R 循环线性回归

作为R区的乞丐，我有一个可能很简单的问题

我对本规范进行了线性回归：

X1=X1\u t-h+X2\u t-h

的h等于1,2,3,4,5：

例如，当h=1时，我运行以下代码：

  Modelo11 <- dynlm(X1 ~ L(X1,1) + L(X2, 1)-1, data = GDP)

这是我的data.frame：

GDP <- data.frame(data )
 GDP
              X1           X2
1    0.542952690  0.226341364
2    0.102328393  0.743360185
3    0.166345969  0.186533485
4    1.406733422  1.392420181
5   -0.469811005 -0.114609464
6   -0.509268267  0.687555461
7    1.470439930  0.298655018
8    1.046456428 -1.056387597
9   -0.492462197 -0.530284962
10  -0.516065519  0.645957530
11   0.624638996  1.044731264
12   0.213616470 -1.652979785
13   0.669747432  1.398602289
14   0.552089131 -0.821013792
15   0.452715216  1.420094663
16  -0.892063248 -1.436600779
17   1.429284965  0.559738610
18   0.853740565 -0.898976767
19   0.741864168  1.352012831
20   0.171494650  1.704764705
21   0.422326351 -0.267064235
22  -1.261643503 -2.090694608
23  -1.321086283 -0.273954212
24   0.365226000  1.965167113
25  -0.080888690 -0.594498893
26  -0.183293801 -0.483053404
27  -1.033792032  0.586491772
28   0.718322432  1.776210145
29  -2.822693790 -0.731509917
30  -1.251740437 -1.918124078
31   1.184256949 -0.016548037
32   2.255202675  0.303438286
33  -0.930446147  0.803126180
34  -1.691383225 -0.157839283
35  -1.081643279 -0.006652717
36   1.034162006 -1.970063305
37  -0.716827488  0.306792930
38   0.098471514  0.338333164
39   0.343536547  0.389775011
40   1.442117465 -0.668885360
41   0.095131066 -0.298356861
42   0.222524607  0.291485267
43  -0.499969717  1.308312472
44   0.588162304  0.026539575
45   0.581215173  0.167710855
46   0.629343124 -0.052835206
47   0.811618963  0.716913172
48   1.463610069 -0.356369304
49  -2.000576321  1.226446201
50   1.278233553  0.313606888
51  -0.700373666  0.770273988
52  -1.206455648  0.344628878
53   0.024602262  1.001621886
54   0.858933385 -0.865771777
55  -1.592291995 -0.384908852
56  -0.833758365 -1.184682199
57  -0.281305858  2.070391729
58  -0.122848757 -0.308397782
59  -0.661013984  1.590741535
60   1.887869805 -1.240283364
61  -0.313677463 -1.393252994
62   1.142864110 -1.150916732
63  -0.633380499 -0.223923970
64  -0.158729527 -1.245647224
65   0.928619010 -1.050636078
66   0.424317087  0.593892028
67   1.108704956 -1.792833100
68  -1.338231248  1.138684394
69  -0.647492569  0.181495183
70   0.295906675 -0.101823172
71  -0.079827607  0.825158278
72   0.050353111 -0.448453121
73   0.129068772  0.205619797
74  -0.221450137  0.051349511
75  -1.300967949  1.639063824
76  -0.861963677  1.273104220
77  -1.691001610  0.746514122
78   0.365888734 -0.055308006
79   1.297349754  1.146102001
80  -0.652382297 -1.095031447
81   0.165682952 -0.012926971
82   0.127996446  0.510673745
83   0.338743162 -3.141650682
84  -0.266916587 -2.483389321
85   0.148135154 -1.239997153
86   1.256591385  0.051984536
87  -0.646281986  0.468210275
88   0.180472423  0.393014848
89   0.231892902 -0.545305005
90  -0.709986273  0.104969765
91   1.231712844 -1.703489840
92   0.435378714  0.876505107
93  -1.880394798 -0.885893722
94   1.083580732  0.117560662
95  -0.499072654 -1.039222894
96   1.850756855 -1.308752222
97   1.653952857  0.440405804
98  -1.057618294 -1.611779530
99  -0.021821282 -0.807071503
100  0.682923562 -2.358596342
101 -1.132293845 -1.488806929
102  0.319237353  0.706203968
103 -2.393105781 -1.562111727
104  0.188653972 -0.637073832
105  0.667003685  0.047694037
106 -0.534018861  1.366826933
107 -2.240330371 -0.071797320
108 -0.220633546  1.612879694
109 -0.022442941  1.172582601
110 -1.542418139  0.635161458
111 -0.684128812 -0.334973482
112  0.688849615  0.056557966
113  0.848602803  0.785297518
114 -0.874157558 -0.434518305
115 -0.404999060 -0.078893114
116  0.735896917  1.637873669
117 -0.174398836  0.542952690
118  0.222418628  0.102328393
119  0.419461884  0.166345969
120 -0.042602368  1.406733422
121  2.135670836 -0.469811005
122  1.197644287 -0.509268267
123  0.395951293  1.470439930
124  0.141327444  1.046456428
125  0.691575897 -0.492462197
126 -0.490708151 -0.516065519
127 -0.358903359  0.624638996
128 -0.227550909  0.213616470
129 -0.766692832  0.669747432
130 -0.001690915  0.552089131
131 -1.786701123  0.452715216
132 -1.251495762 -0.892063248
133  1.123462446  1.429284965
134  0.237862653  0.853740565

---

GDP您的变量
Modelo1
是一个无法存储
lm
对象的向量。当
Modelo1
是一个列表时，它应该可以工作

library(dynlm)

df<-data.frame(rnorm(50),rnorm(50))
names(df)<-c("a","b")

c<-list()

for(h in 1:5){
c[[h]] <- dynlm(a ~ L(a,h) + L(b, h)-1, data = df)
}

*编辑以回应Richard Scriven的评论

获取所有摘要的最有效方法是：

lapply(c, summary) 

这将对列表的每个元素应用summary函数，并返回一个包含结果的列表。
c
将成为一个列表。但是我会编辑代码让它更清晰。非常感谢，亚历克斯！如何获得模型的摘要？例如，对于h=3中的模型：
summary（c3）
我编辑了上面的答案以回答您的问题。对于所有这些模型，
lappy（c，summary）
这将是一种优雅的方式：）
library(dynlm)

df<-data.frame(rnorm(50),rnorm(50))
names(df)<-c("a","b")

c<-list()

for(h in 1:5){
c[[h]] <- dynlm(a ~ L(a,h) + L(b, h)-1, data = df)
}

summary(c[[1]])

lapply(c, summary)