用dplyr拟合多个回归模型

Question 1

我想使用dplyr每小时拟合一个模型（因子变量），但出现错误，而且我不太确定出什么问题了。

df.h <- data.frame( 
  hour     = factor(rep(1:24, each = 21)),
  price    = runif(504, min = -10, max = 125),
  wind     = runif(504, min = 0, max = 2500),
  temp     = runif(504, min = - 10, max = 25)  
)

df.h <- tbl_df(df.h)
df.h <- group_by(df.h, hour)

group_size(df.h) # checks out, 21 obs. for each factor variable

# different attempts:
reg.models <- do(df.h, formula = price ~ wind + temp)

reg.models <- do(df.h, .f = lm(price ~ wind + temp, data = df.h))

我尝试了各种变体，但无法使用。

Question 2

从2020年中期开始，tchakravarty的答案将失败。为了规避的新方法broom，并dpylr似乎互动，以下使用的broom::tidy，broom::augment并且broom::glance可以使用。我们只需要在内部使用它们，do()然后再unnest()进行修改即可。

library(dplyr)
library(broom)

df.h = data.frame( 
  hour     = factor(rep(1:24, each = 21)),
  price    = runif(504, min = -10, max = 125),
  wind     = runif(504, min = 0, max = 2500),
  temp     = runif(504, min = - 10, max = 25)  
)

df.h %>% group_by(hour) %>%
  do(fitHour = tidy(lm(price ~ wind + temp, data = .))) %>% 
  unnest(fitHour)
# # A tibble: 72 x 6
#    hour  term        estimate std.error statistic   p.value
#    <fct> <chr>          <dbl>     <dbl>     <dbl>     <dbl>
#  1 1     (Intercept)   82.4     18.1         4.55  0.000248 
#  2 1     wind         -0.0212   0.0108      -1.96  0.0655   
#  3 1     temp         -1.01     0.792       -1.28  0.218    
#  4 2     (Intercept)   25.9     19.7         1.31  0.206    
#  5 2     wind          0.0204   0.0131       1.57  0.135    
#  6 2     temp          0.680    1.01         0.670 0.511    
#  7 3     (Intercept)   88.3     15.5         5.69  0.0000214
#  8 3     wind         -0.0188   0.00998     -1.89  0.0754   
#  9 3     temp         -0.669    0.653       -1.02  0.319    
# 10 4     (Intercept)   73.4     14.2         5.17  0.0000639

df.h %>% group_by(hour) %>%
  do(fitHour = augment(lm(price ~ wind + temp, data = .))) %>% 
  unnest(fitHour)
# # A tibble: 24 x 13
#    hour  r.squared adj.r.squared sigma statistic p.value    df logLik   AIC   BIC deviance
#    <fct>     <dbl>         <dbl> <dbl>     <dbl>   <dbl> <dbl>  <dbl> <dbl> <dbl>    <dbl>
#  1 1        0.246        0.162    39.0     2.93   0.0790     2  -105.  218.  222.   27334.
#  2 2        0.161        0.0674   43.5     1.72   0.207      2  -107.  223.  227.   34029.
#  3 3        0.192        0.102    33.9     2.14   0.147      2  -102.  212.  217.   20739.
#  4 4        0.0960      -0.00445  34.3     0.956  0.403      2  -102.  213.  217.   21169.
#  5 5        0.230        0.144    31.7     2.68   0.0955     2  -101.  210.  214.   18088.
#  6 6        0.0190      -0.0900   39.8     0.174  0.842      2  -106.  219.  223.   28507.
#  7 7        0.0129      -0.0967   37.1     0.118  0.889      2  -104.  216.  220.   24801.
#  8 8        0.197        0.108    35.3     2.21   0.139      2  -103.  214.  218.   22438.
#  9 9        0.0429      -0.0634   39.4     0.403  0.674      2  -105.  219.  223.   27918.
# 10 10       0.0943      -0.00633  35.6     0.937  0.410      2  -103.  214.  219.   22854.
# # … with 14 more rows, and 2 more variables: df.residual <int>, nobs <int>

df.h %>% group_by(hour) %>%
  do(fitHour = glance(lm(price ~ wind + temp, data = .))) %>% 
  unnest(fitHour)
# # A tibble: 504 x 10
#    hour   price  wind   temp .fitted .resid .std.resid   .hat .sigma  .cooksd
#    <fct>  <dbl> <dbl>  <dbl>   <dbl>  <dbl>      <dbl>  <dbl>  <dbl>    <dbl>
#  1 1      94.2   883. -6.64     70.4  23.7       0.652 0.129    39.6 0.0209  
#  2 1      19.3  2107.  2.40     35.4 -16.0      -0.431 0.0864   39.9 0.00584 
#  3 1      60.5  2161. 18.3      18.1  42.5       1.18  0.146    38.5 0.0795  
#  4 1     116.   1244. 12.0      44.0  71.9       1.91  0.0690   35.8 0.0902  
#  5 1     117.   1624. -8.05     56.1  60.6       1.67  0.128    36.9 0.136   
#  6 1      75.0   220. -0.838    78.6  -3.58     -0.101 0.175    40.1 0.000724
#  7 1     106.    765.  6.15     60.0  45.7       1.22  0.0845   38.4 0.0461  
#  8 1      -9.89 2055. 12.3      26.5 -36.4      -0.979 0.0909   39.0 0.0319  
#  9 1      96.1   215. -8.36     86.3   9.82      0.287 0.232    40.0 0.00830 
# 10 1      27.2   323. 22.4      52.9 -25.7      -0.777 0.278    39.4 0.0774  
# # … with 494 more rows

