R中子集的回归 [英] Regression on a subset in R

问题描述

我想针对不同的国家(即数据的子集)进行相同的回归分析.我确实想出了在R中的操作方法，但是在Stata中轻松完成相同的操作之后，我想知道R中是否有更好的方法.

I want to run the same regression for different countries (i.e. subsets of my data). I did figure out how to do in R, but after doing the same thing with much more ease in Stata, I wonder if there's a better way in R.

在Stata中，您将执行以下操作:

In Stata you would do something like this:

foreach country in USA UK France {
    reg y x1 x2 if country == "`country'"
}

简单易读，对吧?在R中，我想出了split和ddply方法，两者都比较复杂.要使用split

Simple and human-readable, right? In R, I come up with split and ddply methods, both are more complicated. To use split

data.subset <- split(data, data$country)[c("USA", "UK", "France")]
res <- lapply(data.subset, function(subset) lm(y ~ x1 + x2, data=subset))

更紧凑的代码将使用ddply.但是在这种情况下，该模型将在所有国家/地区运行.我可以选择几个吗?

A more compact code would use ddply. But in this case, the model will be run for all countries. Can I choose just a few?

ddply(data, "country", function(df) coefficients(lm(Y~X1+X2, data=df)))

但是，再次，我有兴趣知道是否在Stata中有一个直观，可读的for循环?

But again, I'm interested in knowing whether there is an intuitive, readable for-loop like in Stata?

推荐答案

有几种选择:

使用ddply的一种方法:

ddply( data[ data$country %in% c('USA','UK','France'), ], "country", function(df) coefficients(lm(Y~X1+X2, data=df)))

以不同的方式使用lapply(或sapply):

Using lapply (or sapply) a different way:

lapply( c("USA","UK","France"), function(curcont) lm(y ~ x1+x2, data=data, subset= country==curcont))

您可以使用nlme软件包中的lmList函数.

You could use the lmList function from the nlme package.

您可以直接使用lm(尽管这将使用方差的合并估计而不是单独的估计):

You could use lm directly (though this will use a pooled estimate of the variance instead of separate ones):

lm( y ~ 0 + factor(country) * (x1 + x2), data=data, subset= country %in% c('USA','UK','France') )

还有by函数和for循环以及其他可能的选项.

There is also the by function and for loops and probably other options as well.

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