plot.SpatClust: Plot a set of two-dimensional points with a cluster...
In MathieuEmily/SpatialClustering: Detection of clusters of bi-dimensional points

Description Usage Arguments Details Value References See Also Examples

View source: R/plot.SpatClust.R

plot.SpatClust is a graphical function that the visualization of the clusters.

1 2	## S3 method for class 'SpatClust' plot(x,method=c("CH","Seg"),plot.dendro=TRUE,plot.covariate=FALSE)

`x`	`SpatClust`. Given as output of `SpatialClustering`
`method`	A character string indicating the type of cluster visualization
`plot.dendro`	A boolean. Should the dendrogram be displayed ? (See Details for more information)
`plot.covariate`	A boolean. Should the map of the covariate be displayed ? (See Details for more information)

If method="CH", this function draws the convex hull of each cluster. If method="Seg", plot.SpatClust draws an edge between all pairs of points that are connected (i.e. closer than expected under the underlying homogeneous or inhomogeneous Poisson process). If plot.dendro=TRUE, the graphical output is divided into two parts: one part with the dendrogram and one part with two-dimensional clusters. If plot.dendro=FALSE, two-dimensional clusters are only displayed. If plot.covariate=TRUE, the two-dimensional clusters are accompanied with a image of the covariate on the window of interest.

The form of the value returned by plot depends on the class of its argument. See Details.

A. Bar-Hen, M. Emily and N. Picard. (2015) Spatial Cluster Detection Using Nearest Neighbour Distance, Spatial Statistics, Vol. 14, pages 400-411.
R. Tibshirani, G. Walther and T. Hastie (2001) Estimating the number of data clusters via the gap statistic. J. Roy. Stat. Soc. B 63, 411<e2><80><93>423.

generateListTandP, getClusters, getMatDist, SpatialClustering

## Example of a study of tree location 
data(dataExample)


## Extraction of the data and the window
dDicor <- dataExample$data
w0 <- dataExample$w0

#######
## Identification of the clusters estimated with SpatialClustering in the Homogeneous case
#######

set.seed(123)
res <- SpatialClustering(data=dDicor,window=w0)

## Various plotting possibilities
plot(res)
plot(res,plot.dendro=FALSE)
plot(res,method="Seg",plot.dendro=FALSE)
plot(res,method="Seg",plot.dendro=TRUE)

#######
## Identification of the clusters estimated with SpatialClustering in the Inhomogeneous case
#######

## Extraction of the covariate
Z.Pente <- dataExample$Z.Pente

## Estimation of the cluster
set.seed(345)
res.I <- SpatialClustering(data=dDicor,window=w0,Homogeneous=FALSE,Z=Z.Pente)
plot(res.I)