SPODT (version 0.9-1)

dataSQUARE2: Example data file

Description

Simulated data for SPODT functions. To assess the SpODT algorithm to detect a rotated square shape situation: very high values within a rotated square cluster.

Usage

data(dataSQUARE2)

Format

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

Details

• i: identification of each localization.

• x: longitudinal coordinate.

• y: latitudinal coordinate.

• z: the dependant variable.

References

• 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.

Examples

data(dataSQUARE2)
dataset<-dataSQUARE2
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)