在 R 中运行回归循环的最佳方法是什么？
假设我有可索引的数据源 X 和 Y，例如矩阵。我想运行一组独立回归并存储结果。我最初的方法是
results = matrix(nrow=nrow(X), ncol=(2))
for(i in 1:ncol(X)) {
matrix[i,] = coefficients(lm(Y[i,] ~ X[i,])
}
但是，循环很糟糕，所以我可以用 lapply 来做到这一点，因为
out <- lapply(1:nrow(X), function(i) { coefficients(lm(Y[i,] ~ X[i,])) } )
有更好的方法来做到这一点吗？
Assume that I have sources of data X and Y that are indexable, say matrices. And I want to run a set of independent regressions and store the result. My initial approach would be
results = matrix(nrow=nrow(X), ncol=(2))
for(i in 1:ncol(X)) {
matrix[i,] = coefficients(lm(Y[i,] ~ X[i,])
}
But, loops are bad, so I could do it with lapply as
out <- lapply(1:nrow(X), function(i) { coefficients(lm(Y[i,] ~ X[i,])) } )
Is there a better way to do this?
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你在这里肯定过度优化了。与模型拟合过程相比，循环的开销可以忽略不计，因此简单的答案是 - 使用您认为最容易理解的任何方式。我会选择 for 循环，但 lapply 也很好。
You are certainly overoptimizing here. The overhead of a loop is negligible compared to the procedure of model fitting and therefore the simple answer is - use whatever way you find to be the most understandable. I'd go for the for-loop, but lapply is fine too.
我用 plyr 做这类事情，但我同意这不是一个处理效率问题，而是一个你可以轻松阅读和编写的问题。
I do this type of thing with plyr, but I agree that it's not a processing efficency issue as much as what you are comfortable reading and writing.
如果您只想执行简单的多元线性回归，那么我建议不要使用 lm()。有 lsfit()，但我不确定它是否会提供很大的加速（我从未进行过正式比较）。相反，我建议使用 qr() 和 qrcoef() 执行 (X'X)^{-1}X'y。这将允许您执行多元多元线性回归；也就是说，将响应变量视为矩阵而不是向量，并对每行观测值应用相同的回归。
在您的示例中，每个观察向量是否有不同的设计矩阵？如果是这样，您也许可以修改 Z 以仍然适应这种情况。
If you just want to perform straightforward multiple linear regression, then I would recommend not using lm(). There is lsfit(), but I'm not sure it would offer than much of a speed up (I have never performed a formal comparison). Instead I would recommend performing the (X'X)^{-1}X'y using qr() and qrcoef(). This will allow you to perform multivariate multiple linear regression; that is, treating the response variable as a matrix instead of a vector and applying the same regression to each row of observations.
In your example, is there a different design matrix per vector of observations? If so, you may be able to modify Z in order to still accommodate this.