我想使用dplyr为每小时（因子变量）拟合一个模型，我得到一个错误，并且我不太确定是什么错误。
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))
我尝试了各种各样的变化，但我无法让它发挥作用。
答案 0 :(得分：25)
在dplyr 0.4中，你可以这样做：
df.h %>% do(model = lm(price ~ wind + temp, data = .))
答案 1 :(得分：9)
来自do的文档：
.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框。
所以：
reg.models <- do(df.h,
.f=function(data){
lm(price ~ wind + temp, data=data)
})
可能还可以保存模型适合的小时：
reg.models <- do(df.h,
.f=function(data){
m <- lm(price ~ wind + temp, data=data)
m$hour <- unique(data$hour)
m
})
答案 2 :(得分：8)
我认为你可以以更合适的方式使用dplyr，而不需要在@fabians anwser中定义函数。
results<-df.h %.%
group_by(hour) %.%
do(failwith(NULL, lm), formula = price ~ wind + temp)
或
results<-do(group_by(tbl_df(df.h), hour),
failwith(NULL, lm), formula = price ~ wind + temp)
修改
当然，它也可以在没有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)
答案 3 :(得分：5)
从2020年中开始，tchakravarty's answer将失败。为了绕开broom和dpylr似乎相互影响的新方法，可以使用以下broom::tidy，broom::augment和broom::glance的用法。我们只需要在do()内使用它们，然后在unnest()内使用小工具即可。
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
以此为灵感向Bob Muenchen's Blog致谢。
答案 4 :(得分：4)
我相信有比 loki's answer 更简洁的答案，它放弃了因为已替换/superseded 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
类似于他们的回答，为了访问，只需取消嵌套所需的任何组件：
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
答案 5 :(得分：2)
从 dplyr 1.0.0 开始，group_split 为这个操作提供了一个方便的快捷方式：
library(dplyr)
library(broom)
library(purrr)
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.g <- group_split(df.h, hour)
map_dfr(df.g, function(x) tidy(lm(price ~ wind + temp, data=x)))
#> # A tibble: 72 x 5
#> term estimate std.error statistic p.value
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 (Intercept) -10.4 20.3 -0.512 0.615
#> 2 wind 0.0377 0.0117 3.23 0.00467
#> 3 temp 1.34 0.890 1.50 0.150
#> 4 (Intercept) 34.6 18.6 1.86 0.0799
#> 5 wind 0.0214 0.0125 1.71 0.104
#> 6 temp 0.332 0.865 0.384 0.706
#> 7 (Intercept) 42.5 15.3 2.79 0.0122
#> 8 wind 0.0103 0.0116 0.888 0.386
#> 9 temp -0.542 0.736 -0.736 0.471
#> 10 (Intercept) 64.1 18.8 3.41 0.00312
#> # … with 62 more rows
由 reprex package (v1.0.0) 于 2021 年 3 月 4 日创建