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如何检验存在缺失值的多元正态性？

rnormalizationmissing-datamultivariate-testing
3

在将missMech:TestMCARNormality应用于下面的数据框时，其中有三列包含相同6行的NA:

summary(TestMCARNormality(data = mvn.BN))

我收到一个错误信息：

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

我的意图是使用这个数据来测试SEM的多元正态性假设。 我怀疑的是，因为这三列在相同的受试者中具有缺失值，所以不能将缺失机制视为随意的。 因为这三个向量反映了相同的现象，我尝试将它们合并成一个潜变量，如下所示：

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

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

df$sndPre <- lavPredict(fit)

然而，使用由潜变量组成的新数据进行多元正态性测试，而不是三个原始向量，结果与以前相同出现错误信息。
非常感谢您的帮助！
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欢迎来到SO :) 你的问题似乎表述得很清楚，但我不太明白数据框长什么样。 - Steffen Moritz
哦，数据似乎不太大 - 你可以通过dput()将它添加到你的帖子中。只需运行dput(df)，然后将文本粘贴到你的帖子中。这样每个人都可以直接将其导入R，而你也不必链接到外部文件。 - Steffen Moritz
1个回答
1

希望我能提供一些小提示帮到你。 （不幸的是，我还没有完全理解你想要实现的目标。

我查看了你的数据 - 缺失的数据总是以相同的模式出现。 这意味着 - 如果有缺失的数据，它总是这个模式：

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

这也是你的错误信息所涉及的内容。

警告：应该存在多个丢失数据模式。

当您执行所描述的转换（将变量组合为一个变量）时，这仍然不会改变。新模式如下：

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

但是它仍然是数据集中的同一模式。
我的猜测是，因为这三列对于相同的受试者具有缺失值，所以缺失机制不能被认为是随意的。
是的-这可能是您可以拥有的最明显的非MCAR数据情况。实际上如此明显，以至于MissMech甚至无法计算任何东西。
从维基百科关于MCAR的定义来看：
如果导致任何特定数据项成为NA的事件既独立于可观察变量，也独立于感兴趣的不可观察参数
对于您的数据，这显然不是这种情况。因为NA是独立的，它们会在所有变量上以某种方式分布均匀。 NAs仅出现在3个变量中的事实表明了这一点。因为您可以说：如果一个值是NA取决于它是否为v2、v3、v4变量-因为仅对于这些变量才会出现NA。
通常，MCAR测试会测试这个假设“独立于可观察变量和不可观察参数”。它会以某种方式查看所有可能的模式是否平均发生。因为只有在所有模式都平均发生时，数据才是MCAR的。如果从一开始就只有1个模式-您甚至无法开始计算模式的频率。
希望我能为您提供帮助，这是一个相当简化的解释，以使其易于理解。
关于多元正态性检验：
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")

那将是以非多重插补的方式使用老鼠。您也可以进行多重插补，但然后必须汇总插补结果。两种方法都可能引入偏差，因为您的数据不是MCAR。但是，如果进行完全案例分析，则也可能引入偏差。因此，可能没有100％避免偏差的方法。
非常感谢stats007。每个变量反映了对话中两个交流者在不同步骤中的行为（N = 104）。由NA变量（rlct，R，int）反映的步骤在6次对话中未执行，因此缺失。我的SEM模型建立了一条路径，根据该路径，5个中间变量（潜变量：= rlct-R-int，gta，open）介导了第一个变量（supp）和最后一个变量（dtls）之间的关联。我使用“fiml”来处理缺失值，并且正在努力测试SEM的一个假设，即多元正态性。也许可以进行插补？ - Uri
啊，现在我越来越接近理解你想做什么了 :) 这104个案例本身是否有一些时间依赖性？也许是同一个人在多个星期内？还是这104个案例是独立的？嘿，我会更新我的帖子，向你展示如何通过使用mice进行插补来解决这个问题。但无论如何，由于数据不是MCAR，所以很可能会引入一些偏差（但我认为你无法避免这种情况）。 - Steffen Moritz
他们是独立的。再次感谢你们对此的帮助以及对我在SO的第一步的支持。 - Uri
您可以使用复选标记来标记答案 - 这样，以后遇到相同问题的用户就能看到某个答案解决了问题。 - Steffen Moritz
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