ClusterEval {svcR} | R Documentation |
Set of functions making evaluation. It requires a class column in data.
## S4 method for signature 'findModelCluster': ClusterEval.Pvalue(x=NULL) ## S4 method for signature 'matrix': DataSampling(DatMat=matrix()) ## S4 method for signature 'findModelCluster': ClusterEval.crossval(x=NULL) ## S4 method for signature 'findModelCluster': ClusterEval.final(x=NULL) ## S4 method for signature 'findModelCluster': ClusterEval.scalable(x=NULL) ## S4 method for signature 'findModelCluster': ClusterEval.clusterable(x=NULL)
x |
a findModelCluster object |
DatMat |
iris matrix obtain from a run with findModelCluster function |
DataSampling, ClusterEval.crossval, ClusterEval.final, ClusterEval.scalable and ClusterEval.clusterable are based only on iris data. ClusterEval.scalable make evaluation building a matrix adding line by line to current matrix from iris matrix.ClusterEval.clusterable makes evaluation with a set of q paramter values and a set of nu parameter values.
return a numeric or a list of numeric meaning a precision value between 0 and 100 for the method ClusterEval.scalable and ClusterEval.clusterable it return a list of precision values.
Nicolas Turenne - INRA France nicolas.turenne@jouy.inra.fr
N.Turenne , Some Heuristics to speed-up Support Vector Clustering , technical report 2006, INRA, France http://migale.jouy.inra.fr/~turenne/svc.pdf
## exemple with iris data # generation of the svc model fmc = findModelCluster.Test(); # catch evaluation value P = ClusterEval.Pvalue( fmc );