循环进行多元线性回归 [英] Loop for multiple linear regression

问题描述

我开始使用r，并且一直坚持分析我的数据.我有一个具有80列的数据框.第1列是因变量，从2到80列是自变量.我想执行78个多元线性回归，使模型的第一个自变量固定不变(第2列)，并创建一个列表，我可以在其中保存所有回归，以便以后可以使用AIC得分比较模型.我该怎么办?

Hi I’m starting to use r and am stuck on analyzing my data. I have a dataframe that has 80 columns. Column 1 is the dependent variable and from column 2 to 80 they are the independent variables. I want to perform 78 multiple linear regressions leaving the first independent variable of the model fixed (column 2) and create a list where I can to save all regressions to later be able to compare the models using AIC scores. how can i do it?

这是我的循环

data.frame

for(i in 2:80)

{
Regressions <- lm(data.frame$column1 ~ data.frame$column2 + data.frame [,i])  
}

推荐答案

使用 for 循环，我们可以初始化 list 来存储输出

With the for loop we can initialize a list to store the output

nm1 <- names(df1)[2:80]
Regressions <- vector('list', length(nm1))
for(i in seq_along(Regressions)) {
   Regressions[[i]] <- lm(reformulate(c("column2", nm1[i]), "column1"), data = df1)
  }

或使用 paste 代替 reformulate

for(i in seq_along(Regressions)) {
   Regressions[[i]] <- lm(as.formula(paste0("column1 ~ column2 + ", 
                                nm1[i])), data = df1)
  }

---

使用可复制的示例

---

Using a reproducible example

nm2 <- names(iris)[3:5]
Regressions2 <- vector('list', length(nm2))
for(i in seq_along(Regressions2)) {
    Regressions2[[i]] <- lm(reformulate(c("Sepal.Width", nm2[i]), "Sepal.Length"), data = iris)
 }



Regressions2[[1]]

#Call:
#lm(formula = reformulate(c("Sepal.Width", nm2[i]), "Sepal.Length"), 
#    data = iris)

#Coefficients:
# (Intercept)   Sepal.Width  Petal.Length  
#      2.2491        0.5955        0.4719  

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