w**********n
发帖数: 5 | 1 做多元回归，共有4.50个自变量，500多个obs，但自变量之间有很强的共线性。用
elastic net方法做model selection,试了试一下子模型里有很多变量。要怎样可以筛
选这些变量呢？BIC value怎么算？相关R的命令怎么写？ | G*****1
发帖数: 137 | 2 a simple way is to get rid of some of them.
【在 w**********n 的大作中提到】
: 做多元回归，共有4.50个自变量，500多个obs，但自变量之间有很强的共线性。用
: elastic net方法做model selection,试了试一下子模型里有很多变量。要怎样可以筛
: 选这些变量呢？BIC value怎么算？相关R的命令怎么写？
| D******n
发帖数: 2836 | 3 ya, if there is some prior knowledge ,just choose the ones u think are
important. Maybe 20-30 of them, then you can use forward/backward selection
methods, LARS, LASSO.
Or you can use PCA to reduce the dimension of the data.
Maybe directly using LASSO or Forward/Backward selection methods can work
too, but not sure.
【在 G*****1 的大作中提到】
: a simple way is to get rid of some of them.
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