# 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. ```r 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()` 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 - `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. ## Returns 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` ## Examples ```r 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)) ``` ## Author(s) Roger Bivand

hotspotmap function