研究簇的sd有时返回NA，有时不返回

Question

我有一系列使用kmeans进行聚类的观察结果。然后，我调查每个群集内的标准偏差（sd）并获得最大值。

如果我多次运行相同的代码，有时会出现NA。

减少k_n（簇数）产生相同的效果，增加越差；有/无na.rm=T不会更改。

有人可以解释一下我在做什么错吗？

代码：

k_n <-11
clusters=as.data.frame(kmeans(ex,k_n, nstart=50,iter.max = 15 )$cluster)
clusters<-cbind(clusters,ex)
temp<-sapply(1:k_n, function(k){temp=subset(clusters, clusters[,1]==k)
                                sd<-sd(temp[,2], na.rm = T)
                                return(sd)})
max(temp)
temp

这里是三轮的结果。如您所见，第三个试验返回一个NA，而其他两个则没有。

这是数据“ ex”：

1400 1400 2000 2000 2000 2001 1400 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1400 1400 1401 2000 2000 2000 2000 1401 1401 2000 2000 2000 2000 2000 2000 2000 2000 801 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1400 1400 2000 2000 2000 1400 1400 2000 2000 2000 1200 1600 1500 1960 1350 1900 1900 1900 1350 2000 2000 2000 2000 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 1900 1350 1900 1900 1350 1350 2000 2000 2000 2000 2000 2000 2000 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1920 1350 1350 1900 1900 1900 1900 1900 1350 2000 2000 2000 2000 2000 2000 2000 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 2000 1900 1350 190 0 1900 1900 1900 1350 2000 2000 2000 2000 2000 2000 2000 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 2000 2000 2000 1900 1901 2000 2000 2000 2000 1200 1200 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 2000 2000 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1500 1500 1500 1500 2000 1350 1900 1900 1900 1350 2000 2000 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1500 1500 1400 1350 1350 1900 1900 1900 1350 2000 2000 2000 2000 2000 2000 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1500 1500 1500 1500 1500 0 0 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 150 0 1500 2000 2000 2000 2000 2000 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 1900 1900 1900 1900 1900 1900 1500 1500 1500 1400 190 1901 1901 1901 2000年1350 1900 1900 1900 1350 1350 2000 2000 2000 2000 1500 1560 1900 1900 1900 1900 1900 1900 1400 2000 1350 1900 1900 1900 1350 2000 2000 2000 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1200 1200 1200 1200 1200 1200 1200 1200 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1400 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 1900 2000 1350 1900 1900 1900 1900 1350 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1200 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1900 1900 1350 1350 1350 1350 1350 1350 1900 2000 1350 1900 1900 1900 1900 1900 1900 1900 1350 1350 2000 2000 2000 2000 2000 2000 2000 2000 200 0 2000 1200 1200 1200 1200 1200 1200 1200 1200 1200 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1600 1900 1900 801 1400 1400 1400 1400 1901 1901 1900 1901 1901 1900 1901 1901 1901 1900 1900 1900 1900 1900 1901 1900 1901 1901 1900 1901 1901 1350 1350 1350 1350 1350 1350 1350 2000 2000 2000 2000 2000 2000 2000 2000 2000 1400 1400 1401 1401 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 1100 1100 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 [2000 2000 2000 2000 2000 2000 2000 2000 1800 2000 1800 1800 1400 1200 1400 1600 1800 1800 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000 2000] >

我有一系列使用kmeans进行聚类的观察结果。然后，我调查每个群集内的标准偏差（sd）并获得最大值。如果我多次运行相同的代码，有时，...

Answer 1

您将获得sd的NA，因为群集中只有一个成员。