped memberships is returned where each column corresponds to the elements of n, respectively.

Note

This is a ⁠C++⁠ enhanced implementation of the hclustgeo function in ClustGeo package.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma$group = hclustgeo_disc(gzma,5,alpha = 0.75)
plot(gzma["group"])

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma$group = hclustgeo_disc(gzma,5,alpha = 0.75)
plot(gzma["group"])

construct inverse distance weight

Description

Function for constructing inverse distance weight.

Usage

inverse_distance_swm(sfj, power = 1, bandwidth = NULL)
inverse_distance_swm(sfj, power = 1, bandwidth = NULL)

Arguments

`sfj`	Vector object that can be converted to `sf` by `sf::st_as_sf()`.
`power`	(optional) Default is 1. Set to 2 for gravity weights.
`bandwidth`	(optional) When the distance is bigger than bandwidth, the corresponding part of the weight matrix is set to 0. Default is `NULL`, which means not use the bandwidth.

Details

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

Value

A inverse distance weight matrices with class of matrix.

Examples

library(sf)
pts = read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt = inverse_distance_swm(pts)
wt[1:5,1:5]

library(sf)
pts = read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt = inverse_distance_swm(pts)
wt[1:5,1:5]

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_optnum(qvec, discnumvec, increase_rate = 0.05)
loess_optnum(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 `0.05`.

Value

A two element numeric vector.

discnum: optimal number of spatial data discretization
increase_rate: the critical increase rate of the number of discretization

Note

When increase_rate is not satisfied by the calculation, the discrete number corresponding to the highest ⁠q statistic⁠ is selected as a return.

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

Examples

qv = c(0.26045642,0.64120405,0.43938704,0.95165535,0.46347836,
       0.25385338,0.78778726,0.95938330,0.83247885,0.09285196)
loess_optnum(qv,3:12)

qv = c(0.26045642,0.64120405,0.43938704,0.95165535,0.46347836,
       0.25385338,0.78778726,0.95938330,0.83247885,0.09285196)
loess_optnum(qv,3:12)

test global spatial autocorrelation

Description

Spatial autocorrelation test based on global moran index.

Usage

moran_test(sfj, wt = NULL, alternative = "greater", symmetrize = FALSE)
moran_test(sfj, wt = NULL, alternative = "greater", symmetrize = FALSE)

Arguments

`sfj`	An `sf` object or can be converted to `sf` by `sf::st_as_sf()`.
`wt`	(optional) Spatial weight matrix. Must be a `matrix` class. If `wt` is not provided, `sdsfun` will use a first-order queen adjacency binary matrix.
`alternative`	(optional) Specification of alternative hypothesis as `greater` (default), `lower`, or `two.sided`.
`symmetrize`	(optional) Whether or not to symmetrize the asymmetrical spatial weight matrix wt by: 1/2 * (wt + wt'). Default is `FALSE`.

Value

A list with moran_test class and result stored on the result tibble. Which contains the following information for each variable:

MoranI: observed value of the Moran coefficient
EI: expected value of Moran's I
VarI: variance of Moran's I (under normality)
ZI: standardized Moran coefficient
PI: p-value of the test statistic

Note

This is a ⁠C++⁠ implementation of the MI.vec function in spfilteR package, and embellishes the console output.

The return result of this function is actually a list, please access the result tibble using ⁠$result⁠.

The non-numeric columns of the attribute columns in sfj are ignored.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
moran_test(gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
moran_test(gzma)

normalization

Description

normalization

Usage

normalize_vector(x, to_left = 0, to_right = 1)
normalize_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 which has normalized.

Examples

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

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

extract locations

Description

Extract locations of sf objects.

Usage

sf_coordinates(sfj)
sf_coordinates(sfj)

Arguments

sfj

An sf object or can be converted to sf by sf::st_as_sf().

Value

A matrix.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
sf_coordinates(pts)

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
sf_coordinates(pts)

generates distance matrix

Description

Generates distance matrix for sf object

Usage

sf_distance_matrix(sfj)
sf_distance_matrix(sfj)

Arguments

sfj

An sf object or can be converted to sf by sf::st_as_sf().

Value

A matrix.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_distm = sf_distance_matrix(pts)
pts_distm[1:5,1:5]

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_distm = sf_distance_matrix(pts)
pts_distm[1:5,1:5]

sf object geometry column name

Description

Get the geometry column name of an sf object

Usage

sf_geometry_name(sfj)
sf_geometry_name(sfj)

Arguments

sfj

An sf object.

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_name(gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_name(gzma)

sf object geometry type

Description

Get the geometry type of an sf object

Usage

sf_geometry_type(sfj)
sf_geometry_type(sfj)

Arguments

sfj

An sf object.

