| findSvcModel-class {svcR} | R Documentation |
reading data, computation of the svc model and cluster labelling
Objects can be created by calls of the form new("findSvcModel", ...).
or by calling the findSvcModel function.
lagrangeCoeff:"list" contains
the lagrange coefficients : lagrangeCoeff$A Matrice:"list" contains
variables names Matrice$var, attributes names Matrice$Att and data Matrice$MatMatriceK:"vector" contains
kernel matrixData:"matrix" contains
Data Matrix MinMaxXY:"vector" contains
min max values for first and second coordinatesMisClass:"vector" contains
missclassfied pointsdataFrame:"list" contains
the prefix name of data for files decodingfileIn:"character" contains
data file pathClassPoints:"vector" contains
class of grid pointsCx:"numeric" contains
the x column id of data matrixCy:"numeric" contains
the y column id of data matrixNu:"numeric" contains
nu value of the svc modelKNN:"numeric" contains
knn value for labellingSizeGrid:"numeric" contains
the size grid for labellingAroundNullVA:"numeric" contains
almost null value for lagrange coefficient estimationNumPoints:"vector" contains
value fo grid pointsNicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr
## exemple with iris data MetOpt = "optimStoch"; # optimisation method with randomization MetLab = "gridLabeling"; # grid labelling KernChoice = "KernGaussian"; # radial kernel Nu = 1.0; q = 2000; # lot of clusters K = 1; # only 1 nearest neighbour for clustering Cx = Cy = 0; # we use principal component analysis factors G = 20; # size of the grid for cluster labelling fileIn = ""; # fileIn migth be such as "D:/R/library/svc/", if NULL it will work on term data data(iris) fmc = findSvcModel( iris, MetOpt, MetLab, KernChoice, Nu, q, K, G, Cx, Cy); NP = getNumPoints(fmc); # values of the grid