标签不会出现在 R 中的层次聚类图（树状图）中
Labels don't appear in hierarchical clustering plot (dendogram) in R
我想使用R的排列分布聚类包（https://cran.r-project.org/web/packages/pdc/pdc.pdf) for multivariate time series clustering. After using pdclust method(page 11 of the url pdf ) for hierarchical clustering, I plotted the dendogram using plot method (page 11 again). There are 60 samples. So, in the plot (Hierarchical clustering plot
), 有 60 个时间序列但没有标记。当我尝试指定标签向量而不是 labels = NULL 时，我总是会收到此错误 "Error in graphics:::plotHclust(n1, merge, height, order(x$order), hang, : invalid dendrogram input"。任何帮助将不胜感激。这是我的代码：
data1 <- read.csv(file="file_PID_1_1Apr_00-03.csv",head=FALSE,sep=",")
data2 <- read.csv(file="file_PID_2_1Apr_00-03.csv",head=FALSE,sep=",")
data3 <- read.csv(file="file_PID_3_1Apr_00-03.csv",head=FALSE,sep=",")
data4 <- read.csv(file="file_PID_4_1Apr_00-03.csv",head=FALSE,sep=",")
data5 <- read.csv(file="file_PID_5_1Apr_00-03.csv",head=FALSE,sep=",")
data6 <- read.csv(file="file_PID_6_1Apr_00-03.csv",head=FALSE,sep=",")
data7 <- read.csv(file="file_PID_7_1Apr_00-03.csv",head=FALSE,sep=",")
data8 <- read.csv(file="file_PID_8_1Apr_00-03.csv",head=FALSE,sep=",")
data9 <- read.csv(file="file_PID_1_1Apr_03-06.csv",head=FALSE,sep=",")
data10 <- read.csv(file="file_PID_2_1Apr_03-06.csv",head=FALSE,sep=",")
data11 <- read.csv(file="file_PID_3_1Apr_03-06.csv",head=FALSE,sep=",")
data12 <- read.csv(file="file_PID_4_1Apr_03-06.csv",head=FALSE,sep=",")
data13 <- read.csv(file="file_PID_5_1Apr_03-06.csv",head=FALSE,sep=",")
data14 <- read.csv(file="file_PID_6_1Apr_03-06.csv",head=FALSE,sep=",")
data15 <- read.csv(file="file_PID_7_1Apr_03-06.csv",head=FALSE,sep=",")
data16 <- read.csv(file="file_PID_8_1Apr_03-06.csv",head=FALSE,sep=",")
data17 <- read.csv(file="file_PID_1_1Apr_06-09.csv",head=FALSE,sep=",")
data18 <- read.csv(file="file_PID_2_1Apr_06-09.csv",head=FALSE,sep=",")
data19 <- read.csv(file="file_PID_3_1Apr_06-09.csv",head=FALSE,sep=",")
data20 <- read.csv(file="file_PID_4_1Apr_06-09.csv",head=FALSE,sep=",")
data21 <- read.csv(file="file_PID_5_1Apr_06-09.csv",head=FALSE,sep=",")
data22 <- read.csv(file="file_PID_6_1Apr_06-09.csv",head=FALSE,sep=",")
data23 <- read.csv(file="file_PID_7_1Apr_06-09.csv",head=FALSE,sep=",")
data24 <- read.csv(file="file_PID_8_1Apr_06-09.csv",head=FALSE,sep=",")
data25 <- read.csv(file="file_PID_1_1Apr_09-12.csv",head=FALSE,sep=",")
data26 <- read.csv(file="file_PID_2_1Apr_09-12.csv",head=FALSE,sep=",")
data27 <- read.csv(file="file_PID_3_1Apr_09-12.csv",head=FALSE,sep=",")
data28 <- read.csv(file="file_PID_4_1Apr_09-12.csv",head=FALSE,sep=",")
data29 <- read.csv(file="file_PID_5_1Apr_09-12.csv",head=FALSE,sep=",")
data30 <- read.csv(file="file_PID_6_1Apr_09-12.csv",head=FALSE,sep=",")
data31 <- read.csv(file="file_PID_7_1Apr_09-12.csv",head=FALSE,sep=",")
