1

受SO 这个答案的启发，我正在使用 teh do 一次执行多个回归，但是我想使用显示我的输出并do()生成一个rowwise_df对象，但是如果我提取回归列表，一些信息似乎已经丢失。有没有简单的方法来解决这个问题？下面的最小示例。

首先是一些必需的包

# install.packages(c("tidyverse", "broom", "texreg"), dependencies = TRUE)
library(tidyverse)

二、一些虚拟数据 

df.h = data.frame( 
  hour     = factor(rep(1:6, each = 21)),
  price    = runif(504, min = -10, max = 125),
  wind     = runif(504, min = 0, max = 2500),
  temp     = runif(504, min = - 10, max = 25)  
)

第三个do()

dfHour = df.h %>% group_by(hour) %>%
  do(fitHour = lm(price ~ wind + temp, data = .))

第四，整齐地按组获取系数data_frame

library(broom)
dfHourCoef = tidy(dfHour, fitHour)
dfHourCoef

#> # A tibble: 72 x 6
#> # Groups:   hour [6]
#>    hour  term          estimate std.error statistic  p.value
#>    <fct> <chr>            <dbl>     <dbl>     <dbl>    <dbl>
#>  1 1     (Intercept)  78.2       17.6       4.44    0.000316
#>  2 1     wind          0.000145   0.0107    0.0135  0.989   
#>  3 1     temp        - 1.27       0.834    -1.52    0.145   
#>  4 2     (Intercept)  69.7       18.9       3.68    0.00171 
#>  5 2     wind        - 0.0150     0.0121   -1.24    0.232   
#>  6 2     temp        - 0.00355    0.989    -0.00359 0.997   
#>  7 3     (Intercept)  61.0       14.1       4.32    0.000413
#>  8 3     wind        - 0.00599    0.00987  -0.607   0.552   
#>  9 3     temp          0.603      0.704     0.858   0.402   
#> 10 4     (Intercept)  57.9       19.1       3.02    0.00729 
#> # ... with 8 more rows

但是，我想使用，我尝试过类似的方法，但是输出以某种方式被打乱了。任何帮助，将不胜感激。

library(texreg)
class(dfHour[[2]])
#> [1] "list"
screenreg(dfHour[[2]]) # Not working

手动操作看起来像这样，

fit1 <- lm(price ~ wind + temp, data = subset(df.h, hour == 1))
fit2 <- lm(price ~ wind + temp, data = subset(df.h, hour == 2))
fit3 <- lm(price ~ wind + temp, data = subset(df.h, hour == 3))
fit4 <- lm(price ~ wind + temp, data = subset(df.h, hour == 4))
fit5 <- lm(price ~ wind + temp, data = subset(df.h, hour == 5))
fit6 <- lm(price ~ wind + temp, data = subset(df.h, hour == 6))

fits <- list(fit1, fit2, fit3, fit4, fit5, fit6)
texreg::screenreg(fits)
#> =================================================================================
#>              Model 1     Model 2    Model 3     Model 4    Model 5     Model 6   
#> ---------------------------------------------------------------------------------
#> (Intercept)   78.23 ***   69.73 **   60.96 ***   57.87 **   89.18 ***   64.29 ***
#>              (17.62)     (18.94)    (14.11)     (19.14)    (19.08)     (15.62)   
#> wind           0.00       -0.01      -0.01       -0.01      -0.01        0.00    
#>               (0.01)      (0.01)     (0.01)      (0.01)     (0.01)      (0.01)   
#> temp          -1.27       -0.00       0.60        1.39      -0.48       -2.17 *  
#>               (0.83)      (0.99)     (0.70)      (0.94)     (0.98)      (0.93)   
#> ---------------------------------------------------------------------------------
#> R^2            0.11        0.08       0.05        0.11       0.06        0.23    
#> Adj. R^2       0.02       -0.02      -0.05        0.01      -0.05        0.15    
#> Num. obs.     21          21         21          21         21          21       
#> RMSE          35.24       41.60      32.59       41.44      39.87       38.39    
#> =================================================================================
#> *** p < 0.001, ** p < 0.01, * p < 0.05#>
4

