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我对序列分析非常陌生，并试图在聚合序列矩阵中识别集群，重点关注状态持续时间。但是，当使用 method='CHI2'/'EUCLID' 结合 step=1 （不是其他）时，我收到错误：

if (SCres > currentSCres) { 中的错误：需要 TRUE/FALSE 的缺失值

任何想法为什么（距离矩阵中有一些 NaN，它们可能是由于序列长度不同造成的）？

序列对象和距离矩阵是什么样的代码：

Sequence                                         
1    a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a
2    a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a  
3    a-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-c-c-c
4    a-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e
5    b-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a

Distance matrix
           1         2      3          4
2        NaN                              
3        289.92897   NaN                    
4        141.07472   NaN    263.22855          
5        10.22425    NaN    290.10919  141.44473

代码：

library(TraMineR) #version 2.0-13
library(WeightedCluster) #version 1.4

SO = seqdef(DAT,right='DEL')
DM = seqdist(SO, method = "CHI2", step=1, full.matrix = F)
FIT = seqpropclust(SO, diss=DM, maxcluster=8, 
      properties=c("state", "duration", "spell.age","spell.dur",
        "transition","pattern", "AFtransition", "AFpattern","Complexity"))
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1 回答 1

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计算的"CHI2"两个序列x和y之间的TraMineR距离是长度为 的连续周期内状态分布之间的卡方距离之和step。参见Studer 和 Ritchard (2014, p 8)。

这意味着step=1在每个位置计算卡方距离。当其中一个序列在某些位置（例如第二个序列中的最后一个位置）具有空值时，无法计算这些位置的距离，我们会得到该序列与任何其他序列之间距离的NaN值。CHI2

为避免这种情况，您可以使用以下解决方法：

1) 设置一个step足够大的值，以确保每个序列在每个周期间隔中至少包含一个非空元素。对于您的示例，最长序列的长度为 25。要确保最后一个周期包含非 void 元素，您必须设置step=5.

DAT <- c("a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a",
         "a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a",  
         "a-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-c-c-c",
         "a-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e-e",
         "b-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a-a")
SO <- seqdef(DAT)
DM <- seqdist(SO, method = "CHI2", step=5)
DM
##          [,1]     [,2]     [,3]     [,4]     [,5]
## [1,] 0.000000 0.000000 4.543441 4.543441 1.030776
## [2,] 0.000000 0.000000 4.543441 4.543441 1.030776
## [3,] 4.543441 4.543441 0.000000 2.028370 4.604927
## [4,] 4.543441 4.543441 2.028370 0.000000 4.604927
## [5,] 1.030776 1.030776 4.604927 4.604927 0.000000

2) 删除带有 void 元素的列：

SOdrop <- SO[,1:(ncol(SO)-1)]
SOdrop
DMd <- seqdist(SOdrop, method = "CHI2", step=1)
DMd
##          [,1]     [,2]      [,3]      [,4]     [,5]
## [1,]  0.00000  0.00000 10.041580 10.041580  2.50000
## [2,]  0.00000  0.00000 10.041580 10.041580  2.50000
## [3,] 10.04158 10.04158  0.000000  4.472136 10.34811
## [4,] 10.04158 10.04158  4.472136  0.000000 10.34811
## [5,]  2.50000  2.50000 10.348108 10.348108  0.00000

3）用缺失值填充较短的序列，并将缺失值视为额外的可能状态。默认情况下right='DEL'in seqdef，这会创建空洞。在这里，我们设置right=NA为获取缺失值。

SOm = seqdef(DAT, right=NA)
DMm = seqdist(SOm, method = "CHI2", step=1, with.missing=TRUE)
DMm
##          [,1]      [,2]      [,3]      [,4]      [,5]
## [1,]  0.000000  2.738613 10.408330 10.408330  2.500000
## [2,]  2.738613  0.000000 10.527741 10.527741  3.708099
## [3,] 10.408330 10.527741  0.000000  5.477226 10.704360
## [4,] 10.408330 10.527741  5.477226  0.000000 10.704360
## [5,]  2.500000  3.708099 10.704360 10.704360  0.000000

现在，问题中报告的错误不是错误seqdist，而是库中的seqpropclust函数错误WeightedCluster。该误差显然是由NaN相异矩阵中的 引起的。

于 2020-02-07T12:48:36.100 回答