data(julliot)
par(mfrow = c(3,3))
for(k in 1:7)
area.plot(julliot$area,val = log(julliot$tab[,k]+1),
sub = names(julliot$tab)[k], csub = 2.5)
if (require(splancs, quiet = TRUE)){
par(mfrow = c(3,3))
for(k in 1:7)
s.image(julliot$xy, log(julliot$tab[,k]+1), kgrid = 3, span = 0.25,
sub = names(julliot$tab)[k], csub = 2.5)
}
par(mfrow = c(3,3))
for(k in 1:7) {
area.plot(julliot$area)
s.value(julliot$xy, scalewt(log(julliot$tab[,k]+1)),
sub = names(julliot$tab)[k],csub = 2.5, add.p = TRUE)
}
par(mfrow = c(3,3))
for(k in 1:7)
s.value(julliot$xy,log(julliot$tab[,k]+1),
sub = names(julliot$tab)[k], csub = 2.5)
if (require(spdep, quiet = TRUE)){
par(mfrow = c(1,1))
neig0 <- nb2neig(dnearneigh(as.matrix(julliot$xy), 1, 1.8))
s.label(julliot$xy, neig = neig0, clab = 0.75, incl = FALSE,
addax = FALSE, grid = FALSE)
gearymoran(neig.util.LtoG(neig0), log(julliot$tab+1))
orthogram(log(julliot$tab[,3]+1), ortho = scores.neig(neig0),
nrepet = 9999)}
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