1 回答 1

1

我们可以pull“fitHour”并申请screenreg

library(texreg)
out <- dfHour %>% 
         pull(fitHour) %>% 
         screenreg

-输出

出去

#================================================================================================================================================================================================================================================================================
#             Model 1  Model 2  Model 3   Model 4    Model 5    Model 6     Model 7  Model 8    Model 9    Model 10    Model 11    Model 12    Model 13   Model 14    Model 15   Model 16  Model 17   Model 18   Model 19  Model 20   Model 21  Model 22    Model 23   Model 24  
#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#(Intercept)   32.42    26.59    43.68 *   52.69 **   48.22 **   70.75 ***   48.65    51.23 **   63.47 **   68.99 ***  102.76 ***   64.77 ***   77.99 **   82.14 ***   50.16 **   50.87 *   69.29 **   64.07 **   31.96     66.61 **   44.88 *   88.75 ***   47.27 **   83.94 ***
#             (19.57)  (19.59)  (15.44)   (16.96)    (16.05)    (15.81)     (25.35)  (15.93)    (20.87)    (15.09)     (15.62)     (16.50)     (20.63)    (12.96)     (16.84)    (20.74)   (19.68)    (22.24)    (19.97)   (19.48)    (21.17)   (17.92)     (14.79)    (19.90)   
#wind           0.02     0.02     0.00      0.00       0.01      -0.00       -0.01     0.00      -0.01      -0.00       -0.04 ***    0.01       -0.01      -0.01       -0.00      -0.00     -0.00       0.00       0.02      0.00      -0.00     -0.03 *      0.01      -0.01    
#              (0.01)   (0.01)   (0.01)    (0.02)     (0.01)     (0.01)      (0.01)   (0.01)     (0.01)     (0.01)      (0.01)      (0.01)      (0.01)     (0.01)      (0.01)     (0.01)    (0.01)     (0.01)     (0.01)    (0.01)     (0.01)    (0.01)      (0.01)     (0.01)   
#temp           0.33     0.57    -0.97     -0.09      -0.65      -0.95        0.99    -0.56      -0.27       1.21       -0.84       -0.82       -0.76      -0.67        0.66      -0.02     -0.50       0.62       0.21     -0.75       1.29      0.60        1.04       0.40    
#              (0.90)   (0.85)   (0.76)    (0.93)     (0.67)     (0.90)      (1.09)   (1.05)     (0.91)     (0.91)      (0.72)      (0.90)      (0.90)     (0.56)      (1.06)     (1.05)    (1.06)     (0.77)     (0.94)    (0.98)     (1.00)    (0.87)      (0.73)     (1.05)   
#--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
#R^2            0.10     0.11     0.10      0.00       0.07       0.06        0.07     0.02       0.01       0.10        0.52        0.05        0.06       0.16        0.02       0.00      0.02       0.04       0.10      0.03       0.09      0.20        0.18       0.08    
#Adj. R^2      -0.00     0.01    -0.00     -0.11      -0.04      -0.04       -0.03    -0.09      -0.10      -0.01        0.47       -0.06       -0.05       0.06       -0.09      -0.11     -0.09      -0.07       0.00     -0.07      -0.01      0.12        0.08      -0.03    
#Num. obs.     21       21       21        21         21         21          21       21         21         21          21          21          21         21          21         21        21         21         21        21         21        21          21         21       
#RMSE          36.96    37.34    32.60     45.40      35.19      41.34       42.79    39.51      38.83      35.61       28.44       39.39       38.70      30.11       40.61      40.08     40.88      40.13      41.68     43.08      42.78     39.12       30.62      40.79    
#================================================================================================================================================================================================================================================================================

如果我们需要申请“dfHour”的个别型号

dfHour2 <- dfHour %>%
             ungroup %>%
             mutate(Texreg = map(fitHour, screenreg))

整个练习也可以在没有do的情况下完成

df.h %>%
     group_by(hour) %>% 
     nest(-hour) %>% 
     mutate(model = map(data,  ~ {
         mod <- lm(price ~ wind + temp, data = .x)
        tibble(list(mod), Texreg = list(screenreg(mod)))})) %>%
    select(-data) %>%
    unnest
于 2018-04-20T11:01:35.927 回答