如何在 R 中为预定义的向量创建回归模型列表？

要为预定义的向量创建回归模型列表，我们可以创建一个空列表，然后使用 for 循环来创建回归模型列表。例如，如果我们有两个向量，假设为 x 和 y，并且我们想要在 x 和 y 之间创建回归模型列表，那么我们使用 list() 创建一个空列表，并按特定次数执行 for 循环，如下面示例所示。

示例 1

 在线演示

x<-rnorm(20)
y<-rnorm(20)
List_1=list()
for (i in 1:10) List_1[[i]] = lm(y~x)
List_1

输出

[[1]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[2]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[3]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[4]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[5]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[6]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[7]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[8]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[9]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

[[10]]
Call:
lm(formula = y ~ x)
Coefficients:
(Intercept) x
   -0.1294 0.2600

示例 2

 在线演示

x1<-rpois(2000,1)
x2<-rpois(2000,2)
x3<-rpois(2000,2)
y1<-rpois(2000,5)
List_2=list()
for (i in 1:10) List_2[[i]] = lm(y1~x1+x2+x3)
List_2

输出

[[1]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
   5.065561 -0.039094 0.006488 -0.035636

[[2]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
   5.065561 -0.039094 0.006488 -0.035636

[[3]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
   5.065561 -0.039094 0.006488 -0.035636

[[4]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

[[5]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
   5.065561 -0.039094 0.006488 -0.035636

[[6]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

[[7]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

[[8]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

[[9]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

[[10]]
Call:
lm(formula = y1 ~ x1 + x2 + x3)
Coefficients:
(Intercept) x1 x2 x3
 5.065561 -0.039094 0.006488 -0.035636

更新时间：06-Mar-2021

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