Cluster classifications for local indicators of spatial association

Used to return a factor showing so-called cluster classification for local indicators of spatial association for local Moran's I, local Geary's C (and its multivariate variant) and local Getis-Ord G. This factor vector can be added to a spatial object for mapping.

hotspot(obj, ...)

# S3 method for default
hotspot(obj, ...)

# S3 method for localmoran
hotspot(obj, Prname, cutoff=0.005, quadrant.type="mean",
 p.adjust="fdr", droplevels=TRUE, ...)
# S3 method for summary.localmoransad
hotspot(obj, Prname, cutoff=0.005,
 quadrant.type="mean", p.adjust="fdr", droplevels=TRUE, ...)
# S3 method for data.frame.localmoranex
hotspot(obj, Prname, cutoff=0.005,
 quadrant.type="mean", p.adjust="fdr", droplevels=TRUE, ...)

# S3 method for localG
hotspot(obj, Prname, cutoff=0.005, p.adjust="fdr", droplevels=TRUE, ...)

# S3 method for localC
hotspot(obj, Prname, cutoff=0.005, p.adjust="fdr", droplevels=TRUE, ...)

Arguments

obj

An object of class localmoran, localC or localG

Prname

A character string, the name of the column containing the probability values to be classified by cluster type if found “interesting”

cutoff

Default 0.005, the probability value cutoff larger than which the observation is not found “interesting”

p.adjust

Default "fdr", the p.adjust() methood used, one of c("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none")

droplevels

Default TRUE, should empty levels of the input cluster factor be dropped

quadrant.type

Default "mean", for "localmoran" objects only, can be c("mean", "median", "pysal") to partition the Moran scatterplot; "mean" partitions on the means of the variable and its spatial lag, "median" on medians of the variable and its spatial lag, "pysal" at zero for the centred variable and its spatial lag

...

other arguments passed to methods.

Value

A factor showing so-called cluster classification for local indicators of spatial association.

Author

Roger Bivand

Examples

orig <- spData::africa.rook.nb
listw <- nb2listw(orig)
x <- spData::afcon$totcon

set.seed(1)
C <- localC_perm(x, listw)
Ch <- hotspot(C, Prname="Pr(z != E(Ci)) Sim", cutoff=0.05, p.adjust="none")
table(addNA(Ch))
#> 
#> High-High   Low-Low      <NA> 
#>         4         1        37 
set.seed(1)
I <- localmoran_perm(x, listw)
Ih <- hotspot(I, Prname="Pr(z != E(Ii)) Sim", cutoff=0.05, p.adjust="none")
table(addNA(Ih))
#> 
#> High-High      <NA> 
#>         6        36 
Is <- summary(localmoran.sad(lm(x ~ 1), nb=orig))
Ish <- hotspot(Is, Prname="Pr. (Sad)", cutoff=0.05, p.adjust="none")
table(addNA(Ish))
#> 
#> High-High      <NA> 
#>         5        37 
Ie <- as.data.frame(localmoran.exact(lm(x ~ 1), nb=orig))
Ieh <- hotspot(Ie, Prname="Pr. (exact)", cutoff=0.05, p.adjust="none")
table(addNA(Ieh))
#> 
#> High-High      <NA> 
#>         5        37 
set.seed(1)
G <- localG_perm(x, listw)
Gh <- hotspot(G, Prname="Pr(z != E(Gi)) Sim", cutoff=0.05, p.adjust="none")
table(addNA(Gh))
#> 
#> High <NA> 
#>    6   36