如何使用R对数据集的起点执行线性回归

时间:2015-02-16 08:04:48

标签: r linear-regression

如何使用日期约束执行回归?我只想对数据集的“非零”部分进行回归。

主要问题是第2栏和第2栏。 3从不同的Dates&我编写了一个循环,它将执行从起点(2012-01-01)到2013-12-31的回归,每次迭代都会增加6个月的间隔。

DATES <- c("201201/201206", "201201/201212", "201201/201306", "201201/201312")

如何执行回归以便使用第2列和第2列的起点。在给定的迭代中3?

以下是RETS数据集:

RETS <- structure(c(0.0159, 0.0016, 0.0027, -0.0026, 0.0024, 0.0086, 
6e-04, 0.0024, -0.0052, 0.0039, 0.0111, 0.0053, 0.0037, -0.0026, 
-0.0011, 0.0083, -0.0051, -4e-04, -0.0035, -4e-04, 0.0088, 0.0015, 
0.014, -6e-04, 0.0025, 0.0029, 0.0013, -0.0074, 0.0074, -0.0013, 
-0.0046, 0.0111, 0.0027, 5e-04, -0.0033, 0.0045, 0.0022, 0.0017, 
0.0029, -0.0039, 0.0051, -0.003, -0.0041, -0.0146, 0.007, 0.0099, 
0.0039, 1e-04, 0.0181, -0.0011, 0.0059, 0.0014, 0.0039, -0.0029, 
-0.0017, -0.0072, 0.0033, 0.014, -0.0031, -0.0049, -0.0017, 0.0042, 
0.0073, -0.004, -0.01, -5e-04, -0.0113, -0.0168, 0.0081, 0.0131, 
-0.0119, -6e-04, 0.0148, -0.0034, -0.0064, 0.0017, -0.0084, 0.0038, 
0.0137, 0.007, 0.0016, -0.0037, 0.0063, -0.003, -0.0076, -0.0162, 
8e-04, -0.004, -0.006, 0.0021, -0.003, -0.0111, -0.0058, -0.0038, 
-0.0148, -0.0086, 0.0172, 0.0018, 5e-04, 0.002, -0.0033, 0.0121, 
-0.0145, -0.0022, -0.0252, -5e-04, 0.0076, 0.0225, 6e-04, 0.0079, 
-0.0127, 0.0115, -0.0065, 0.0107, 0.0102, 0.0019, 0.0097, -0.0016, 
-0.0225, 0.0077, -0.016, 0.0051, 0.009, -0.0029, 0.025, 0.003, 
0.0066, -0.0045, -0.0095, -0.0012, -0.0087, 2e-04, -0.0049, 0.0168, 
-0.0024, 0.0068, 0.0074, 0.0027, -0.0092, -0.0101, -0.0086, 2e-04, 
0.0164, 0.0185, 0, -0.007, -9e-04, -0.0069, 0.0198, 0.002, 0.0051, 
0.0012, 8e-04, 0.0016, -5e-04, 1e-04, 0.0011, 0.0074, 0.0013, 
1e-04, -0.003, 4e-04, -0.0082, 0.0061, 1e-04, -0.001, 8e-04, 
-0.0072, 0.0047, -9e-04, -8e-04, 0.0203, 0.0039, -0.0057, 0.0028, 
0.0034, 0.0152, 0.0045, -0.0034, -8e-04, 5e-04, 1e-04, -4e-04, 
-0.0015, -0.0106, -0.0056, 0.0094, -0.0046, 0.0026, 0.0011, 0.0041, 
0.0071, 1e-04, -0.0034, -0.0099, -0.0064, 6e-04, -0.0033, 0.0084, 
0.0101, 0.0045, -0.0026, -0.0167, 1e-04, -0.0139, -0.0029, 0.003, 
-6e-04, 0, 0.0104, -0.0089, 0.0021, 0.0078, -0.0227, -0.012, 
8e-04, 8e-04, -0.0035, -0.0135, -0.0017, 0.0049, 0.0202, 5e-04, 
0.0019, 0.0136, -0.0021, -0.0051, 0.008, 0.0047, 2e-04, -0.0049, 
-0.0015, 0.0018, 0.0034, 0.003, 4e-04, 0.0068, 5e-04, -0.0061, 
-0.0037, 0.0117, 0.0111, -0.0074, 0.0057, -0.0091, -0.0031, -0.0042, 
-0.0013, -0.0108, 0.017, 0.0256, -0.0023, 0.0044, -0.0027, -0.0029, 
0.0026, 0.008, -1e-04, -7e-04, 7e-04, -1e-04, 0.0064, 0.0022, 
0.0054, 0.0016, 3e-04, 0.0056, -0.0012, 0.004, -0.0039, -0.0025, 
0.0104, -0.0113, 0.0101, 8e-04, -0.0013, 0.0055, -2e-04, 0.0017, 
8e-04, 9e-04, -0.0012, 0.0075, -0.0125, -0.006, 0.0097, -0.019, 
