# Calinski-Harabasz pseudo F-statistic based on distance matrix Calculates Calinski-Harabasz pseudo F-statistic based on distance matrix ```r index.G1d (d,cl) ``` ## Arguments - `d`: distance matrix (see `dist_SDA`) - `cl`: a vector of integers indicating the cluster to which each object is allocated ## Returns value of Calinski-Harabasz pseudo F-statistic based on distance matrix ## Author(s) Andrzej Dudek andrzej.dudek@ue.wroc.pl , Justyna Wilk Department of Econometrics and Computer Science, Wroclaw University of Economics, Poland ## References Calinski, T., Harabasz, J. (1974), **A dendrite method for cluster analysis**, "Communications in Statistics", vol. 3, 1-27. Everitt, B.S., Landau, E., Leese, M. (2001), **Cluster analysis**, Arnold, London, p. 103. ISBN 9780340761199. Gordon, A.D. (1999), **Classification**, Chapman & Hall/CRC, London, p. 62. ISBN 9781584880134. Milligan, G.W., Cooper, M.C. (1985), **An examination of procedures of determining the number of cluster in a data set**, "Psychometrika", vol. 50, no. 2, 159-179. Available at: tools:::Rd_expr_doi("10.1007/BF02294245") . Diday, E., Noirhomme-Fraiture, M. (eds.) (2008), **Symbolic Data Analysis with SODAS Software**, John Wiley & Sons, Chichester, pp. 236-262. Dudek, A. (2007), **Cluster Quality Indexes for Symbolic Classification. An Examination**, In: H.H.-J. Lenz, R. Decker (Eds.), Advances in Data Analysis, Springer-Verlag, Berlin, pp. 31-38. Available at: tools:::Rd_expr_doi("10.1007/978-3-540-70981-7_4") . ## Details See file $R_LIBS_USER\symnbolicDA\pdf\indexG1d_details.pdf for further details ## See Also `DClust`, `SClust`; `index.G2`, `index.G3`, `index.S`, `index.H`,`index.KL`,`index.Gap`, `index.DB` in `clusterSim` library ## Examples ```r # LONG RUNNING - UNCOMMENT TO RUN # Example 1 #library(stats) #data("cars",package="symbolicDA") #x<-cars #d<-dist_SDA(x, type="U_2") #wynik<-hclust(d, method="ward", members=NULL) #clusters<-cutree(wynik, 4) #G1d<-index.G1d(d, clusters) #print(G1d) # Example 2 #data("cars",package="symbolicDA") #md <- dist_SDA(cars, type="U_3", gamma=0.5, power=2) # nc - number_of_clusters #min_nc=2 #max_nc=10 #res <- array(0,c(max_nc-min_nc+1,2)) #res[,1] <- min_nc:max_nc #clusters <- NULL #for (nc in min_nc:max_nc) #{ #cl2 <- pam(md, nc, diss=TRUE) #res[nc-min_nc+1,2] <- G1d <- index.G1d(md,cl2$clustering) #clusters <- rbind(clusters, cl2$clustering) #} #print(paste("max G1d for",(min_nc:max_nc)[which.max(res[,2])],"clusters=",max(res[,2]))) #print("clustering for max G1d") #print(clusters[which.max(res[,2]),]) #write.table(res,file="G1d_res.csv",sep=";",dec=",",row.names=TRUE,col.names=FALSE) #plot(res, type="p", pch=0, xlab="Number of clusters", ylab="G1d", xaxt="n") #axis(1, c(min_nc:max_nc)) ```

index.G1d.rd function