hotspotmap: Cluster classifications for local indicators of spatial...

In spdep: Spatial Dependence: Weighting Schemes, Statistics

Cluster classifications for local indicators of spatial association

Description

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.

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, ...)

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(s)

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))
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))
Is <- summary(localmoran.sad(lm(x ~ 1), nb=orig))
Ish <- hotspot(Is, Prname="Pr. (Sad)", cutoff=0.05, p.adjust="none")
table(addNA(Ish))
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))
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))

spdep documentation built on Nov. 23, 2023, 9:06 a.m.