jc.test: A function to compute joint-count test for binomial and...

In spqdep: Testing for Spatial Independence of Cross-Sectional Qualitative Data

A function to compute joint-count test for binomial and multinomial (asymptotic and permutation distributions).

Description

A function to compute joincount tests for spatial qualitative data. This function is a wrapper of joincount.multi and joincount.test in spdep package.

Usage

jc.test(formula = NULL,
               data = NULL,
               fx = NULL,
               listw = NULL,
               na.action,
               zero.policy = NULL,
               distr = "asymptotic",
               alternative = "greater",
               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
listw	A listw object created for example by nb2listw from spdep package; if nb2listw not given, the spatial weights are built using the object given in listw argument (usually an sf object). Default = NULL.
na.action	A function (default options("na.action")), can also be na.omit or na.exclude. It may be necessary to set zero.policy to TRUE because this subsetting may create no-neighbour observations.
zero.policy	Similar to the corresponding parameter of lagsarlm function in spatialreg package. If TRUE assign zero to the lagged value of zones without neighbours. Default = NULL.
distr	character. Distribution type "asymptotic" (default) or "mc".
alternative	character string specifying the alternative hypothesis, must be one of "greater" (default), or "less".
control	list of additional arguments.

Value

An spjctest object. This type of object is a list of htest objects. The length of the list is the number of factor variables included in the formula or the number of columns in fx. Each element of the list can be a jclist object, for binomial factors, or a jcmulti object for multinomial factors. See joincount.test or joincount.multi for additional details.

Control arguments

nsim	number of permutations used in the Monte Carlo distribution. Default nsim = 999.
seedinit	seed to select the initial element during the simulations. Default seedinit = 1111.
adjust.n	default TRUE, if FALSE the number of observations is not adjusted for no-neighbour observations, if TRUE, the number of observations is adjusted consistently. See joincount.multi for aditional information.
spChk	should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption(). See joincount.multi for aditional information.
sampling	default nonfree, may be free. See joincount.test for additional information.
queen	default TRUE. Defines the neighborhood criteria for sf objects.
style	defines the style for listw. Default = B (binary).
knn	chooses the number of neighboors when this criteria is used. Default knn = 5.

Author(s)

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

References

• Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, pp. 19-20.

• Upton, G., Fingleton, B. 1985 Spatial data analysis by example: point pattern and qualitative data, Wiley, pp. 158–170.

See Also

print.summary.spqtest, joincount.test, joincount.multi

Examples

## Case 1
## Multinomial + Binomial using a sf multipolygon

data("provinces_spain")
# sf::sf_use_s2(FALSE)
provinces_spain$Mal2Fml <- factor(provinces_spain$Mal2Fml > 100)
levels(provinces_spain$Mal2Fml) = c("men","woman")
provinces_spain$Older <- cut(provinces_spain$Older, breaks = c(-Inf,19,22.5,Inf))
levels(provinces_spain$Older) = c("low","middle","high")
f1 <- ~ Older + Mal2Fml
jc1 <- jc.test(formula = f1,
               data = provinces_spain,
               distr = "mc",
               alternative = "greater",
               zero.policy = TRUE)
summary(jc1)

provinces_spain$Coast <- factor(provinces_spain$Coast)
levels(provinces_spain$Coast) = c("no","yes")
f2 <- ~ Mal2Fml + Coast
jc2 <- jc.test(formula = f2,
               data = provinces_spain,
               distr = "mc",
               zero.policy = TRUE)
summary(jc2)


# Case 2:
## Multinomial using a sf multipoint
data("FastFood.sf")
# sf::sf_use_s2(FALSE)
f1 <- ~ Type
jc3 <- jc.test(formula = f1,
               data = FastFood.sf,
               distr = "asymptotic",
               control = list(knn = 6))
 summary(jc3)

# Examples function joincount.test
data(oldcol, package = "spdep")
HICRIME <- cut(COL.OLD$CRIME, breaks = c(0,35,80), labels = c("low","high"))
names(HICRIME) <- rownames(COL.OLD)
jc4 <- jc.test(fx = HICRIME,
               listw = spdep::nb2listw(COL.nb,
               style="B"))
summary(jc4)
 jc5 <- jc.test(fx = HICRIME,
                listw = spdep::nb2listw(COL.nb, style="B"),
                distr = "mc")
 summary(jc5)
 HICRIME <- cut(COL.OLD$CRIME, breaks = c(0, 35, 80),
                labels = c("low","high"))
 names(HICRIME) <- rownames(COL.OLD)
 jc6 <- jc.test(fx = HICRIME,
                listw = spdep::nb2listw(COL.nb,
                                 style = "B"))
summary(jc6)

spqdep documentation built on April 12, 2025, 1:50 a.m.