Value

A lowercase character vector

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_type(gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_geometry_type(gzma)

generates cgcs2000 Gauss-Kruger projection epsg coding character

Description

Generates a Gauss-Kruger projection epsg coding character corresponding to an sfj object under the CGCS2000 spatial reference.

Usage

sf_gk_proj_cgcs2000(sfj, degree = 6L)
sf_gk_proj_cgcs2000(sfj, degree = 6L)

Arguments

`sfj`	An `sf` object or can be converted to `sf` by `sf::st_as_sf()`.
`degree`	(optional) `3`-degree or `6`-degree zonal projection, default is `6L`.

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun')) |>
  sf::st_transform(4490)
sf_gk_proj_cgcs2000(gzma,3)
sf_gk_proj_cgcs2000(gzma,6)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun')) |>
  sf::st_transform(4490)
sf_gk_proj_cgcs2000(gzma,3)
sf_gk_proj_cgcs2000(gzma,6)

generates wgs84 utm projection epsg coding character

Description

Generates a utm projection epsg coding character corresponding to an sfj object under the WGS84 spatial reference.

Usage

sf_utm_proj_wgs84(sfj)
sf_utm_proj_wgs84(sfj)

Arguments

sfj

An sf object or can be converted to sf by sf::st_as_sf().

Value

A character.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_utm_proj_wgs84(gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
sf_utm_proj_wgs84(gzma)

generates voronoi diagram

Description

Generates Voronoi diagram (Thiessen polygons) for sf object

Usage

sf_voronoi_diagram(sfj)
sf_voronoi_diagram(sfj)

Arguments

sfj

An sf object.

Value

An sf object of polygon geometry type or can be converted to this by sf::st_as_sf().

Note

Only sf objects of (multi-)point type are supported to generate voronoi diagram and the returned result includes only the geometry column.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_v = sf_voronoi_diagram(pts)

library(ggplot2)
ggplot() +
  geom_sf(data = pts_v, color = 'red',
          fill = 'transparent') +
  geom_sf(data = pts, color = 'blue', size = 1.25) +
  theme_void()

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
pts_v = sf_voronoi_diagram(pts)

library(ggplot2)
ggplot() +
  geom_sf(data = pts_v, color = 'red',
          fill = 'transparent') +
  geom_sf(data = pts, color = 'blue', size = 1.25) +
  theme_void()

only spade power of spatial determinant

Description

only spade power of spatial determinant

Usage

spade_psd(y, hs, wt)
spade_psd(y, hs, wt)

Arguments

`y`	Dependent variable
`hs`	Independent variable
`wt`	Spatial weight matrix

Value

A numeric value

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spade_psd(y = gzma$PS_Score,
          hs = discretize_vector(gzma$PS_Score,5),
          wt = wt1)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spade_psd(y = gzma$PS_Score,
          hs = discretize_vector(gzma$PS_Score,5),
          wt = wt1)

constructs spatial weight matrices based on contiguity

Description

Constructs spatial weight matrices based on contiguity via spdep package.

Usage

spdep_contiguity_swm(
  sfj,
  queen = TRUE,
  k = NULL,
  order = 1L,
  cumulate = TRUE,
  style = "W",
  zero.policy = TRUE
)
spdep_contiguity_swm(
  sfj,
  queen = TRUE,
  k = NULL,
  order = 1L,
  cumulate = TRUE,
  style = "W",
  zero.policy = TRUE
)

Arguments

`sfj`	An `sf` object or can be converted to `sf` by `sf::st_as_sf()`.
`queen`	(optional) if `TRUE`, using queen contiguity, otherwise rook contiguity. Default is `TRUE`.
`k`	(optional) The number of nearest neighbours. Ignore this parameter when not using distance based neighbours to construct spatial weight matrices.
`order`	(optional) The order of the adjacency object. Default is `1`.
`cumulate`	(optional) Whether to accumulate adjacency objects. Default is `TRUE`.
`style`	(optional) `style` can take values `W`, `B`, `C`, and `S`. More to see `spdep::nb2mat()`. Default is `W`.
`zero.policy`	(optional) if `FALSE` stop with error for any empty neighbour sets, if `TRUE` permit the weights list to be formed with zero-length weights vectors. Default is `TRUE`.

Value

A matrix

Note

When k is set to a positive value, using K-Nearest Neighbor Weights.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = spdep_contiguity_swm(gzma, k = 6, style = 'B')
wt2 = spdep_contiguity_swm(gzma, queen = TRUE, style = 'B')
wt3 = spdep_contiguity_swm(gzma, queen = FALSE, order = 2, style = 'B')

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = spdep_contiguity_swm(gzma, k = 6, style = 'B')
wt2 = spdep_contiguity_swm(gzma, queen = TRUE, style = 'B')
wt3 = spdep_contiguity_swm(gzma, queen = FALSE, order = 2, style = 'B')

constructs spatial weight matrices based on distance

Description

Constructs spatial weight matrices based on distance via spdep package.

