SPODT (version 0.9-1)

spodt: Spatial Oblique Decision Tree main function

Description

The spodt function provides a spatial partionning.

Usage

spodt(formula, data, weight = FALSE, graft = 0, level.max = 5, min.parent = 10, min.child = 5, rtwo.min = 0.001)

Arguments

formula

a formula, with a response but no interaction terms. The left hand side has to contain the quantitative response variable. The right hand side should contain the quantitative and qualitative variables to be split according to a non oblique algorithm. For single spatial analysis (with no cofactor) the right hand side should be ~1.

data

a SpatialPointsDataFrame containing the coordinates and the variables. spodt needs planar coordinates. Geographic coordinates have to be projected. Otherwise, euclidian coordinates can be used.

weight

logical value indicating whether the interclass variances should be weighted or not.

graft

if not equals to 0, a numerical value in ]0;1] indicating the minimal modification of R2global requires to grafted the final classes.

level.max

the maximal level of the regression tree above which the splitting algorithm is stopped.

min.parent

the minimal size of a node below which the splitting algorithm is stopped.

min.child

the minimal size of the children classes below which the split is refused and algorithm is stopped.

rtwo.min

the minimal value of R2 above which the node split is refused and algorithm is stopped. Specified as a numerical value between 0 and 1.

Value

The spodt function computes an object of class spodt with the different components of the classification tree, i.e. i) at each step: the point locations within each class, the R2 coefficients of the splitting line; ii) global results: the R2global (object@R2), the final partition (object@partition) including the graft results.

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.

See Also

spodt.tree, spodtSpatialLines, test.spodt

Examples

data(dataMALARIA)
#Example : number of malaria episodes per child at each household,
          #from November to December 2009, Bandiagara, Mali.
#Copyright: Pr Ogobara Doumbo, MRTC, Bamako, Mali. email: okd[at]icermali.org
coordinates(dataMALARIA)<-c("x","y")
class(dataMALARIA)
proj4string(dataMALARIA)<-"+proj=longlat +datum=WGS84 +ellps=WGS84"
dataMALARIA<-spTransform(dataMALARIA, CRS("+proj=merc +datum=WGS84 +ellps=WGS84"))

gr<-0.07   #graft parameter
rtw<-0.01 #rtwo.min
parm<-25  #min.parent
childm<-2 #min.child
lmx<-7 

sp<-spodt(dataMALARIA@data[,2]~1, dataMALARIA, weight=TRUE, graft=gr, min.ch=childm,
          min.parent=parm, level.max=lmx, rtwo.min=rtw)
sp
sp@R2

#the warning "root is a leaf" tells that no split can be provided by the
    # spodt function according to the splitting parameters