0.0068, 0.0126, -0.002, 0.0033, 0.0053, 0.009, 0.0013, 0.0018, 
0.0042, 0.0038, -0.0023, 0.0015, 0.0053, -0.0013, -0.0055, -0.0023, 
0.007, -0.0086, 0.0081, -0.0042, 0.008, 0, 0.0031, -0.004, 0.0049, 
-0.0101, 0.004, -0.0045, 0.0067, 0.0034, 0.0123, 0.0033, -0.0025, 
-0.0231, 0.0148, -0.0146, -0.0063, 0.0087, 0.0044, 0.0103, 6e-04, 
0.0041, -0.0018, 0.0067, 0.0023, -0.0088, 0.0093, 0.0101, 0.0026, 
0.0051, 0.0046, -0.0029, 0.0032, 8e-04, 0.0103, 0.0054, -0.0047, 
0.0096, -1e-04, 0.0015, -0.0074, -0.0029, -8e-04, 0.006, -0.0065, 
0.0037, -0.0144, 0.0055, -0.0048, -0.014, 0.0091, 0.0127, 0, 
-0.0103, -0.0083, 0.0152, -0.0063, 0.0077, 0.0079, -0.0138, -0.0248, 
0.0032, -0.0127, 0.0096, 0.0099, 0.0059, -0.0041, 0.0058, -0.001, 
4e-04, 0.0108, 0.0057, 0.0072, 4e-04, 0.0136, 4e-04, 0.0038, 
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0.0022, 0.0051, -1e-04, -2e-04, 0.0047, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 
0, 0, 0, 0, 0, -0.1099, -0.089, 0.0323, 0.0322, -0.0339, -0.0962, 
-0.0225, 0.05, -0.0635, -0.0296, -0.0383, 0.0363, -0.0186, 0.03, 
-0.0033, 0.0144, -0.0047, 0.0374, 0.0608, 0.0467, 0.0159, -0.0097, 
0.0076, 0.038, -0.03, 0.0324, -0.0263, -0.027, -0.0083, -0.0106, 
0.014, 0.0087, 0.0083, 0.0139, -0.0218, -0.0159, -0.0052, -0.0029, 
-0.0804, -0.0057, 0.0363, -0.0038, -0.0083, -3e-04, -0.0104, 
0.0313, -0.0849, -0.1169, -0.0236, -0.0622, -0.0382, -0.0402, 
0.0524, 0.0394, -0.0547, 0, 0.014, 0.0381, -0.0096, -0.0565, 
0.0402, -0.0627, -0.0413, 0.0504, -0.0425, 0.0146, 0, -0.0015, 
-0.0134, 0.0099, -0.0124, -5e-04, -0.054, -0.0183, 0.0479, 0.0205, 
0.0011, -0.009, 0.033, 0.0772, -0.0105, 0.0623, -0.0218, 0.0163, 
0.0649, -0.0301, 0.012, -0.0906, -0.0245, 0.0168, -0.0145, 0.0659, 
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-0.0083, 0.0097, -0.0159, 0.0361, 0.0029, 0.0084, 0.0148, 0.0295, 
-0.0517, 0.0146, -0.0083, -0.0403, -0.0545, 0.0189, 0.0143, -0.0138, 
-0.0035, -0.0102, -0.0315, 0.0197, 0.0211, -0.0063, 0.0215, -0.0162, 
-0.0415, -0.0055, 0.0052, 0.0044, -0.019, 0.0141, 0.0194, -0.0022, 
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3L), .Dimnames = list(NULL, c("SPY", "FB", "AAPL")))

这是我用来执行回归的循环。由于RETS中有3个资产,DATES中有4个不同的日期,因此会返回12中存储的BETA次回归

BETA <- data.frame()
ALL <- data.frame()

for (k in 1:length(DATES)){
for (i in 1:ncol(RETS)){
  rets <- RETS[DATES[k]]
  MOD  <- lm(rets$SPY ~ rets[,i])
  COEF <- cbind(coef(MOD)[1],coef(MOD)[2])
  BETA <- rbind(BETA,COEF)  
}

ALL <- merge(ALL, BETA)

}

1 个答案:

答案 0 :(得分:1)

用NA替换零。在这种情况下,na.action = na.omit的默认lm参数将自动删除它们。

RETS.na <- replace(RETS, RETS == 0, NA)

或者如果要删除任何列中有NA的所有行,则:

RETS.na <- na.omit(RETS.na)

要获取DATES[1]的点子集,请说:

RETS.na[ DATES[1] ]