Usage

spdep_distance_swm(
  sfj,
  kernel = NULL,
  k = NULL,
  bandwidth = NULL,
  power = 1,
  style = "W",
  zero.policy = TRUE
)
spdep_distance_swm(
  sfj,
  kernel = NULL,
  k = NULL,
  bandwidth = NULL,
  power = 1,
  style = "W",
  zero.policy = TRUE
)

Arguments

`sfj`	An `sf` object or can be converted to `sf` by `sf::st_as_sf()`.
`kernel`	(optional) The kernel function, can be one of `uniform`, `triangular`,`quadratic`(`epanechnikov`),`quartic` and `gaussian`. Default is `NULL`.
`k`	(optional) The number of nearest neighbours. Default is `NULL`. Only useful when `kernel` is provided.
`bandwidth`	(optional) The bandwidth, default is `NULL`. When the spatial reference of sf object is the geographical coordinate system, the unit of `bandwidth` is `km`. The unit used in the projection coordinate system are consistent with those used in the sf object coordinate system.
`power`	(optional) Default is `1`. Useful when `kernel` is not provided.
`style`	(optional) `style` can take values `W`, `B`, `C`, and `S`. More to see `spdep::nb2mat()`. Default is `W`. For spatial weights based on distance functions, a style of `B` means using the original value of the calculated distance function.
`zero.policy`	(optional) if `FALSE` stop with error for any empty neighbour sets, if `TRUE` permit the weights list to be formed with zero-length weights vectors. Default is `TRUE`.

Details

five different kernel weight functions:

uniform: $K_{(z)} = 1/2$ ,for $\lvert z \rvert < 1$
triangular $K_{(z)} = 1 - \lvert z \rvert$ ,for $\lvert z \rvert < 1$
quadratic (epanechnikov) $K_{(z)} = \frac{3}{4} \left( 1 - z^2 \right)$ ,for $\lvert z \rvert < 1$
quartic $K_{(z)} = \frac{15}{16} {\left( 1 - z^2 \right)}^2$ ,for $\lvert z \rvert < 1$
gaussian $K_{(z)} = \frac{1}{\sqrt{2 \pi}} e^{- \frac{z^2}{2}}$

For the equation above, $z = d_{ij} / h_i$ where $h_i$ is the bandwidth

Value

A matrix

Note

When kernel is setting, using distance weight based on kernel function, Otherwise the inverse distance weight will be used.

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt1 = spdep_distance_swm(pts, style = 'B')
wt2 = spdep_distance_swm(pts, kernel = 'gaussian')
wt3 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian')
wt4 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian', bandwidth = 10000)

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
wt1 = spdep_distance_swm(pts, style = 'B')
wt2 = spdep_distance_swm(pts, kernel = 'gaussian')
wt3 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian')
wt4 = spdep_distance_swm(pts, k = 3, kernel = 'gaussian', bandwidth = 10000)

spatial linear models selection

Description

spatial linear models selection

Usage

spdep_lmtest(formula, data, listw = NULL)
spdep_lmtest(formula, data, listw = NULL)

Arguments

`formula`	A formula for linear regression model.
`data`	An `sf` object of observation data.
`listw`	(optional) A listw. See `spdep::mat2listw()` and `spdep::nb2listw()` for details.

Value

A list

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
spdep_lmtest(PS_Score ~ ., gzma)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
spdep_lmtest(PS_Score ~ ., gzma)

construct neighbours list

Description

construct neighbours list

Usage

spdep_nb(sfj, queen = TRUE, k = NULL, order = 1L, cumulate = TRUE)
spdep_nb(sfj, queen = TRUE, k = NULL, order = 1L, cumulate = TRUE)

Arguments

`sfj`	An `sf` object or can be converted to `sf` by `sf::st_as_sf()`.
`queen`	(optional) if `TRUE`, using queen contiguity, otherwise rook contiguity. Default is `TRUE`.
`k`	(optional) The number of nearest neighbours. Ignore this parameter when not using distance based neighbours.
`order`	(optional) The order of the adjacency object. Default is `1`.
`cumulate`	(optional) Whether to accumulate adjacency objects. Default is `TRUE`.

Value

A neighbours list with class nb

Note

When k is set to a positive value, using K-Nearest Neighbor

Examples

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
nb1 = spdep_nb(pts, k = 6)
nb2 = spdep_nb(pts, queen = TRUE)
nb3 = spdep_nb(pts, queen = FALSE, order = 2)

pts = sf::read_sf(system.file('extdata/pts.gpkg',package = 'sdsfun'))
nb1 = spdep_nb(pts, k = 6)
nb2 = spdep_nb(pts, queen = TRUE)
nb3 = spdep_nb(pts, queen = FALSE, order = 2)

spatial c(k)luster analysis by tree edge removal

Description

SKATER forms clusters by spatially partitioning data that has similar values for features of interest.

