R carret软件包中的警告查找关联”；行和列的组合在截止线“上方”；不获取和返回值

我目前正在尝试根据变量的相关性筛选变量 在我的Mac电脑上用RStudio中R的carret软件包

到目前为止，我可以计算和打印数据集的相关性。但是，一旦应用findCorrelation方法，就不会返回任何数据。我只收到以下警告：

行和列的组合在截止线上方，值=标记列

库（插入符号）
预处理属性类我认为问题在于你的相关矩阵：

> class(na.omit(descrCor))
[1] "matrix"
> dim(na.omit(descrCor))
[1]   0 153

这些数据包含许多缺失数据的列：

> pct_na <- unlist(lapply(data.train, function(x) mean(is.na(x))))
> summary(pct_na)
   Min. 1st Qu.  Median    Mean 3rd Qu.    Max. 
 0.0000  0.0000  0.9793  0.6401  0.9793  0.9793 

现在执行过滤器：

> highlyCorDescr <- findCorrelation(descrCor, cutoff = .9,    verbose=TRUE,names=FALSE)
Compare row 10  and column  1 with corr  0.992 
  Means:  0.266 vs 0.164 so flagging column 10 
Compare row 1  and column  9 with corr  0.925 
  Means:  0.247 vs 0.161 so flagging column 1 
Compare row 9  and column  4 with corr  0.928 
  Means:  0.229 vs 0.158 so flagging column 9 
Compare row 8  and column  2 with corr  0.966 
  Means:  0.24 vs 0.154 so flagging column 8 
Compare row 19  and column  18 with corr  0.918 
  Means:  0.089 vs 0.155 so flagging column 18 
Compare row 46  and column  31 with corr  0.914 
  Means:  0.099 vs 0.158 so flagging column 31 
Compare row 46  and column  33 with corr  0.933 
  Means:  0.081 vs 0.161 so flagging column 33 
All correlations <= 0.9 
> keep_these <- names(data.train)[!(names(data.train) %in% colnames(descrCor)[highlyCorDescr])]
> data.train.subset <- data.train[, keep_these]

>highlyCorDescr您是否尝试过降低截止值？看起来没有一个组合的相关性>=0.9。是的，即使是0.1，我也试过了，但没有什么区别。我还可以在descrCor中看到数据，但是有一些NA没有被NA.omit删除。这可能是原因吗？你能发布你的数据的最小样本吗？如果na.omit不起作用，我会认为他们没有意识到你的一些na实际上缺失了，但如果没有可复制的例子，就无法判断。完全没有链接到你的数据，但看起来插入符号的创建者回答了你的问题，所以我认为你应该很好！
> sum(pct_na > .1)
[1] 100
> keepers <- data.train[,names(which(pct_na <= .1))]
> descrCor <- cor(keepers ,use="complete.obs")

> summary(descrCor[upper.tri(descrCor)])
     Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
-0.992000 -0.108800  0.001911  0.001667  0.088680  0.980900 

> highlyCorDescr <- findCorrelation(descrCor, cutoff = .9,    verbose=TRUE,names=FALSE)
Compare row 10  and column  1 with corr  0.992 
  Means:  0.266 vs 0.164 so flagging column 10 
Compare row 1  and column  9 with corr  0.925 
  Means:  0.247 vs 0.161 so flagging column 1 
Compare row 9  and column  4 with corr  0.928 
  Means:  0.229 vs 0.158 so flagging column 9 
Compare row 8  and column  2 with corr  0.966 
  Means:  0.24 vs 0.154 so flagging column 8 
Compare row 19  and column  18 with corr  0.918 
  Means:  0.089 vs 0.155 so flagging column 18 
Compare row 46  and column  31 with corr  0.914 
  Means:  0.099 vs 0.158 so flagging column 31 
Compare row 46  and column  33 with corr  0.933 
  Means:  0.081 vs 0.161 so flagging column 33 
All correlations <= 0.9 
> keep_these <- names(data.train)[!(names(data.train) %in% colnames(descrCor)[highlyCorDescr])]
> data.train.subset <- data.train[, keep_these]