data32 <- read.csv(file="file_PID_8_1Apr_09-12.csv",head=FALSE,sep=",")
data33 <- read.csv(file="file_PID_1_1Apr_12-15.csv",head=FALSE,sep=",")
data34 <- read.csv(file="file_PID_2_1Apr_12-15.csv",head=FALSE,sep=",")
data35 <- read.csv(file="file_PID_3_1Apr_12-15.csv",head=FALSE,sep=",")
data36 <- read.csv(file="file_PID_4_1Apr_12-15.csv",head=FALSE,sep=",")
data37 <- read.csv(file="file_PID_5_1Apr_12-15.csv",head=FALSE,sep=",")
data38 <- read.csv(file="file_PID_6_1Apr_12-15.csv",head=FALSE,sep=",")
data39 <- read.csv(file="file_PID_7_1Apr_12-15.csv",head=FALSE,sep=",")
data40 <- read.csv(file="file_PID_8_1Apr_12-15.csv",head=FALSE,sep=",")
data41 <- read.csv(file="file_PID_2_1Apr_15-18.csv",head=FALSE,sep=",")
data42 <- read.csv(file="file_PID_3_1Apr_15-18.csv",head=FALSE,sep=",")
data43 <- read.csv(file="file_PID_4_1Apr_15-18.csv",head=FALSE,sep=",")
data44 <- read.csv(file="file_PID_6_1Apr_15-18.csv",head=FALSE,sep=",")
data45 <- read.csv(file="file_PID_7_1Apr_15-18.csv",head=FALSE,sep=",")
data46 <- read.csv(file="file_PID_8_1Apr_15-18.csv",head=FALSE,sep=",")
data47 <- read.csv(file="file_PID_1_1Apr_18-21.csv",head=FALSE,sep=",")
data48 <- read.csv(file="file_PID_2_1Apr_18-21.csv",head=FALSE,sep=",")
data49 <- read.csv(file="file_PID_3_1Apr_18-21.csv",head=FALSE,sep=",")
data50 <- read.csv(file="file_PID_4_1Apr_18-21.csv",head=FALSE,sep=",")
data51 <- read.csv(file="file_PID_6_1Apr_18-21.csv",head=FALSE,sep=",")
data52 <- read.csv(file="file_PID_7_1Apr_18-21.csv",head=FALSE,sep=",")
data53 <- read.csv(file="file_PID_8_1Apr_18-21.csv",head=FALSE,sep=",")
data54 <- read.csv(file="file_PID_1_1Apr_21-24.csv",head=FALSE,sep=",")
data55 <- read.csv(file="file_PID_2_1Apr_21-24.csv",head=FALSE,sep=",")
data56 <- read.csv(file="file_PID_3_1Apr_21-24.csv",head=FALSE,sep=",")
data57 <- read.csv(file="file_PID_4_1Apr_21-24.csv",head=FALSE,sep=",")
data58 <- read.csv(file="file_PID_6_1Apr_21-24.csv",head=FALSE,sep=",")
data59 <- read.csv(file="file_PID_7_1Apr_21-24.csv",head=FALSE,sep=",")
data60 <- read.csv(file="file_PID_8_1Apr_21-24.csv",head=FALSE,sep=",")
list <- array(0,dim=c(720,60,4))
myfunc <- function(j,i,k){
if (j == 1) return (data1[i,k])
else if (j==2) return (data2[i,k])
else if (j==3) return (data17[i,k])
else if (j==4) return (data9[i,k])
else if (j==5) return (data5[i,k])
else if (j==6) return (data6[i,k])
else if (j==7) return (data7[i,k])
else if (j==8) return (data8[i,k])
else if (j==9) return (data9[i,k])
else if (j==10) return (data10[i,k])
else if (j==11) return (data11[i,k])
else if (j==12) return (data12[i,k])
else if (j==13) return (data13[i,k])
else if (j==14) return (data14[i,k])
else if (j==15) return (data15[i,k])
else if (j==16) return (data16[i,k])
else if (j==17) return (data17[i,k])
else if (j==18) return (data18[i,k])
else if (j==19) return (data19[i,k])
