线性回归并将结果存储在数据框中 [英] Linear Regression and storing results in data frame

问题描述

我正在对数据框中的某些变量进行线性回归.我希望能够通过分类变量对线性回归进行子集化，为每个分类变量运行线性回归，然后将t统计量存储在数据框中.如果可能的话，我希望没有循环地进行此操作.

I am running a linear regression on some variables in a data frame. I'd like to be able to subset the linear regressions by a categorical variable, run the linear regression for each categorical variable, and then store the t-stats in a data frame. I'd like to do this without a loop if possible.

这是我要做的事的一个示例:

Here's a sample of what I'm trying to do:

  a<-  c("a","a","a","a","a",
         "b","b","b","b","b",
         "c","c","c","c","c")     
  b<-  c(0.1,0.2,0.3,0.2,0.3,
         0.1,0.2,0.3,0.2,0.3,
         0.1,0.2,0.3,0.2,0.3)
  c<-  c(0.2,0.1,0.3,0.2,0.4,
         0.2,0.5,0.2,0.1,0.2,
         0.4,0.2,0.4,0.6,0.8)
      cbind(a,b,c)

我可以通过运行以下线性回归并非常容易地得出t统计量来开始:

I can begin by running the following linear regression and pulling the t-statistic out very easily:

  summary(lm(b~c))$coefficients[2,3]

但是，我希望能够在a列是a，b或c时运行回归.然后，我想将t-stats存储在一个看起来像这样的表中:

However, I'd like to be able to run the regression for when column a is a, b, or c. I'd like to then store the t-stats in a table that looks like this:

variable t-stat
a        0.9
b        2.4
c        1.1

希望如此.如果您有任何建议，请告诉我！

Hope that makes sense. Please let me know if you have any suggestions!

推荐答案

以下是对plyr软件包和ddply()的投票.

Here's a vote for the plyr package and ddply().

plyrFunc <- function(x){
  mod <- lm(b~c, data = x)
  return(summary(mod)$coefficients[2,3])
  }

tStats <- ddply(dF, .(a), plyrFunc)
tStats
  a         V1
1 a  1.6124515
2 b -0.1369306
3 c  0.6852483

这篇关于线性回归并将结果存储在数据框中的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！