归功于Bob Muenchen的Blog的启发。

Question 3

最简单的方法是使用，大约在2015年5月broom。broom包含三个函数，用于按组处理来自统计操作的复杂返回对象：（按组tidy处理来自统计操作的系数向量），glance（按组处理来自统计操作的汇总统计信息），以及augment（处理来自于统计操作的观察级结果）分组统计操作）。

这是一个演示方法，用于按组将线性回归的各种结果提取到整洁的data_frames中。

tidy：

library(dplyr)
library(broom)

df.h = data.frame( 
  hour     = factor(rep(1:24, each = 21)),
  price    = runif(504, min = -10, max = 125),
  wind     = runif(504, min = 0, max = 2500),
  temp     = runif(504, min = - 10, max = 25)  
)

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

# get the coefficients by group in a tidy data_frame
dfHourCoef = tidy(dfHour, fitHour)
dfHourCoef

这使，

    Source: local data frame [72 x 6]
    Groups: hour

hour        term     estimate   std.error  statistic     p.value
1     1 (Intercept) 53.336069324 21.33190104  2.5002961 0.022294293
2     1        wind -0.008475175  0.01338668 -0.6331053 0.534626575
3     1        temp  1.180019541  0.79178607  1.4903262 0.153453756
4     2 (Intercept) 77.737788772 23.52048754  3.3051096 0.003936651
5     2        wind -0.008437212  0.01432521 -0.5889765 0.563196358
6     2        temp -0.731265113  1.00109489 -0.7304653 0.474506855
7     3 (Intercept) 38.292039924 17.55361626  2.1814331 0.042655670
8     3        wind  0.005422492  0.01407478  0.3852630 0.704557388
9     3        temp  0.426765270  0.83672863  0.5100402 0.616220435
10    4 (Intercept) 30.603119492 21.05059583  1.4537888 0.163219027
..  ...         ...          ...         ...        ...         ...

augment：

 # get the predictions by group in a tidy data_frame
dfHourPred = augment(dfHour, fitHour)
dfHourPred

这使，

Source: local data frame [504 x 11]
Groups: hour

hour       price      wind      temp  .fitted  .se.fit     .resid       .hat   .sigma      .cooksd .std.resid
1     1  83.8414055   67.3780 -6.199231 45.44982 22.42649  38.391590 0.27955950 42.24400 0.1470891067  1.0663820
2     1   0.3061628 2073.7540 15.134085 53.61916 14.10041 -53.312993 0.11051343 41.43590 0.0735584714 -1.3327207
3     1  80.3790032  520.5949 24.711938 78.08451 20.03558   2.294497 0.22312869 43.64059 0.0003606305  0.0613746
4     1 121.9023855 1618.0864 12.382588 54.23420 10.31293  67.668187 0.05911743 40.23212 0.0566557575  1.6447224
5     1  -0.4039594 1542.8150 -5.544927 33.71732 14.53349 -34.121278 0.11740628 42.74697 0.0325125137 -0.8562896
6     1  29.8269832  396.6951  6.134694 57.21307 16.04995 -27.386085 0.14318542 43.05124 0.0271028701 -0.6975290
7     1  -7.1865483 2009.9552 -5.657871 29.62495 16.93769 -36.811497 0.15946292 42.54487 0.0566686969 -0.9466312
8     1  -7.8548693 2447.7092 22.043029 58.60251 19.94686 -66.457379 0.22115706 39.63999 0.2983443034 -1.7753911
9     1  94.8736726 1525.3144 24.484066 69.30044 15.93352  25.573234 0.14111563 43.12898 0.0231796755  0.6505701
10    1  54.4643001 2473.2234 -7.656520 23.34022 21.83043  31.124076 0.26489650 42.74790 0.0879837510  0.8558507
..  ...         ...       ...       ...      ...      ...        ...        ...      ...          ...        ...

