Question

我正在用R执行SVD分析，但我有一个结构NA值的矩阵。在这种情况下是否可以获得SVD分解？有替代解决方案吗？提前致谢

Answer 1

您可能想要在SpatioTemporal软件包中尝试SVDmiss函数，该软件包含缺失值插补以及计算插补矩阵上的SVD。请检查此链接SVDmiss Function

但是，您可能希望对数据的性质保持警惕，以及在您的情况下是否缺少价值估算。

Answer 2

我尝试在R中使用带有NA值的SVM而没有成功。有时它们在分析中很重要，所以我通常按如下方式转换数据：

如果你有很多变量，试着减少它们的数量（聚类，套索等......）

转换剩余的预测变量：

- 对于定量变量：

   - calculate deciles per predictor (leaving missing obs out)
   - calculate frequency of Y per decile (assuming Y is qualitative)
   - regroup deciles on their Y freq similarity into 2/3/4 groups 
     (you can do this by looking at their plot too)
   - create for each group a new binary variable 
     (X11 = 1 if X1 takes values in the interval ...)
   - calculate Y frequency for missing obs of that predictor
   - join the missing obs category to the variable that has the closest Y freq

- 对于定性变量：

   - if you have variables with lots of levels you should do clustering by Y 
     variable
   - for variables with lesser levels, you can calculate Y freq per class
   - regroup the classes like above
   - calculate the same thing for missing obs and attach it to the most similar 
     group of non-missing
   - recode the variable as for numeric case*

在那里，现在你有一个完整的虚拟变量数据库，以及执行SVM，神经网络，LASSO等的机会......

R中缺少值的SVD

2 个答案: