Spatial cross correlation
Calculates univariate or bivariate spatial cross-correlation using local Moran's-I (LISA), following Chen (2015)
crossCorrelation(
x,
y = NULL,
coords = NULL,
w = NULL,
type = c("LSCI", "GSCI"),
k = 1000,
dist.function = "inv.power",
scale.xy = TRUE,
scale.partial = FALSE,
scale.matrix = FALSE,
alpha = 0.05,
clust = TRUE,
return.sims = FALSE
)x |
Vector of x response variables |
y |
Vector of y response variables, if not specified the univariate statistic is returned |
coords |
A matrix of coordinates corresponding to [x,y], only used if k = NULL. Can also be an sp object with relevant x,y coordinate slot (ie., points or polygons) |
w |
Spatial neighbors/weights in matrix format. Dimensions must match [n(x),n(y)] and be symmetrical. If w is not defined then a default method is used. |
type |
c("LSCI","GSCI") Return Local Spatial Cross-correlation Index (LSCI) or Global Spatial cross-correlation Index (GSCI) |
k |
Number of simulations for calculating permutation distribution under the null hypothesis of no spatial autocorrelation |
dist.function |
("inv.power", "neg.exponent") If w = NULL, the default method for deriving spatial weights matrix, options are: inverse power or negative exponent |
scale.xy |
(TRUE/FALSE) scale the x,y vectors, if FALSE it is assumed that they are already scaled following Chen (2015) |
scale.partial |
(FALSE/TRUE) rescale partial spatial autocorrelation statistics [-1 - 1] |
scale.matrix |
(FALSE/TRUE) If a neighbor/distance matrix is passed, should it be scaled using [w/sum(w)] |
alpha |
= 0.05 confidence interval (default is 95 pct) |
clust |
(FALSE/TRUE) Return approximated lisa clusters |
return.sims |
(FALSE/TRUE) Return randomizations vector n = k |
When not simulated k=0, a list containing:
I Global autocorrelation statistic
SCI A data.frame with two columns representing the xy and yx autocorrelation
nsim value of NULL to represent p values were derived from observed data (k=0)
p Probability based observations above/below confidence interval
t.test Probability based on t-test
clusters If "clust" argument TRUE, vector representing LISA clusters
when simulated (k>0), a list containing:
I Global autocorrelation statistic
SCI A data.frame with two columns representing the xy and yx autocorrelation
nsim value representing number of simulations
global.p p-value of global autocorrelation statistic
local.p Probability based simulated data using successful rejection of t-test
range.p Probability based on range of probabilities resulting from paired t-test
clusters If "clust" argument TRUE, vector representing lisa clusters
Chen., Y. (2015) A New Methodology of Spatial Cross-Correlation Analysis. PLoS One 10(5):e0126158. doi:10.1371/journal.pone.0126158
library(sp)
library(spdep)
data(meuse)
coordinates(meuse) <- ~x+y
#### Providing a neighbor contiguity spatial weights matrix
all.linked <- max(unlist(nbdists(knn2nb(knearneigh(coordinates(meuse))),
coordinates(meuse))))
nb <- nb2listw(dnearneigh(meuse, 0, all.linked), style = "B", zero.policy = TRUE)
Wij <- as.matrix( as(nb, "symmetricMatrix") )
I <- crossCorrelation(meuse$zinc, meuse$copper, w = Wij,
clust=TRUE, k=99)
meuse$lisa <- I$SCI[,"lsci.xy"]
spplot(meuse, "lisa")
#meuse$lisa.clust <- as.factor(I$cluster)
#spplot(meuse, "lisa.clust")
#### Using a default spatial weights matrix method (inverse power function)
I <- crossCorrelation(meuse$zinc, meuse$copper, coords = coordinates(meuse),
clust = TRUE, k=99)
meuse$lisa <- I$SCI[,"lsci.xy"]
spplot(meuse, "lisa")
#meuse$lisa.clust <- as.factor(I$cluster)
# spplot(meuse, "lisa.clust")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.