Utility function to facilitate the spatial diagnostic of a classification
Calculate the following indicators: Moran I index (spdep::moranI) for each column of the membership matrix, Join count test (spdep::joincount.multi) for the most likely groups of each datapoint, Spatial consistency index (see function spConsistency) and the Elsa statistic (see function calcElsa). Note that if the FCMres object given was constructed with rasters, the joincount statistic is not calculated and no p-values are provided for the Moran I indices.
spatialDiag(
object,
nblistw = NULL,
window = NULL,
undecided = NULL,
matdist = NULL,
nrep = 50
)A FCMres object, typically obtained from functions CMeans, GCMeans, SFCMeans, SGFCMeans. Can also be a simple membership matrix.
A list.w object describing the neighbours typically produced by the spdep package. Required if data is a dataframe, see the parameter window if you use a list of rasters as input. Can also be NULL if object is a FCMres object.
If rasters were used for the classification, the window must be specified instead of a list.w object. Can also be NULL if object is a FCMres object.
A float giving the threslhod to detect undecided observations. An observation is undecided if its maximum membership value is bellow this float. If null, no observations are undecided.
A matrix representing the dissimilarity between the clusters. The matrix must be squared and the diagonal must be filled with zeros.
An integer indicating the number of permutation to do to simulate the random distribution of the spatial inconsistency
A named list with :
MoranValues : the moran I values for each column of the membership matrix (spdep::MoranI)
JoinCounts : the result of the join count test calculated with the most likely group for each datapoint (spdep::joincount.multi)
SpConsist : the mean value of the spatial consistency index (the lower, the better, see ?spConsistency for details)
data(LyonIris)
AnalysisFields <-c("Lden","NO2","PM25","VegHautPrt","Pct0_14","Pct_65","Pct_Img",
"TxChom1564","Pct_brevet","NivVieMed")
dataset <- sf::st_drop_geometry(LyonIris[AnalysisFields])
queen <- spdep::poly2nb(LyonIris,queen=TRUE)
Wqueen <- spdep::nb2listw(queen,style="W")
result <- SFCMeans(dataset, Wqueen,k = 5, m = 1.5, alpha = 1.5, standardize = TRUE)
spatialDiag(result, undecided=0.45, nrep=30)