从主题列表阻止引导

问题描述

我正在尝试有效地实现块自举技术来获取回归系数的分布.主要概述如下.

I'm trying to efficiently implement a block bootstrap technique to get the distribution of regression coefficients. The main outline is as follows.

我有一个面板数据集，并说公司和年份是指数.对于引导程序的每次迭代，我希望对n个主题进行替换采样.从这个样本中，我需要构建一个新的数据框，该数据框是每个采样对象的所有观测值的rbind()堆栈，进行回归并提取系数.重复进行一堆迭代，比如说100.

I have a panel data set, and say firm and year are the indices. For each iteration of the bootstrap, I wish to sample n subjects with replacement. From this sample, I need to construct a new data frame that is an rbind() stack of all the observations for each sampled subject, run the regression, and pull out the coefficients. Repeat for a bunch of iterations, say 100.

• 每个公司都有可能被多次选择，因此我需要在每次迭代的数据集中多次将其数据包含进来.
• 像下面一样，使用循环和子集方法似乎在计算上很麻烦.
• 请注意，对于我的真实数据帧n，其次数迭代比下面的示例大得多.

我最初的想法是使用split()命令按主题将现有的数据框分成一个列表.在此使用

My thoughts initially are to break the existing data frame into a list by subject using the split() command. From there, use

sample(unique(df1$subject),n,replace=TRUE)

以获取新列表，然后也许从plyr包中实现quickdf以构造一个新的数据框.

to get the new list, then perhaps implement quickdf from the plyr package to construct a new data frame.

慢速代码示例:

require(plm)
data("Grunfeld", package="plm")

firms = unique(Grunfeld$firm)
n = 10
iterations = 100
mybootresults=list()

for(j in 1:iterations){

  v = sample(length(firms),n,replace=TRUE)
  newdata = NULL

  for(i in 1:n){
    newdata = rbind(newdata,subset(Grunfeld, firm == v[i]))
  }

  reg1 = lm(value ~ inv + capital, data = newdata)
  mybootresults[[j]] = coefficients(reg1)

}

mybootresults = as.data.frame(t(matrix(unlist(mybootresults),ncol=iterations)))
names(mybootresults) = names(reg1$coefficients)
mybootresults

  (Intercept)      inv    capital
1    373.8591 6.981309 -0.9801547
2    370.6743 6.633642 -1.4526338
3    528.8436 6.960226 -1.1597901
4    331.6979 6.239426 -1.0349230
5    507.7339 8.924227 -2.8661479
...
...

推荐答案

这样的事情如何?

myfit <- function(x, i) {
   mydata <- do.call("rbind", lapply(i, function(n) subset(Grunfeld, firm==x[n])))
   coefficients(lm(value ~ inv + capital, data = mydata))
}

firms <- unique(Grunfeld$firm)

b0 <- boot(firms, myfit, 999)

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