spdep (version 0.2-3)

sp.correlogram: Spatial correlogram

Description

Spatial correlograms for Moran's I and the autocorrelation coefficient, with print and plot helper functions.

Usage

sp.correlogram(neighbours, var, order = 1, method = "corr", style = "W", 
  randomisation = TRUE, zero.policy = FALSE, spChk=NULL)
plot.spcor(x, main, ylab, ylim, ...)
print.spcor(x, ...)

Arguments

neighbours

an object of class nb

var

a numeric vector

order

maximum lag order

method

"corr" for correlation, "I" for Moran's I

style

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

randomisation

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

zero.policy

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()

x

an object from sp.correlogram() of class spcor

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

Value

• returns a list of class spcor:
• 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

Cliff, A. D., Ord, J. K. 1981 Spatial processes, Pion, pp. 118--122, Martin, R. L., Oeppen, J. E. 1975 The identification of regional forecasting models using space-time correlation functions, Transactions of the Institute of British Geographers, 66, 95--118.

See Also

nblag, moran

Examples

data(nc.sids)
ft.SID74 <- sqrt(1000)*(sqrt(nc.sids$SID74/nc.sids$BIR74) +
  sqrt((nc.sids$SID74+1)/nc.sids$BIR74))
tr.SIDS74 <- ft.SID74*sqrt(nc.sids$BIR74)
names(tr.SIDS74) <- rownames(nc.sids)
print(sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="corr",
 zero.policy=TRUE))
print(sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="I",
 zero.policy=TRUE))
plot(sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="I",
 zero.policy=TRUE))
plot(sp.correlogram(ncCC89.nb, tr.SIDS74, order=8, method="corr",
 zero.policy=TRUE))
drop.no.neighs <- !(1:length(ncCC89.nb) %in% which(card(ncCC89.nb) == 0))
sub.ncCC89.nb <- subset(ncCC89.nb, drop.no.neighs)
plot(sp.correlogram(sub.ncCC89.nb, subset(tr.SIDS74,  drop.no.neighs),
 order=8, method="corr"))