如何解决如何在线性回归中表示不同数据框上的变量，lm()
我在 R 中的线性回归表达式有问题。
当我输入带有“硬值”的函数时，它工作得很好：
LM <- lm(`589499102` ~ `Value-Weighted Return`,data = strategic_return,subset = 3:255)
现在我想通过另一个工作表上的变量来交换 589499102 的硬值。我尝试了以下几种不同的方法，但都没有奏效
LM <- lm(OtherSheet$Col1[1] ~ `Value-Weighted Return`,subset = 3:255) 或
LM <- lm(`OtherSheet$Col1[1]` ~ `Value-Weighted Return`,subset = 3:255)
它甚至不起作用，当我创建一个变量时：
newvariable <- "589499102"
LM <- lm(`newvariable` ~ `Value-Weighted Return`,subset = 3:255)
最终的目标是将其集成到 for 循环中。但是，当这个问题解决后，这将是一个较小的问题。
现在的数据是这样的：
表 1：strategy_date 和 cusip。该表实际上提供了回归本身的信息。哪一列需要回归哪一行。
| strategic_cusip | announcement_date | ... | ... | ... | row_start_regression | row_end_regression |
|---|---|---|---|---|---|---|
| 589499102 | 2010-02-21 | ... | ... | ... | 3 | 255 |
| 705573103 | 2010-03-14 | ... | ... | ... | 18 | 270 |
| 46069S109 | 2010-03-21 | ... | ... | ... | 23 | 275 |
| ... | ... | ... | ... | ... | ... | ... |
表 2：strategy_return。此表包含实际进行回归的“原始数据”。
| 日期 | 002824100 | 00430U103 | 589499102 | ... | ... | 价值加权回报 |
|---|---|---|---|---|---|---|
| 2009-01-05 | -0.01717700 | 0.0022422 | -0.038760 | ... | ... | 0.016764 |
| 2009-01-06 | -0.03267477 | -0.0044743 | 0.016129 | ... | ... | 0.033647 |
| 2009-01-07 | -0.00549882 | -0.1146067 | 0.031746 | ... | ... | -0.022271 |
| 2009-01-08 | 0.01105845 | 0.0329949 | 0.0076923 | ... | ... | 0.011896 |
| 2009-01-09 | -0.00058594 | -0.0491400 | 0.046512 | ... | ... | -0.018748 |
| 2009-01-12 | -0.02169240 | -0.0051680 | -0.022222 | ... | ... | -0.026589 |
| ... | ... | ... | ... | ... | ... | ... |
| ... | ... | ... | ... | ... | ... | ... |
| 2020-12-31 | 0.03292349 | -0.0031726 | -0.1029381 | ... | ... | 0.0213947 |
目标是将表 strategy_return (002824100,00430U103,589499102) 的每一列与“Value-Weighted Return”列在时间段内回归到 strategy_dateandcusip$row_start_regression 到strategy_dateandcusip$row_end_end_pregression。 >
第一张表中的字段 strategy_dateandcusip[1,1] 与第二张表的第四列的标题相同strategy_return = 589499102。我尝试像这样链接这些表。
我有 dput() 上面的图解表格。我很抱歉长代码。我真的试图将其最小化为仅必要的行和列。
Sheet 1: strategic_dateandcusip:
structure(list(strategic_cusip = structure(2:1,.Label = c("46069S109","589499102"),class = "factor"),strategic_cusip_new = c("589499102","46069S109"),strategic_date = structure(c(14661,14689),class = "Date"),strategic_dateweekday = structure(c(14662,14690),row_of_eventdate = c(286,306),row_start_regression = c(3,23),row_end_regression = c(255,275)),row.names = c(1L,3L),class = "data.frame")
Sheet 2: strategic_return
structure(list(Date = 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= c(NA,313L),class = "data.frame")```
解决方法
这就是我的处理方式：
library(data.table)
setDT(strategic_return)
#create column with row numbers
strategic_return[,rowID := .I]
#create a column with x values,#since data doesn't contain these
strategic_return[,x := rnorm(.N)]
#reshape to long format
DF <- melt(strategic_return,id.vars = c("Date","rowID","x"))
#select data
DF <- DF[strategic_dateandcusip,.SD,on = c("variable == strategic_cusip","rowID >= row_start_regression","rowID <= row_end_regression")]
library(nlme)
fit <- lmList(value ~ x | variable,data = DF,pool = FALSE)
summary(fit)
library(lmtest)
lapply(fit,coeftest,cov. = vcovHC,type = "HC1")
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