如何测试缺少值的多元正态性？

时间：2019-07-03 14:50:47

标签： r normalization missing-data multivariate-testing

在数据列上应用missMech:TestMCARNormality时（见下文），其中三列由相同的6行的NA组成：

summary(TestMCARNormality(data = mvn.BN))

我收到一条错误消息：

Warning: More than one missing data pattern should be present.
Error in TestMCARNormality(data = mvn.BN) :

我的意图是使用此数据测试SEM的多元正态性假设。我的怀疑是，由于这三列在相同的主题上都有缺失值，因此缺失机制不能认为是任意的。因为这三个向量反映了相同的现象，所以我尝试将它们折叠为一个潜在变量，如下所示：

sndPre <- 'sndPre  =~ r + rlct + int'

summary(fit <- cfa(sndPre, data = mvn.BN, missing="fiml"))

df$sndPre <- lavPredict(fit)

但是，使用包含潜在变量而不是三个原始向量的新数据测试多重正态性会导致与以前相同的错误消息。

我们将不胜感激！

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1 个答案:

答案 0 :(得分：0)

只有一些小提示，希望我能有所帮助。 （很遗憾，我仍然没有完全了解您要归档的内容。

我查看了您的数据-丢失的数据总是以相同的模式出现。 这意味着-如果缺少数据，则始终是这种模式：

non-NA, NA, NA, NA, non-NA, non-NA, non-NA

这也是您的错误信息。

  

警告：应该存在多个缺少的数据模式。

进行您描述的转换（将变量组合为一个变量）时，该转换仍然不会改变。新的模式是：

non-NA, NA, non-NA, non-NA, non-NA

但是整个数据集仍然是相同的模式。

  

我的怀疑是，由于这三列的主题完全相同而缺少值，因此不能将缺失机制视为任意的。

是的-这可能是您可以拥有的非MCAR数据最明显的情况。实际上如此明显，以至于MissMech甚至无法计算出一些东西。

根据MCAR的维基百科定义

  

如果导致任何特定数据项为NA的事件与可观察变量和不可观察参数无关

您的数据显然不是这种情况。由于NA是独立的，因此它们将以某种方式均匀地分布在所有变量上。 NA仅针对3个变量出现的事实表明了这一点。 因为您可以说：如果值取决于NA，则它的变量为v2，v3，v4-因为仅对于这些变量，才会出现NA。

通常，MCAR测试将测试该假设“独立于可观察变量和不可观察参数”。它会以某种方式看起来是否所有可能的模式均等地出现。因为只有那时数据才是MCAR。如果从一开始只有一种模式-您甚至无法开始计算模式的频率。

希望我能帮上忙-这是相当简化的说明，以使其易于理解。

关于多元正态检验：

library("MVN")
library("mice")

#imputation with mice
imp <- mice(mvn, m=5)
imp_data <- complete(imp, action = 1)

# mvn test with MVN package
mvn(imp_data, univariatePlot = "qqplot")

那将以非多重插补方式使用小鼠。您也可以进行多次插补，但是随后您必须合并插补结果。由于您的数据不是MCAR，因此两种方式都有可能引入偏差。但是，如果您进行完整的案例分析，您也可能会引入偏见。因此可能无法100％避免偏见。

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