SPODT (version 0.9-1)

dataSQUARE1_5: Example data file

Description

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

Usage

data(dataSQUARE1_5)

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(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)