else if (j==20) return (data20[i,k])
else if (j==21) return (data21[i,k])
else if (j==22) return (data22[i,k])
else if (j==23) return (data23[i,k])
else if (j==24) return (data24[i,k])
else if (j==25) return (data25[i,k])
else if (j==26) return (data26[i,k])
else if (j==27) return (data27[i,k])
else if (j==28) return (data28[i,k])
else if (j==29) return (data29[i,k])
else if (j==30) return (data30[i,k])
else if (j==31) return (data31[i,k])
else if (j==32) return (data32[i,k])
else if (j==33) return (data33[i,k])
else if (j==34) return (data34[i,k])
else if (j==35) return (data35[i,k])
else if (j==36) return (data36[i,k])
else if (j==37) return (data37[i,k])
else if (j==38) return (data38[i,k])
else if (j==39) return (data39[i,k])
else if (j==40) return (data40[i,k])
else if (j==41) return (data41[i,k])
else if (j==42) return (data42[i,k])
else if (j==43) return (data43[i,k])
else if (j==44) return (data44[i,k])
else if (j==45) return (data45[i,k])
else if (j==46) return (data46[i,k])
else if (j==47) return (data47[i,k])
else if (j==48) return (data48[i,k])
else if (j==49) return (data49[i,k])
else if (j==50) return (data50[i,k])
else if (j==51) return (data51[i,k])
else if (j==52) return (data52[i,k])
else if (j==53) return (data53[i,k])
else if (j==54) return (data54[i,k])
else if (j==55) return (data55[i,k])
else if (j==56) return (data56[i,k])
else if (j==57) return (data57[i,k])
else if (j==58) return (data58[i,k])
else if (j==59) return (data59[i,k])
else if (j==60) return (data60[i,k])
}
list <- array(0,dim=c(720,60,4))
for(i in 1:720){
for (j in 1:60){
list[i,j,1] <- myfunc(j,i,6)
list[i,j,2] <- myfunc(j,i,7)
list[i,j,3] <- myfunc(j,i,8)
list[i,j,4] <- myfunc(j,i,9)
}
}
library("pdc")
clustering <- pdclust(list)
plot(clustering, labels= NULL, type="rectangle", timeseries.as.labels = T, p.values=T)
我创建了一个没有数据文件的精简版代码，这样就可以更轻松地讨论您的问题。在这里，我创建了 60 个具有 4 个维度和 720 个时间点的时间序列（就像您所做的那样）。只是我模拟了一半的试验来自随机正态，另一半来自具有叠加随机正态的线性趋势。因此，对于 pdc，它们显然应该分为两组。这是代码：
require("pdc")
# make this replicable by setting a random seed
set.seed(7823)
# 60 TS each with 4 dimensions and 720 timepoints
# half of them are random uniform other half are a mix of random uniform
# and linear increase
list <- array(0,dim=c(720,60,4))
for (i in 1:30) {
for (j in 1:4) {
list[,i,j] <- rnorm(n = 720)
list[,i+30,j] <- rnorm(n=720)+1:720
}
}
cols <- c(rep("red",30),rep("blue",30))
labels <- c(rep("normal",30),rep("normal+trend",30))
# run clustering and color original groups each in red and blue
clustering <- pdclust(list)
pdf("pdcplot.pdf")
plot(clustering, labels= labels, type="rectangle", cols=cols, cex=0.5)
dev.off()
我可以轻松绘制标签。我添加了 "cex=0.5" 以减小图中的字体大小。另外，我删除了 "timeseries.as.labels = T" 因为当您指定标签时它会被覆盖。这是我的情节的样子（带标签）：
Clustering of simulated data with labels
