Q.map.test: Compute the QE and QI tests of Equivalence and Independence...

In spqdep: Testing for Spatial Independence of Qualitative Data in Cross Section

Compute the QE and QI tests of Equivalence and Independence between maps

Description

This function compute the QE and QI tests for maps comparison based on symbolic entropy.

Usage

Q.map.test(formula = formula, data = data, coor = NULL, m = m, r = 1,
type = "combinations", control = list())

Arguments

formula	a symbolic description of the two factors.
data	(optional) a data frame or a sf object containing the variables to testing for.
coor	(optional) a 2xN vector with coordinates.
m	length of m-surrounding.
r	maximum overlapping between any two m-surroundings (default = 1).
type	Type of symbols: "permutations" or "combinations". Default "combinations"
control	Optional argument. See Control Argument section.

Details

If data is not a sf object the coor argument with the coordinates of each observation must be included.

Value

A list with two objects of the class htest. The first one is the QE test of Equivalence between maps and the second one is the QI test of independence between maps. the elements of each test are:

method	a character string giving description of the method.
data.name	a character string giving the name(s) of the data.
statistic	the value of the statistic QE or/and QI.
alternative	a character string describing the alternative hypothesis.
p.value	p-value for QE or QI.
parameter	free degree of the statistic for QE or QI.
symb	A matrix with the symbols.
mh	m-surrounding of th map.
Tm	number of maps (ONLY 2).
sample.size	number of symbolized observations.
nsk	a matrix Tm x symbols with the frequency of the number of symbols of each map.

Control arguments

Several parameters to construct the m-surrounding

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

seedinit

seed to select the initial element to star the algorithm to compute the m-surroundings.

Author(s)

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

References

• Ruiz M, López FA and A Páez (2011). Comparison of Thematic Maps Using Symbolic Entropy. International Journal of Geographical Information Science, 26, 413-439.

• Ruiz, M., López, FA, and Páez, A. (2010). Testing for spatial association of qualitative data using symbolic dynamics. Journal of Geographical Systems, 12(3), 281-309.0.

See Also

dgp.spq, m.surround, Q.test

Examples

# Case 1:
N <- 200
cx <- runif(N)
cy <- runif(N)
x <- cbind(cx,cy)
listw <- spdep::nb2listw(spdep::knn2nb(
           spdep::knearneigh(cbind(cx,cy), k = 4)))
p <- c(1/6, 3/6, 2/6)
rho = 0.5
QY1 <- dgp.spq(p = p, listw = listw, rho = rho)
rho = 0.8
QY2 <- dgp.spq(p = p, listw = listw, rho = rho)
dt = data.frame(QY1,QY2)
m = 3
r = 1
formula <- ~ QY1 + QY2
control <- list(dtmaxknn = 10)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
                   type ="combinations", control = control)
print(qmap)
plot(qmap)
plot(qmap, ci=.6)
plot(qmap[[1]]$mh)
summary(qmap[[1]]$mh)

control <- list(dtmaxknn = 20)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
                   type ="permutations", control = control)
print(qmap)
plot(qmap)
plot(qmap[[1]]$mh)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
                   type ="combinations")
print(qmap)
plot(qmap)
control <- list(dtmaxknn = 10)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
                   type ="combinations", control = control)
print(qmap)
plot(qmap)

# Case 2:
data(provinces_spain)
sf::sf_use_s2(FALSE)
m = 3
r = 1

provinces_spain$Male2Female <- factor(provinces_spain$Male2Female > 100)
levels(provinces_spain$Male2Female) = c("men","woman")
provinces_spain$Coast <- factor(provinces_spain$Coast)
levels(provinces_spain$Coast) = c("no","yes")
formula <- ~ Coast + Male2Female
qmap <- Q.map.test(formula = formula, data = provinces_spain, m = m, r = r,
                   type ="combinations")
print(qmap)
plot(qmap)
plot(qmap[[1]]$mh)

control <- list(dtmaxknn = 6)
qmap <- Q.map.test(formula = formula, data = provinces_spain, m = m, r = r,
                   type ="combinations", control = control)
print(qmap)
plot(qmap[[1]]$mh)
summary(qmap[[1]]$mh)

spqdep documentation built on March 28, 2022, 5:06 p.m.