Q.test: A function to compute Q test for spatial qualitative data...

In paezha/spqdata: A package for the exploratory analysis of cross-sectional spatial qualitative data

Description

A function to compute Q test for spatial qualitative data

Usage

Q.test(formula = NULL, data = NULL, na.action,
m = 3, r = 1, distr = "asymptotic",
fx = NULL, coor = NULL, control = list())

Q.test(formula = NULL, data = NULL, na.action, fx = NULL, coor = NULL, m = 3,
r = 1, distr = "asymptotic", control = list())

Arguments

formula	a symbolic description of the factor(s).
data	an (optional) data frame or a sf object with points/multipolygons geometry containing the variable(s) to be tested.
fx	a factor or a matrix of factors in columns
coor	(optional) a 2xN vector with spatial coordinates. Used when data is not a spatial object
m	length of m-surrounding (default = 3).
r	maximum overlapping between any two m-surroundings (default = 1).
distr	character. Distribution type "asymptotic" (default) or "mc".
control	Optional argument. See Control Argument section.

Details

Aquí Antonio escribe una linda historia ....

Value

An list of two object of the class htest. Each element of the list return the:

data.name	a character string giving the names of the data.
statistic	Value of the Q test
N	total number of observations.
R	total number of simbolized observations.
m	length m-surrounding.
r	degree of overlapping.
df	degree of freedom.
distr	type of distribution used to get the significance of the Q test.
type	type of simbols.

Control arguments

distance

character to select the type of distance. Default = "Euclidean" for Cartesian coordinates only: one of Euclidean, Hausdorff or Frechet; for geodetic coordinates, great circle distances are computed (see sf::st_distance())

dtmaxabs

Delete degenerate surrounding based on the absolute distance between observations.

dtmaxpc

A value between 0 and 1. Delete degenerate surrounding based on the distance. Delete m-surrounding when the maximum distance between observation is upper than k percentage of maximum distance between anywhere observation.

dtmaxknn

A integer value 'k'. Delete degenerate surrounding based on the near neighborhood criteria. Delete m-surrounding is a element of the m-surrounding is not include in the set of k near neighborhood of the first element

nsim

number of simulations for get the Monte Carlo distribution. Default = 999

seedinit

seed to select the initial element to star the algorithm to get compute the m-surroundings or to start the simulation

Author(s)

Fernando López	[e-mail]
Román Mínguez	[e-mail]
Antonio Páez	[e-mail]
Manuel Ruiz	[e-mail]

References

• Ruiz M, López FA, A Páez. (2010). Testing for spatial association of qualitative data using symbolic dynamics. Journal of Geographical Systems. 12 (3) 281-309

Examples

# Case 1: With coordinates
rm(list = ls())
N <- 100
cx <- runif(N)
cy <- runif(N)
coor <- cbind(cx,cy)
p <- c(1/6,3/6,2/6)
rho = 0.5
listw <- spdep::nb2listw(knn2nb(knearneigh(cbind(cx,cy), k = 4)))
fx <- dgp.spq(list = listw, p = p, rho = rho)
q.test <- Q.test(fx = fx, coor = coor, m = 3, r = 1)
summary(q.test)
plot(q.test)
print(q.test)
q.test.mc <- Q.test(fx = fx, coor = coor, m = 3, r = 1, distr = "mc")
summary(q.test.mc)
plot(q.test.mc)
print(q.test.mc)

# Case 2: With a sf object
rm(list = ls())
data("FastFood")
f1 <- ~ Type
q.test <- Q.test(formula = f1, data = FastFood.sf, m = c(3, 4),
r = c(1, 2, 3), control = list(distance ="Euclidean"))
summary(q.test)
plot(q.test)
print(q.test)
# Case 3: With a sf object with isolated areas
rm(list = ls())
data("Spain")
f1 <- ~ Older65 + MenWoman
q.test <- Q.test(formula = f1,
data = spain.sf, m = 3, r = 1, control = list(seedinit = 1111))
summary(q.test)
print(q.test)
plot(q.test)
q.test.mc <- Q.test(formula = f1, data = spain.sf, m = 4, r = 3, distr = "mc", control = list(seedinit = 1111))
summary(q.test.mc)
print(q.test.mc)
plot(q.test.mc)
# Case 4: Examples with multipolygons
rm(list = ls())
library(sf)
fname <- system.file("shape/nc.shp", package="sf")
nc <- st_read(fname)
qb79 <- quantile(nc$BIR79)
nc$QBIR79 <- (nc$BIR79 > qb79[2]) + (nc$BIR79 > qb79[3]) +
(nc$BIR79 >= qb79[4]) + 1
nc$QBIR79 <- as.factor(nc$QBIR79)
plot(nc["QBIR79"], pal = c("#FFFEDE","#FFDFA2", "#FFA93F", "#D5610D"),
     main = "BIR79 (Quartiles)")
sid79 <- quantile(nc$SID79)
nc$QSID79 <- (nc$SID79 > sid79[2]) + (nc$SID79 > sid79[3]) +
(nc$SID79 >= sid79[4]) + 1
nc$QSID79 <- as.factor(nc$QSID79)
plot(nc["QSID79"], pal = c("#FFFEDE","#FFDFA2", "#FFA93F", "#D5610D"),
     main = "SID79 (Quartiles)")
f1 <- ~ QSID79 + QBIR79
lq1nc <- Q.test(formula = f1, data = nc, m = 5, r = 2,
control = list(seedinit = 1111, dtmaxpc = 0.5, distance = "Euclidean") )
print(lq1nc)

lq2nc <- Q.test(formula = f1, data = nc, m = 5, r = 2, control = list(dtmaxpc = 0.2) )
print(lq2nc)

lq3nc <- Q.test(formula = f1, data = nc, m = 5, r = 2, control = list(dtmaxknn = 5) )
print(lq3nc)

# Case 5: Examples with points and matrix of variables
fx <- matrix(c(nc$QBIR79, nc$QSID79), ncol = 2, byrow = TRUE)
mctr <- suppressWarnings(sf::st_centroid(sf::st_geometry(nc)))
mcoor <- sf::st_coordinates(mctr)[,c("X","Y")]
q.test <- Q.test(fx = fx, coor = mcoor, m = 5, r = 2,control = list(seedinit = 1111, dtmaxpc = 0.5))
print(q.test)
plot(q.test)

paezha/spqdata documentation built on Dec. 22, 2021, 5:24 a.m.