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を用いたクラスター分析 - eonet.ne.jp

1 R Excel 8 1 ( ) R R ( ) (complete ) # data <- (" ", header=T) #1- (complete) c1 <- hclust( (1-cor(t(data))), method = "complete") # c2 <- hclust( (1-cor(data)), method = "complete") # plot(c1, hang=-1) hang=-1 plot(c2, hang=-1) hang=-1 2 # correlationDistanceTable <- (1-cor( ((data)))) # # pdf file pdf(file=" ") # pdf height=10, width=10 heatmap(x= ( ),Colv= (c2),Rowv= (c1), distfun=correlationDistanceTable, hclustfun=function(x) hclust(correlationDistanceTable),cexRow= , margins = c(6,10)) () (1-cor(t(data))) dist(data) (1-cor(data)) dist(t(data)) hclust method single (UPGMA ) average ward cexRow +1 / log10( ) margins=()

2 #ヒートマップ用に距離行列を整える correlationDistanceTable <- as.dist(1-cor(as.matrix((data)))) # 距離行列用 #ヒートマップをpdfに出力する file名は適宜変更する

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Transcription of を用いたクラスター分析 - eonet.ne.jp

1 1 R Excel 8 1 ( ) R R ( ) (complete ) # data <- (" ", header=T) #1- (complete) c1 <- hclust( (1-cor(t(data))), method = "complete") # c2 <- hclust( (1-cor(data)), method = "complete") # plot(c1, hang=-1) hang=-1 plot(c2, hang=-1) hang=-1 2 # correlationDistanceTable <- (1-cor( ((data)))) # # pdf file pdf(file=" ") # pdf height=10, width=10 heatmap(x= ( ),Colv= (c2),Rowv= (c1), distfun=correlationDistanceTable, hclustfun=function(x) hclust(correlationDistanceTable),cexRow= , margins = c(6,10)) () (1-cor(t(data))) dist(data) (1-cor(data)) dist(t(data)) hclust method single (UPGMA ) average ward cexRow +1 / log10( ) margins=( ) data <- (" ", header=T) c1 <- hclust( (1-cor(t(data))), method = "complete") c2 <- hclust( (1-cor(data)), method = "complete") correlationDistanceTable <- (1-cor( ((data)))) pdf(file=" ") heatmap(x= (data),Colv= (c2),Rowv= (c1), distfun=correlationDistanceTable, hclustfun=function(x))

2 Hclust(correlationDistanceTable), cexRow= , margins = c(6,10)) ()


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