Question

我正在做多个OLS回归。我使用了以下lm函数：

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

这给了我摘要输出：

    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

这很完美。但是，我总共进行了10次多次OLS回归，我希望在数据框中创建自己的摘要输出，我在其中为所有10个分析提取截距估计值，tvalue估计值和p值。因此它将是10x3，其中列名称将是Model1，Model2，..，Model10和行名称：Value，t-value和p-Value。

我感谢任何帮助。

Answer 1

有一些软件包（stargazer和texreg）以及outreg的代码。

无论如何，如果你只对拦截感兴趣，这里有一种方法：

# Estimate a bunch of different models, stored in a list
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
do.call(cbind, lapply(fits, function(z) summary(z)$coefficients["(Intercept)", ]))


# 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

Answer 2

查看broom包，它是为了满足您的要求而创建的。唯一的区别是它将模型放入行中，将不同的统计信息放入列中，我理解您更喜欢相反的情况，但如果真的有必要，您可以解决这个问题。

举个例子，函数tidy()将模型输出转换为数据帧。

model <- lm(mpg ~ cyl, data=mtcars)
summary(model) 

Call:
lm(formula = mpg ~ cyl, data = mtcars)

Residuals:
    Min      1Q  Median      3Q     Max 
-4.9814 -2.1185  0.2217  1.0717  7.5186 

Coefficients:
            Estimate Std. Error t value Pr(>|t|)    
(Intercept)  37.8846     2.0738   18.27  < 2e-16 ***
cyl          -2.8758     0.3224   -8.92 6.11e-10 ***
---
Signif. codes:  0 ‘***’ 0.001 ‘**’ 0.01 ‘*’ 0.05 ‘.’ 0.1 ‘ ’ 1

Residual standard error: 3.206 on 30 degrees of freedom
Multiple R-squared:  0.7262,    Adjusted R-squared:  0.7171 
F-statistic: 79.56 on 1 and 30 DF,  p-value: 6.113e-10

和

 library(broom)
 tidy(model)

产生以下数据框：

         term estimate std.error statistic      p.value
1 (Intercept) 37.88458 2.0738436 18.267808 8.369155e-18
2         cyl -2.87579 0.3224089 -8.919699 6.112687e-10

查看?tidy.lm以查看更多选项，例如置信区间等。

要将十个模型的输出合并为一个数据框，您可以使用

library(dplyr)
bind_rows(one, two, three, ... , .id="models")

或者，如果您的不同模型来自使用相同数据框的回归，则可以将其与dplyr结合使用：

models <- mtcars %>% group_by(gear) %>% do(data.frame(tidy(lm(mpg~cyl, data=.), conf.int=T)))

Source: local data frame [6 x 8]
Groups: gear

  gear        term  estimate std.error statistic      p.value  conf.low  conf.high
1    3 (Intercept) 29.783784 4.5468925  6.550360 1.852532e-05 19.960820 39.6067478
2    3         cyl -1.831757 0.6018987 -3.043297 9.420695e-03 -3.132080 -0.5314336
3    4 (Intercept) 41.275000 5.9927925  6.887440 4.259099e-05 27.922226 54.6277739
4    4         cyl -3.587500 1.2587382 -2.850076 1.724783e-02 -6.392144 -0.7828565
5    5 (Intercept) 40.580000 3.3238331 12.208796 1.183209e-03 30.002080 51.1579205
6    5         cyl -3.200000 0.5308798 -6.027730 9.153118e-03 -4.889496 -1.5105036

来自多个回归输出的汇总数据框

2 个答案: