来自多个回归输出的摘要数据帧 [英] summary dataframe from several multiple regression outputs

问题描述

我正在做多个OLS回归。我使用了以下lm函数：

  GroupNetReturnsStockPickers<  -  read.csv（GroupNetReturnsStockPickers.csv，header = 
 ModelGroupNetReturnsStockPickers<  -  lm（StockPickersNet〜Mkt.RF + SMB + HML + WML，data = GroupNetReturnsStockPickers）
名称（GroupNetReturnsStockPickers）
总结（ModelGroupNetReturnsStockPickers）
  

哪些给我的摘要输出：

 调用：
 lm（公式= StockPickersNet〜Mkt.RF + SMB + HML + WML，data = GroupNetReturnsStockPickers）
 
剩余：
最小1Q中位数3Q最大
 -0.029698 -0.005069 -0.000328 0.004546 0.041948 
 
系数：
估计标准错误t值Pr（> | t |）
（截取）4.655e-05 5.981e-04 0.078 0.938 
 Mkt.RF -1.713e-03 1.202e-02 -0.142 0.887 
 SMB 3.006e-02 2.545e-02 1.181 0.239 
 HML 1.970e-02 2.350e-02 0.838 0.403 
 WML 1.107e-02 1.444e-02 0.766 0.444 
 
残差标准错误：251自由度上的0.009029 
多个R平方：0.01033，调整后的R平方：-0.005445 
 F统计：0.6548在4和251 DF，p值：0.624 
  

这是完美的。然而，我正在做10次多重OLS回归，而且我想在数据框中创建自己的汇总输出，其中我提取了所有10个分析的截取估计值，t值估计值和p值。 。因此，它将是一个10x3，其中列名称将是Model1，Model2，..，Model10和行名称：Value，t-value和p-Value。

感谢任何帮助。

解决方案

有几个软件包执行此操作（stargazer和texreg）以及 outreg 。

无论如何，如果你只对这里的拦截感兴趣是一种方法：

 适合< -  list（）＃创建空列表来存储模型
适合$ model1<  -  lm（Ozone〜Solar.R，data = airquality）
适合$ model2<  -  lm（Ozone〜Solar.R + Wind，data = airquality）
适合$ model3<  -  lm（Ozone〜Solar.R + Wind + Temp，data = airquality）
 
＃组合拦截的结果
 smry<  -  do.call（rbind ，$（$）
 rownames（smry）<  -  paste0（model，1：3）
t（ smry）
 
＃RESULT：
＃model1 model2 model3 
＃估算18.598727772 7.724604e + 01 -64.342078929 
＃标准错误6.747904163 9.067507e + 00 23.054724347 
＃t值2.756222869 8.518995e + 00 -2.790841389 
＃Pr（> | t |）0.006856021 1.052118e-13 0.006226638 
  

I am doing multiple OLS regressions. I have used the following lm function:

GroupNetReturnsStockPickers <- read.csv("GroupNetReturnsStockPickers.csv", header=TRUE, sep=",", dec=".")
ModelGroupNetReturnsStockPickers <- lm(StockPickersNet ~ Mkt.RF+SMB+HML+WML, data=GroupNetReturnsStockPickers)
names(GroupNetReturnsStockPickers)
summary(ModelGroupNetReturnsStockPickers)

Which gives me the summary output of:

    Call:
  lm(formula = StockPickersNet ~ Mkt.RF + SMB + HML + WML, data = GroupNetReturnsStockPickers)

Residuals:
  Min        1Q    Median        3Q       Max 
-0.029698 -0.005069 -0.000328  0.004546  0.041948 

Coefficients:
              Estimate Std. Error t value Pr(>|t|)
(Intercept)  4.655e-05  5.981e-04   0.078    0.938
Mkt.RF      -1.713e-03  1.202e-02  -0.142    0.887
SMB          3.006e-02  2.545e-02   1.181    0.239
HML          1.970e-02  2.350e-02   0.838    0.403
WML          1.107e-02  1.444e-02   0.766    0.444

Residual standard error: 0.009029 on 251 degrees of freedom
Multiple R-squared:  0.01033,   Adjusted R-squared:  -0.005445 
F-statistic: 0.6548 on 4 and 251 DF,  p-value: 0.624

This is perfect. However, I am doing a total of 10 multiple OLS regressions, and I wish to create my own summary output, in a data frame, where I extract the Intercept Estimate, the tvalue estimate, and the p-value, for all 10 analyzes individually. Hence it would be a 10x3, where the columns names would be Model1, Model2,..,Model10, and row names: Value, t-value and p-Value.

I appreciate any help.

解决方案

There's a few packages that do this (stargazer and texreg) as well as this code for outreg.

In any case, if you are only interested in the intercept here is one approach:

fits <- list() # Create empty list to store models
fits$model1 <- lm(Ozone ~ Solar.R, data = airquality)
fits$model2 <- lm(Ozone ~ Solar.R + Wind, data = airquality)
fits$model3 <- lm(Ozone ~ Solar.R + Wind + Temp, data = airquality)

# Combine the results for the intercept
smry <- do.call(rbind, lapply(fits, function(z) t(summary(z)$coefficients[1, ])))
rownames(smry) <- paste0("model", 1:3)
t(smry)

# RESULT:
#                  model1       model2        model3
# Estimate   18.598727772 7.724604e+01 -64.342078929
# Std. Error  6.747904163 9.067507e+00  23.054724347
# t value     2.756222869 8.518995e+00  -2.790841389
# Pr(>|t|)    0.006856021 1.052118e-13   0.006226638

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