当我指定的标签数量与时间序列数量不匹配时，我只能重现您报告的错误。您可能想再次检查标签向量的大小（例如 length(labels)==60）。
我想使用R的排列分布聚类包（https://cran.r-project.org/web/packages/pdc/pdc.pdf) for multivariate time series clustering. After using pdclust method(page 11 of the url pdf ) for hierarchical clustering, I plotted the dendogram using plot method (page 11 again). There are 60 samples. So, in the plot (Hierarchical clustering plot ), 有 60 个时间序列但没有标记。当我尝试指定标签向量而不是 labels = NULL 时，我总是会收到此错误 "Error in graphics:::plotHclust(n1, merge, height, order(x$order), hang, : invalid dendrogram input"。任何帮助将不胜感激。这是我的代码：
data1 <- read.csv(file="file_PID_1_1Apr_00-03.csv",head=FALSE,sep=",")
data2 <- read.csv(file="file_PID_2_1Apr_00-03.csv",head=FALSE,sep=",")
data3 <- read.csv(file="file_PID_3_1Apr_00-03.csv",head=FALSE,sep=",")
data4 <- read.csv(file="file_PID_4_1Apr_00-03.csv",head=FALSE,sep=",")
data5 <- read.csv(file="file_PID_5_1Apr_00-03.csv",head=FALSE,sep=",")
data6 <- read.csv(file="file_PID_6_1Apr_00-03.csv",head=FALSE,sep=",")
data7 <- read.csv(file="file_PID_7_1Apr_00-03.csv",head=FALSE,sep=",")
data8 <- read.csv(file="file_PID_8_1Apr_00-03.csv",head=FALSE,sep=",")
data9 <- read.csv(file="file_PID_1_1Apr_03-06.csv",head=FALSE,sep=",")
data10 <- read.csv(file="file_PID_2_1Apr_03-06.csv",head=FALSE,sep=",")
data11 <- read.csv(file="file_PID_3_1Apr_03-06.csv",head=FALSE,sep=",")
data12 <- read.csv(file="file_PID_4_1Apr_03-06.csv",head=FALSE,sep=",")
data13 <- read.csv(file="file_PID_5_1Apr_03-06.csv",head=FALSE,sep=",")
data14 <- read.csv(file="file_PID_6_1Apr_03-06.csv",head=FALSE,sep=",")
data15 <- read.csv(file="file_PID_7_1Apr_03-06.csv",head=FALSE,sep=",")
data16 <- read.csv(file="file_PID_8_1Apr_03-06.csv",head=FALSE,sep=",")
data17 <- read.csv(file="file_PID_1_1Apr_06-09.csv",head=FALSE,sep=",")
data18 <- read.csv(file="file_PID_2_1Apr_06-09.csv",head=FALSE,sep=",")
data19 <- read.csv(file="file_PID_3_1Apr_06-09.csv",head=FALSE,sep=",")
data20 <- read.csv(file="file_PID_4_1Apr_06-09.csv",head=FALSE,sep=",")
data21 <- read.csv(file="file_PID_5_1Apr_06-09.csv",head=FALSE,sep=",")
data22 <- read.csv(file="file_PID_6_1Apr_06-09.csv",head=FALSE,sep=",")
data23 <- read.csv(file="file_PID_7_1Apr_06-09.csv",head=FALSE,sep=",")
data24 <- read.csv(file="file_PID_8_1Apr_06-09.csv",head=FALSE,sep=",")
data25 <- read.csv(file="file_PID_1_1Apr_09-12.csv",head=FALSE,sep=",")
data26 <- read.csv(file="file_PID_2_1Apr_09-12.csv",head=FALSE,sep=",")
data27 <- read.csv(file="file_PID_3_1Apr_09-12.csv",head=FALSE,sep=",")
data28 <- read.csv(file="file_PID_4_1Apr_09-12.csv",head=FALSE,sep=",")
data29 <- read.csv(file="file_PID_5_1Apr_09-12.csv",head=FALSE,sep=",")
data30 <- read.csv(file="file_PID_6_1Apr_09-12.csv",head=FALSE,sep=",")
data31 <- read.csv(file="file_PID_7_1Apr_09-12.csv",head=FALSE,sep=",")
data32 <- read.csv(file="file_PID_8_1Apr_09-12.csv",head=FALSE,sep=",")
