R通过lapply命令从乘法回归中提取回归系数 [英] R extract regression coefficients from multiply regression via lapply command

问题描述

我有一个包含多个变量的大型数据集，其中一个是状态变量，每个状态的编码为1-50.我想对数据集的其余27个变量(总共有55个变量)进行28个变量的回归，并针对每个状态进行回归.

I have a large dataset with several variables, one of which is a state variable, coded 1-50 for each state. I'd like to run a regression of 28 variables on the remaining 27 variables of the dataset (there are 55 variables total), and specific for each state.

换句话说，对于state == 1的观测值，对covariate1，covariate2，...，covariate27进行变量1的回归.然后，我想对状态2-50的变量1重复此操作，并对变量2，变量3，...，变量28重复整个过程.

In other words, run a regression of variable1 on covariate1, covariate2, ..., covariate27 for observations where state==1. I'd then like to repeat this for variable1 for states 2-50, and the repeat the whole process for variable2, variable3,..., variable28.

我认为我已经编写了正确的R代码来执行此操作，但是接下来我想做的就是提取系数，理想情况下是将其提取到系数矩阵中.有人可以帮我吗?这是我到目前为止编写的代码:

I think I've written the correct R code to do this, but the next thing I'd like to do is extract the coefficients, ideally into a coefficient matrix. Could someone please help me with this? Here's the code I've written so far:

for (num in 1:50) {

    #PUF is the data set I'm using

    #Subset the data by states
    PUFnum <- subset(PUF, state==num)

    #Attach data set with state specific data
    attach(PUFnum)

    #Run our prediction regression
    #the variables class1 through e19700 are the 27 covariates I want to use
    regression <- lapply(PUFnum,  function(z) lm(z ~ class1+class2+class3+class4+class5+class6+class7+
                                                     xtot+e00200+e00300+e00600+e00900+e01000+p04470+e04800+
                                                     e09600+e07180+e07220+e07260+e06500+e10300+
                                                     e59720+e11900+e18425+e18450+e18500+e19700))

    Beta <- lapply(regression, function(d) d<- coef(regression$d))


    detach(PUFnum)
}

推荐答案

这是经典Split-Apply-Combine问题的另一个示例，可以使用@hadley的plyr包来解决.在遇到问题时，您想

This is another example of the classic Split-Apply-Combine problem, which can be addressed using the plyr package by @hadley. In your problem, you want to

1. 按状态分割数据帧
2. 对每个子集应用回归
3. 将系数合并到数据帧中.

我将使用MASS库中可用的Cars93数据集来说明它.我们有兴趣根据国家/地区的origin找出horsepower和enginesize之间的关系.

I will illustrate it with the Cars93 dataset available in MASS library. We are interested in figuring out the relationship between horsepower and enginesize based on origin of country.

# LOAD LIBRARIES
require(MASS); require(plyr)

# SPLIT-APPLY-COMBINE
regressions <- dlply(Cars93, .(Origin), lm, formula = Horsepower ~ EngineSize)
coefs <- ldply(regressions, coef)

   Origin (Intercept) EngineSize
1     USA    33.13666   37.29919
2 non-USA    15.68747   55.39211

编辑.例如，用PUF代替Cars93，用state代替Origin，用fm代替公式

EDIT. For your example, substitute PUF for Cars93, state for Origin and fm for the formula

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