为什么"colMeans()"和"rowMeans()"的功能要比在"lapply()"中使用均值功能更快? [英] Why are `colMeans()` and `rowMeans()` functions faster than using the mean function with `lapply()`?

问题描述

我想问的是，rowMeans()和colMeans()函数在算法上如何优化速度?

What I want to ask is, algorithmically, what do the rowMeans() and colMeans() functions do to optimize speed?

推荐答案

此外，请考虑lapply()的作用.它设置对函数mean()的重复调用.因此，除了实际计算均值的开销(在快速C代码中完成)之外，lapply()版本还会反复产生健全性检查代码和与mean()相关联的方法分派的开销.

In addition, consider what lapply() does. It sets up repeated calls to the function mean(). So as well as the overhead of actually computing a mean (which is done in fast C code), the lapply() version repeatedly incurs the overhead of the sanity checking code and method dispatch associated with mean().

rowMeans()和colMeans()仅在内部进行一组健全性检查，它们的C代码经过优化，可以循环遍历那里的行/列，而无需通过单独的R调用.

rowMeans() and colMeans() incur only a single set of sanity checks as internally, their C code is optimised to loop over the rows/columns there rather than via separate R calls.

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