Cluster Classifications for Local Indicators of Spatial Association and Local Indicators for Categorical Data

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. When obj is of class licd, a list of up to six factors for measures of local composition (analytical and permutation), local configuration (analytical and permutation), and combined measures, both the interaction of composition and configuration, and a simplified recoding of these.

Usage

hotspot(obj, ...)

# Default S3 method
hotspot(obj, ...)

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

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

# S3 method for class 'localC'
hotspot(obj, Prname, cutoff=0.005, p.adjust="fdr", droplevels=TRUE, ...)
# S3 method for class 'licd'
hotspot(obj, type = "both", cutoff = 0.05, p.adjust = "none", 
 droplevels = TRUE, control = list(), ...)

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() method 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

type

When obj is of class licd, default both, may also be comp for local composition or config for local configuration

control

When obj is of class licd, default binomial_sidak 2, binomial_overlap TRUE, jcm_sidak 3. binomial_overlap may be set FALSE to avoid the Binomial probability values summing to more than unity - the tests in Boots (2003, p. 141) do overlap (>= and <=), and the Šidák exponents may be set to 1 to prevent by-observation correction for 2 Binomial and 3 Normal probability values per observation

...

other arguments passed to methods.

Value

A factor showing so-called cluster classification for local indicators of spatial association. When obj is of class licd, a list of up to six factors for measures of local composition (analytical and permutation), local configuration (analytical and permutation), and combined measures, both the interaction of composition and configuration, and a simplified recoding of these.

See also

licd_multi

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