如何在线性回归 lm() 中表达不同数据框上的变量
How to express a variable on a different data frame in a linear regression, lm()
我在 R 中遇到线性回归表达式的问题。
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 sheet 上的变量交换 589499102 的硬值。我尝试了如下几种不同的方法，但其中 none 行得通
LM <- lm(OtherSheet$Col1[1] ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255) 或
LM <- lm(`OtherSheet$Col1[1]` ~ `Value-Weighted Return`, data = strategic_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`, data = strategic_return, subset = 3:255)
最终的目标是将其集成到 for 循环中。不过等这个问题解决了，问题就小了。
目前的数据是这样的：
Sheet1:strategic_dateandcusip。这个 sheet 实际上给出了回归本身的信息。哪一列需要对哪一行进行回归。
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
...
...
...
...
...
...
...
Sheet 2: strategic_return。 sheet 包含实际进行回归的“原始数据”。
Date
002824100
00430U103
589499102
...
...
Value-Weighted Return
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
目标是在记录的时间段内使用“价值加权 Return”列对 sheet strategic_return（002824100、00430U103、589499102）的每一列进行回归在 sheet strategic_dateandcusip$row_start_regression 到 strategic_dateandcusip$row_end_regression.
第一个sheetstrategic_dateandcusip[1,1]中的字段与第二个第四列的表头相同sheetstrategic_return=589499102 . 我试着 link 这些 sheet 那样。
我有 dput() 上面的插图表格。很抱歉代码太长。我真的试图将它最小化到只需要必要的行和列。
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), class = "Date"),
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 = structure(c(14246, 14249, 14250, 14251,
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0.0130293159609121, -0.0192926045016078)), row.names = c(NA,
313L), class = "data.frame")```
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")
我在 R 中遇到线性回归表达式的问题。
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 sheet 上的变量交换 589499102 的硬值。我尝试了如下几种不同的方法，但其中 none 行得通
LM <- lm(OtherSheet$Col1[1] ~ `Value-Weighted Return`, data = strategic_return, subset = 3:255) 或
LM <- lm(`OtherSheet$Col1[1]` ~ `Value-Weighted Return`, data = strategic_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`, data = strategic_return, subset = 3:255)
最终的目标是将其集成到 for 循环中。不过等这个问题解决了，问题就小了。
目前的数据是这样的：
Sheet1:strategic_dateandcusip。这个 sheet 实际上给出了回归本身的信息。哪一列需要对哪一行进行回归。
| 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 |
| ... | ... | ... | ... | ... | ... | ... |
Sheet 2: strategic_return。 sheet 包含实际进行回归的“原始数据”。
| Date | 002824100 | 00430U103 | 589499102 | ... | ... | Value-Weighted Return |
|---|---|---|---|---|---|---|
| 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 |
目标是在记录的时间段内使用“价值加权 Return”列对 sheet strategic_return（002824100、00430U103、589499102）的每一列进行回归在 sheet strategic_dateandcusip$row_start_regression 到 strategic_dateandcusip$row_end_regression.
第一个sheetstrategic_dateandcusip[1,1]中的字段与第二个第四列的表头相同sheetstrategic_return=589499102 . 我试着 link 这些 sheet 那样。
我有 dput() 上面的插图表格。很抱歉代码太长。我真的试图将它最小化到只需要必要的行和列。
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), class = "Date"),
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 = structure(c(14246, 14249, 14250, 14251,
14252, 14253, 14256, 14257, 14258, 14259, 14260, 14264, 14265,
14266, 14267, 14270, 14271, 14272, 14273, 14274, 14277, 14278,
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14435, 14438, 14439, 14440, 14441, 14442, 14445, 14446, 14447,
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14461, 14462, 14463, 14466, 14467, 14468, 14469, 14470, 14473,
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14525, 14526, 14529, 14530, 14531, 14532, 14533, 14536, 14537,
14538, 14539, 14540, 14543, 14544, 14545, 14546, 14547, 14550,
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14602, 14606, 14607, 14608, 14609, 14613, 14614, 14615, 14616,
14617, 14620, 14621, 14622, 14623, 14624, 14628, 14629, 14630,
14631, 14634, 14635, 14636, 14637, 14638, 14641, 14642, 14643,
14644, 14645, 14648, 14649, 14650, 14651, 14652, 14656, 14657,
14658, 14659, 14662, 14663, 14664, 14665, 14666, 14669, 14670,
14671, 14672, 14673, 14676, 14677, 14678, 14679, 14680, 14683,
14684, 14685, 14686, 14687, 14690, 14691, 14692, 14693, 14694,
14697, 14698, 14699), class = "Date"), `46069S109` = c(NA, -0.0161453077699294,
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0.00215866162978948, 0.0360796984383413, 0.0103950103950105,
0.00154320987654309, -0.00410888546481758, -0.0268179473955647,
0.0196078431372548, -0.0119542619542618, -0.0268279852709101,
-0.0205405405405405, -0.0187637969094923, -0.00731158605174368,
0.0186968838526913, -0.0122358175750834, 0.00394144144144126,
-0.0112170499158721, 0.00283607487237667, -0.0147058823529411,
-0.0195177956371988, 0.00409836065573772, -0.0186588921282799,
0.00772430184194905, -0.00117924528301884, 0.0070838252656433,
-0.0169988276670574, -0.00298151460942163, 0.00239234449760782,
0.0405727923627685, -0.173738532110092, 0.029146426092991, 0.00134861766689141,
0.0181818181818182, -0.0112433862433862, -0.0153846153846153,
0.00475543478260859, -0.0182555780933063, 0.00757575757575766,
0.00546821599453179, 0.0319510537049625, 0.00527009222661397,
-0.00196592398427257, -0.010505581089954, -0.0311877903118779,
-0.0383561643835617, 0.0334757834757835, 0.00551343900758098,
0.0274160383824538, 0.00733822548365573, 0.00860927152317886,
0.00328299409061057, 0.00654450261780114, -0.0156046814044213,
-0.0204755614266843, 0.0114632501685772, 0.0233333333333333,
0.0130293159609121, -0.0192926045016078)), row.names = c(NA,
313L), class = "data.frame")```
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")