Usage

spdep_skater(sfj, k = 6, nb = NULL, ini = 5, ...)
spdep_skater(sfj, k = 6, nb = NULL, ini = 5, ...)

Arguments

`sfj`	An `sf` object of observation data. Please ensure that the attribute columns are included in the SKATER analysis.
`k`	(optional) The number of clusters. Default is `6`.
`nb`	(optional) A neighbours list with class nb. If the input `nb` is NULL, it will be constructed automatically using `spdep_nb()`.
`ini`	(optional) The initial node in the minimal spanning tree. Defaul is `5`.
`...`	(optional) Other parameters passed to spdep::skater().

Value

A numeric vector of clusters.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma_c = spdep_skater(gzma,8)
gzma$group = gzma_c
plot(gzma["group"])

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
gzma_c = spdep_skater(gzma,8)
gzma$group = gzma_c
plot(gzma["group"])

spatial variance

Description

spatial variance

Usage

spvar(x, wt, method = "cpp")
spvar(x, wt, method = "cpp")

Arguments

`x`	A numerical vector .
`wt`	The spatial weight matrix.
`method`	(optional) The method for calculating spatial variance, which can be chosen as either `cpp` or `r`. Default is `cpp`.

Details

The spatial variance formula is $\Gamma = \frac{\sum_i \sum_{j \neq i} \omega_{ij}\frac{(y_i-y_j)^2}{2}}{\sum_i \sum_{j \neq i} \omega_{ij}}$

Value

A numerical value.

Examples

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spvar(gzma$PS_Score,wt1)

gzma = sf::read_sf(system.file('extdata/gzma.gpkg',package = 'sdsfun'))
wt1 = inverse_distance_swm(gzma)
spvar(gzma$PS_Score,wt1)

test explanatory power of spatial stratified heterogeneity

Description

Spatial stratified heterogeneity test based on geographical detector q value.

Usage

ssh_test(y, hs)
ssh_test(y, hs)

Arguments

`y`	Variable Y, continuous numeric vector.
`hs`	Spatial stratification or classification of each explanatory variable. `factor`, `character`, `integer` or `data.frame`, `tibble` and `sf` object.

Value

A tibble

Note

This is a ⁠C++⁠ implementation of the factor_detector function in gdverse package.

Examples

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

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

standardization

Description

To calculate the Z-score using variance normalization, the formula is as follows:

$Z = \frac{(x - mean(x))}{sd(x)}$

Usage

standardize_vector(x)
standardize_vector(x)

Arguments

`x`	A numeric vector

Value

A standardized numeric vector

Examples

standardize_vector(1:10)

standardize_vector(1:10)

convert discrete variables in a tibble to integers

Description

convert discrete variables in a tibble to integers

Usage

tbl_all2int(tbl)
tbl_all2int(tbl)

Arguments

tbl

A tibble,data.frame or sf object.

Value

A converted tibble,data.frame or sf object.

Examples

demotbl = tibble::tibble(x = c(1,2,3,3,1),
                         y = letters[1:5],
                         z = c(1L,1L,2L,2L,3L),
                         m = factor(letters[1:5],levels = letters[5:1]))
tbl_all2int(demotbl)

demotbl = tibble::tibble(x = c(1,2,3,3,1),
                         y = letters[1:5],
                         z = c(1L,1L,2L,2L,3L),
                         m = factor(letters[1:5],levels = letters[5:1]))
tbl_all2int(demotbl)

Package 'sdsfun'

Help Index

check for NA values in a tibble

Description

Usage

Arguments

Value

Examples

discretization

Description

Usage

Arguments

Value

Examples

transforming a category tibble into the corresponding dummy variable tibble

Description

Usage

Arguments

Value

Examples

transforming a categorical variable into dummy variables

Description

Usage

Arguments

Value

Examples

get variable names in a formula and data

Description

Usage

Arguments

Value

Examples

spatial fuzzy overlay

Description

Usage

Arguments

Value

Note

Examples

generate subsets of a set

Description

Usage

Arguments

Value

Examples

only geodetector q-value

Description

Usage

Arguments

Value

Examples

hierarchical clustering with spatial soft constraints

Description

Usage

Arguments

Value

Note

Examples

construct inverse distance weight

Description

Usage

Arguments

Details

Value

Examples

determine optimal spatial data discretization for individual variables

Description

Usage

Arguments

Value

Note

Examples

test global spatial autocorrelation

Description

Usage

Arguments

Value

Note

Examples

normalization