data33 <- read.csv(file="file_PID_1_1Apr_12-15.csv",head=FALSE,sep=",")
data34 <- read.csv(file="file_PID_2_1Apr_12-15.csv",head=FALSE,sep=",")
data35 <- read.csv(file="file_PID_3_1Apr_12-15.csv",head=FALSE,sep=",")
data36 <- read.csv(file="file_PID_4_1Apr_12-15.csv",head=FALSE,sep=",")
data37 <- read.csv(file="file_PID_5_1Apr_12-15.csv",head=FALSE,sep=",")
data38 <- read.csv(file="file_PID_6_1Apr_12-15.csv",head=FALSE,sep=",")
data39 <- read.csv(file="file_PID_7_1Apr_12-15.csv",head=FALSE,sep=",")
data40 <- read.csv(file="file_PID_8_1Apr_12-15.csv",head=FALSE,sep=",")
data41 <- read.csv(file="file_PID_2_1Apr_15-18.csv",head=FALSE,sep=",")
data42 <- read.csv(file="file_PID_3_1Apr_15-18.csv",head=FALSE,sep=",")
data43 <- read.csv(file="file_PID_4_1Apr_15-18.csv",head=FALSE,sep=",")
data44 <- read.csv(file="file_PID_6_1Apr_15-18.csv",head=FALSE,sep=",")
data45 <- read.csv(file="file_PID_7_1Apr_15-18.csv",head=FALSE,sep=",")
data46 <- read.csv(file="file_PID_8_1Apr_15-18.csv",head=FALSE,sep=",")
data47 <- read.csv(file="file_PID_1_1Apr_18-21.csv",head=FALSE,sep=",")
data48 <- read.csv(file="file_PID_2_1Apr_18-21.csv",head=FALSE,sep=",")
data49 <- read.csv(file="file_PID_3_1Apr_18-21.csv",head=FALSE,sep=",")
data50 <- read.csv(file="file_PID_4_1Apr_18-21.csv",head=FALSE,sep=",")
data51 <- read.csv(file="file_PID_6_1Apr_18-21.csv",head=FALSE,sep=",")
data52 <- read.csv(file="file_PID_7_1Apr_18-21.csv",head=FALSE,sep=",")
data53 <- read.csv(file="file_PID_8_1Apr_18-21.csv",head=FALSE,sep=",")
data54 <- read.csv(file="file_PID_1_1Apr_21-24.csv",head=FALSE,sep=",")
data55 <- read.csv(file="file_PID_2_1Apr_21-24.csv",head=FALSE,sep=",")
data56 <- read.csv(file="file_PID_3_1Apr_21-24.csv",head=FALSE,sep=",")
data57 <- read.csv(file="file_PID_4_1Apr_21-24.csv",head=FALSE,sep=",")
data58 <- read.csv(file="file_PID_6_1Apr_21-24.csv",head=FALSE,sep=",")
data59 <- read.csv(file="file_PID_7_1Apr_21-24.csv",head=FALSE,sep=",")
data60 <- read.csv(file="file_PID_8_1Apr_21-24.csv",head=FALSE,sep=",")
list <- array(0,dim=c(720,60,4))
myfunc <- function(j,i,k){
if (j == 1) return (data1[i,k])
else if (j==2) return (data2[i,k])
else if (j==3) return (data17[i,k])
else if (j==4) return (data9[i,k])
else if (j==5) return (data5[i,k])
else if (j==6) return (data6[i,k])
else if (j==7) return (data7[i,k])
else if (j==8) return (data8[i,k])
else if (j==9) return (data9[i,k])
else if (j==10) return (data10[i,k])
else if (j==11) return (data11[i,k])
else if (j==12) return (data12[i,k])
else if (j==13) return (data13[i,k])
else if (j==14) return (data14[i,k])
else if (j==15) return (data15[i,k])
else if (j==16) return (data16[i,k])
else if (j==17) return (data17[i,k])
else if (j==18) return (data18[i,k])
else if (j==19) return (data19[i,k])
else if (j==20) return (data20[i,k])
