Description Usage Format Details References Examples
Simulated data for SPODT functions. To assess the SpODT algorithm to detect a rotated square shape situation: high values within a rotated square cluster.
1 |
A data frame with 300 observations on the following 4 variables (300 locations).
i
a numeric vector
x
a numeric vector
y
a numeric vector
z
a numeric vector
i
: identification of each localization.
x
: longitudinal coordinate.
y
: latitudinal coordinate.
z
: the dependant variable.
Gaudart J, Graffeo N, Coulibaly D, Barbet G, Rebaudet S, Dessay N, Doumbo O, Giorgi R. SPODT: An R Package to Perform Spatial Partitioning. Journal of Statistical Software 2015;63(16):1-23. http://www.jstatsoft.org/v63/i16/
Gaudart J, Poudiougou B, Ranque S, Doumbo O. Oblique decision trees for spatial pattern detection: optimal algorithm and application to malaria risk. BMC Medical Research Methodology 2005;5:22
Gaudart J, Giorgi R, Poudiougou B, Toure O, Ranque S, Doumbo O, Demongeot J. Detection de clusters spatiaux sans point source predefini: utilisation de cinq methodes et comparaison de leurs resultats. Revue d'Epidemiologie et de Sante Publique 2007;55(4):297-306
Fichet B, Gaudart J, Giusiano B. Bivariate CART with oblique regression trees. International conference of Data Science and Classification, International Federation of Classification Societies, Ljubljana, Slovenia, July 2006.
1 2 3 4 5 6 7 8 9 10 | data(dataSQUARE1_5)
dataset<-dataSQUARE1_5
coordinates(dataset)<-c("x","y")
#coordinates are planar ones
#Example : split the area without covariable analysis
sp<-spodt(dataset@data$z~1, dataset, weight=FALSE, graft=0.2)
ssp<-spodtSpatialLines(sp,dataset)
plot(ssp)
points(dataset,cex=dataset@data$z)
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