Q.map.test | R Documentation |
This function compute the QE and QI tests for maps comparison based on symbolic entropy.
Q.map.test(formula = formula, data = data, coor = NULL, m = m, r = 1,
type = "combinations", control = list())
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. |
If data
is not a sf object the coor
argument with the coordinates
of each observation must be included.
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. |
Several parameters to construct the m-surrounding
Delete degenerate surrounding based on the absolute distance between observations.
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.
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
seed to select the initial element to star the algorithm to compute the m-surroundings.
Fernando López | fernando.lopez@upct.es |
Román Mínguez | roman.minguez@uclm.es |
Antonio Páez | paezha@gmail.com |
Manuel Ruiz | manuel.ruiz@upct.es |
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.
dgp.spq
, m.surround
, Q.test
# 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]]$mh)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
type ="combinations")
print(qmap)
plot(qmap)
control <- list(dtmaxknn = 10)
qmap <- Q.map.test(formula = formula, data = dt, coor = x, m = m, r = r,
type ="combinations", control = control)
print(qmap)
plot(qmap)
# Case 2:
data(provinces_spain)
# sf::sf_use_s2(FALSE)
m = 3
r = 1
provinces_spain$Mal2Fml <- factor(provinces_spain$Mal2Fml > 100)
levels(provinces_spain$Mal2Fml) = c("men","woman")
provinces_spain$Coast <- factor(provinces_spain$Coast)
levels(provinces_spain$Coast) = c("no","yes")
formula <- ~ Coast + Mal2Fml
qmap <- Q.map.test(formula = formula, data = provinces_spain, m = m, r = r,
type ="combinations")
print(qmap)
plot(qmap)
plot(qmap[[1]]$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)
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