Package 'sdsfun'

check for NA values in a tibble

Description

check for NA values in a tibble

Usage

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)

---

discretization

Description

discretization

Usage

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')

---

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)

Arguments

tbl	A tibble or data.frame.

Value

A tibble

Examples

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)

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))

---

get variable names in a formula and data

Description

get variable names in a formula and data

Usage

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)

---

spatial fuzzy overlay

Description

spatial fuzzy overlay

Usage

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

---

generate subsets of a set

Description

generate subsets of a set

Usage

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)

---

only geodetector q-value

Description

only geodetector q-value

Usage

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)))

---

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, ...)

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"])

---

construct inverse distance weight

Description

Function for constructing inverse distance weight.

Usage

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]

---

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)

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)

---

test global spatial autocorrelation

Description

Spatial autocorrelation test based on global moran index.

Usage

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)

---

normalization

Description

normalization

Usage

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))

---

extract locations

Description

Extract locations of sf objects.

Usage

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)

---

generates distance matrix

Description

Generates distance matrix for sf object

Usage

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]

---

sf object geometry column name

Description

Get the geometry column name of an sf object

Usage

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)

---

sf object geometry type

Description

Get the geometry type of an sf object

Usage

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)

---

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)

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)

---

generates voronoi diagram

Description

Generates Voronoi diagram (Thiessen polygons) for sf object

Usage

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()

---

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
)

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')

---

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
)

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)

---

spatial linear models selection

Description

spatial linear models selection

Usage

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)

---

construct neighbours list

Description

construct neighbours list

Usage

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)

---

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, ...)

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"])

---

spatial variance

Description

spatial variance

Usage

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)

---

test explanatory power of spatial stratified heterogeneity

Description

Spatial stratified heterogeneity test based on geographical detector q value.

Usage

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)))

---

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)

Arguments

x	A numeric vector

Value

A standardized numeric vector

Examples

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)

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)

---