glance：

# get the summary statistics by group in a tidy data_frame
dfHourSumm = glance(dfHour, fitHour)
dfHourSumm

这使，

Source: local data frame [24 x 12]
Groups: hour

hour  r.squared adj.r.squared    sigma statistic    p.value df    logLik      AIC      BIC deviance df.residual
1     1 0.12364561    0.02627290 42.41546 1.2698179 0.30487225  3 -106.8769 221.7538 225.9319 32383.29          18
2     2 0.03506944   -0.07214506 36.79189 0.3270961 0.72521125  3 -103.8900 215.7799 219.9580 24365.58          18
3     3 0.02805424   -0.07993974 39.33621 0.2597760 0.77406651  3 -105.2942 218.5884 222.7665 27852.07          18
4     4 0.17640603    0.08489559 41.37115 1.9277147 0.17434859  3 -106.3534 220.7068 224.8849 30808.30          18
5     5 0.12575453    0.02861615 42.27865 1.2945915 0.29833246  3 -106.8091 221.6181 225.7962 32174.72          18
6     6 0.08114417   -0.02095092 35.80062 0.7947901 0.46690268  3 -103.3164 214.6328 218.8109 23070.31          18
7     7 0.21339168    0.12599076 32.77309 2.4415266 0.11529934  3 -101.4609 210.9218 215.0999 19333.36          18
8     8 0.21655629    0.12950699 40.92788 2.4877430 0.11119114  3 -106.1272 220.2543 224.4324 30151.65          18
9     9 0.23388711    0.14876346 35.48431 2.7476160 0.09091487  3 -103.1300 214.2601 218.4381 22664.45          18
10   10 0.18326177    0.09251307 40.77241 2.0194425 0.16171339  3 -106.0472 220.0945 224.2726 29923.01          18
..  ...        ...           ...      ...       ...        ... ..       ...      ...      ...      ...         ...

Question 4

在dplyr 0.4中，您可以执行以下操作：

df.h %>% do(model = lm(price ~ wind + temp, data = .))

Question 5

从文档中do：

.f：适用于每件作品的功能。提供给.f的第一个未命名参数将是一个数据帧。

所以：

reg.models <- do(df.h, 
                 .f=function(data){
                     lm(price ~ wind + temp, data=data)
                 })

可能还有助于节省模型安装的时间：

reg.models <- do(df.h, 
                 .f=function(data){
                     m <- lm(price ~ wind + temp, data=data)
                     m$hour <- unique(data$hour)
                     m
                 })

Question 6

我认为您可以dplyr以更适当的方式使用，而无需像@fabians anwser中那样定义函数。

results<-df.h %.% 
group_by(hour) %.% 
do(failwith(NULL, lm), formula = price ~ wind + temp)

要么

results<-do(group_by(tbl_df(df.h), hour),
failwith(NULL, lm), formula = price ~ wind + temp)

编辑： 当然也可以不使用failwith

results<-df.h %.% 
    group_by(hour) %.% 
    do(lm, formula = price ~ wind + temp)


results<-do(group_by(tbl_df(df.h), hour),
lm, formula = price ~ wind + temp)

Question 7

我相信有一个比loki答案更紧凑的答案，后者放弃了自替换/取代以来的内容do()：

library(dplyr)
library(broom)
library(tidyr)

h.lm <- df.h %>%
      nest_by(hour) %>%
      mutate(fitHour = list(lm(price ~ wind + temp, data = data))) %>%
      summarise(tidy_out = list(tidy(fitHour)),
                glance_out = list(glance(fitHour)),
                augment_out = list(augment(fitHour))) %>%
      ungroup()

h.lm
# # A tibble: 24 x 4
#    hour  tidy_out         glance_out        augment_out
#    <fct> <list>           <list>            <list>
#  1 1     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  2 2     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  3 3     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  4 4     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  5 5     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  6 6     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  7 7     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  8 8     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
#  9 9     <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
# 10 10    <tibble [3 × 5]> <tibble [1 × 12]> <tibble [21 × 9]>
# # … with 14 more rows

类似于他们的答案，以便访问，只需嵌套任何所需的组件即可：

unnest(select(h.lm, hour, tidy_out))
# # A tibble: 72 x 6
#    hour  term        estimate std.error statistic p.value
#    <fct> <chr>          <dbl>     <dbl>     <dbl>   <dbl>
#  1 1     (Intercept) 63.2       20.9        3.02  0.00728
#  2 1     wind        -0.00237    0.0139    -0.171 0.866
#  3 1     temp        -0.266      0.950     -0.280 0.783
#  4 2     (Intercept) 65.1       23.0        2.83  0.0111
#  5 2     wind         0.00691    0.0129     0.535 0.599
#  6 2     temp        -0.448      0.877     -0.510 0.616
#  7 3     (Intercept) 65.2       17.8        3.67  0.00175
#  8 3     wind         0.00515    0.0112     0.458 0.652
#  9 3     temp        -1.87       0.695     -2.69  0.0148
# 10 4     (Intercept) 49.7       17.6        2.83  0.0111
# # … with 62 more rows