spcorrelogram.bi {bispdep}

Bivariate spatial correlogram

Description

Bivariate spatial correlograms for Moran's Ixy and the autocorrelation coefficient, with print and plot helper functions.

Usage

spcorrelogram.bi(neighbours, varX, varY, order = 1, method = "corr",
                 style = "W", randomisation = TRUE, zero.policy = NULL,
                 spChk=NULL, alternative = "greater", drop.EI2=FALSE)
## S3 method for class 'spcorbi'
plot(x, main, ylab, ylim, ...)
## S3 method for class 'spcorbi'
print(x, p.adj.method="none", ...)

Arguments

neighbours

an object of class nb

varX

a numeric vector of the variable x

varY

a numeric vector of the variable y

order

maximum lag order

method

"corr" for correlation, "I" for Moran's Ixy, "C" for Geary's Cxy

style

style can take values W, B, C, and S

randomisation

variance of I or C calculated under the assumption of randomisation, if FALSE normality

zero.policy

default NULL, use global option value; if FALSE stop with error for any empty neighbour sets, if TRUE permit the weights list to be formed with zero-length weights vectors

spChk

should the data vector names be checked against the spatial objects for identity integrity, TRUE, or FALSE, default NULL to use get.spChkOption()

alternative

a character string specifying the alternative hypothesis, must be one of greater (default), less or two.sided.

drop.EI2

default FALSE, if TRUE, emulate CrimeStat <= 4.02

x

an object from spcorrelogram.bi() of class spcorbi

p.adj.method

correction method as in p.adjust

main

an overall title for the plot

ylab

a title for the y axis

ylim

the y limits of the plot

...

further arguments passed through

Details

The print function also calculates the standard deviates of Bivariate Moran's Ixy or Geary's Cxy and a two-sided probability value, optionally using p.adjust to correct by the nymber of lags. The plot function plots a bar from the estimated value of Bivariate Moran's Ixy, or Geary's Cxy to +/- twice the square root of its variance (in previous releases only once, not twice). The table includes the count of included observations in brackets after the lag order. Care must be taken when interpreting the results, since increasing the order of the lag tends to include fewer observations.

Value

returns a list of class spcorbi:

res

for "corr" a vector of values; for "I", a matrix of estimates of "I", expectations, and variances

method

"I" or "corr"

cardnos

list of tables of neighbour cardinalities for the lag orders used

var

variable name

References

Czaplewski, R.L., Reich, R.M. 1993. Expected value and variance of Moran's bivariate spatial autocorrelation statistic for a permutation test, Research paper RM-309, Fort Collins, CO U.S. Department of Agriculture, Forest Service, Rocky Mountain Forest and Range Experiment Station, p, 13.

See Also

nblag, moran.bi, p.adjust

Examples

library(spdep)
data(columbus)
data(oldcol)
columbus <- st_read(system.file("shapes/columbus.shp", package="spData")[1], quiet=TRUE)
plot(st_geometry(columbus))
col_nbq <- poly2nb(columbus)

Cspcb <- spcorrelogram.bi(col_nbq,columbus$CRIME,columbus$INC,order=7,
                          method="corr",zero.policy=TRUE,alternative="two.sided")
print(Cspcb)
plot(Cspcb)

Ispcb <- spcorrelogram.bi(col_nbq,columbus$CRIME,columbus$INC,order=7,
                          method="I",zero.policy=TRUE,alternative="two.sided")
print(Ispcb)
plot(Ispcb)

Cspcb <- spcorrelogram.bi(col_nbq,columbus$CRIME,columbus$INC,order=7,
                          method="C",zero.policy=TRUE,alternative="two.sided")
print(Ispcb)
plot(Ispcb)