else if (j==21) return (data21[i,k])
else if (j==22) return (data22[i,k])
else if (j==23) return (data23[i,k])
else if (j==24) return (data24[i,k])
else if (j==25) return (data25[i,k])
else if (j==26) return (data26[i,k])
else if (j==27) return (data27[i,k])
else if (j==28) return (data28[i,k])
else if (j==29) return (data29[i,k])
else if (j==30) return (data30[i,k])
else if (j==31) return (data31[i,k])
else if (j==32) return (data32[i,k])
else if (j==33) return (data33[i,k])
else if (j==34) return (data34[i,k])
else if (j==35) return (data35[i,k])
else if (j==36) return (data36[i,k])
else if (j==37) return (data37[i,k])
else if (j==38) return (data38[i,k])
else if (j==39) return (data39[i,k])
else if (j==40) return (data40[i,k])
else if (j==41) return (data41[i,k])
else if (j==42) return (data42[i,k])
else if (j==43) return (data43[i,k])
else if (j==44) return (data44[i,k])
else if (j==45) return (data45[i,k])
else if (j==46) return (data46[i,k])
else if (j==47) return (data47[i,k])
else if (j==48) return (data48[i,k])
else if (j==49) return (data49[i,k])
else if (j==50) return (data50[i,k])
else if (j==51) return (data51[i,k])
else if (j==52) return (data52[i,k])
else if (j==53) return (data53[i,k])
else if (j==54) return (data54[i,k])
else if (j==55) return (data55[i,k])
else if (j==56) return (data56[i,k])
else if (j==57) return (data57[i,k])
else if (j==58) return (data58[i,k])
else if (j==59) return (data59[i,k])
else if (j==60) return (data60[i,k])
}
list <- array(0,dim=c(720,60,4))
for(i in 1:720){
for (j in 1:60){
list[i,j,1] <- myfunc(j,i,6)
list[i,j,2] <- myfunc(j,i,7)
list[i,j,3] <- myfunc(j,i,8)
list[i,j,4] <- myfunc(j,i,9)
}
}
library("pdc")
clustering <- pdclust(list)
plot(clustering, labels= NULL, type="rectangle", timeseries.as.labels = T, p.values=T)
我创建了一个没有数据文件的精简版代码，这样就可以更轻松地讨论您的问题。在这里，我创建了 60 个具有 4 个维度和 720 个时间点的时间序列（就像您所做的那样）。只是我模拟了一半的试验来自随机正态，另一半来自具有叠加随机正态的线性趋势。因此，对于 pdc，它们显然应该分为两组。这是代码：
require("pdc")
# make this replicable by setting a random seed
set.seed(7823)
# 60 TS each with 4 dimensions and 720 timepoints
# half of them are random uniform other half are a mix of random uniform
# and linear increase
list <- array(0,dim=c(720,60,4))
for (i in 1:30) {
for (j in 1:4) {
list[,i,j] <- rnorm(n = 720)
list[,i+30,j] <- rnorm(n=720)+1:720
}
}
cols <- c(rep("red",30),rep("blue",30))
labels <- c(rep("normal",30),rep("normal+trend",30))
# run clustering and color original groups each in red and blue
clustering <- pdclust(list)
pdf("pdcplot.pdf")
plot(clustering, labels= labels, type="rectangle", cols=cols, cex=0.5)
dev.off()
我可以轻松绘制标签。我添加了 "cex=0.5" 以减小图中的字体大小。另外，我删除了 "timeseries.as.labels = T" 因为当您指定标签时它会被覆盖。这是我的情节的样子（带标签）：
Clustering of simulated data with labels
当我指定的标签数量与时间序列数量不匹配时，我只能重现您报告的错误。您可能想再次检查标签向量的大小（例如 length(labels)==60）。