用dplyr拟合多个回归模型
我想用dplyr为每小时（因子变量）拟合一个模型，我得到了一个错误，我不太确定出了什么问题用dplyr拟合多个回归模型,r,dplyr,R,Dplyr,我想用dplyr为每小时（因子变量）拟合一个模型，我得到了一个错误，我不太确定出了什么问题 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 <- 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))
dolibrary(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
.freg.models我认为在不需要像@fabians anwser那样定义函数的地方，可以更恰当地使用dplyr
results<-df.h %.%
group_by(hour) %.%
do(failwith(NULL, lm), formula = price ~ wind + temp)
在dplyr 0.4中，您可以执行以下操作：
df.h %>% do(model = lm(price ~ wind + temp, data = .))
最简单的方法是在2015年5月左右使用broom
包含三个函数，用于按组处理统计操作中返回的复杂对象：tidy
（按组处理统计操作中的系数向量）、glance
（按组处理统计操作中的汇总统计信息）和augment
（按组处理统计操作的观察级结果）
这里演示了如何使用它将分组线性回归的各种结果提取到整洁的数据框架中
整洁
：
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
这就给了,
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
.. ... ... ... ... ... ...
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
.. ... ... ... ... ... ... ... ... ... ... ...
增强
：
# get the predictions by group in a tidy data_frame
dfHourPred = augment(dfHour, fitHour)
dfHourPred
这就给了,
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
.. ... ... ... ... ... ...
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
.. ... ... ... ... ... ... ... ... ... ... ...
一瞥
：
# get the summary statistics by group in a tidy data_frame
dfHourSumm = glance(dfHour, fitHour)
dfHourSumm
这就给了,
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
.. ... ... ... ... ... ... .. ... ... ... ... ...
到2020年中期，将失败。为了规避扫帚
和dpylr
的新方法，下面使用扫帚：：整洁
，扫帚：：增强
和扫帚：：扫视
似乎是相互作用的。我们只需在do（）
和以后的unnest（）中使用它们
tibble
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
库（dplyr）
图书馆（扫帚）
df.h=数据帧（
小时=系数（代表（1:24，各=21）），
价格=runif（504，最小=10，最大=125），
风=runif（504，最小=0，最大=2500），
温度=runif（504，最小=10，最大=25）
)
df.h%>%按（小时）分组%>%
do（fitHour=tidy（lm（价格~风+温度，数据=））%>%
最晚（星期五）
##A tibble:72 x 6
#小时项估计标准误差统计p值
#
#1 1（截距）82.4 18.1 4.55 0.000248
#2 1风-0.0212 0.0108-1.96 0.0655
#3 1温度-1.01 0.792-1.28 0.218
#4.2（截距）25.9 19.7 1.31 0.206
#5.2风0.0204 0.0131 1.57 0.135
#6.2温度0.680 1.01 0.670 0.511
#7.3（截距）88.3 15.5 5.69 0.0000214
#8.3风-0.0188 0.00998-1.89 0.0754
#9.3温度-0.669 0.653-1.02 0.319
#104（截距）73.414.25.17 0.0000639
df.h%>%按（小时）分组%>%
do（fitHour=增加（lm（价格~风+温度，数据=））%>%
最晚（星期五）
##tibble:24 x 13
#小时r平方调整r平方sigma统计p值df logLik AIC BIC偏差
#
# 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.
##…还有14行，还有2个变量：df.residence，nobs
df.h%>%按（小时）分组%>%
do（fitHour=glance（lm（价格~风+温度，数据=））%>%
最晚（星期五）
##A tibble:504 x 10
#小时价格风温安装剩余标准剩余hat西格玛cooksd
#
# 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
##…还有494行
这方面的灵感来源于。我相信有一个比这个更简洁的答案，它放弃了自替换以来的/do（）
：
库（dplyr）
图书馆（扫帚）
图书馆（tidyr）
h、 lm%
嵌套时间（小时）%>%
变异（fitHour=列表（lm（价格~风+温度，数据=数据）））%>%
总结（整理=列表（整理（休息时间）），
扫视=列表（扫视（fitHour）），
增大输出=列表（增大（fitHour））%>%
解组（）
h、 lm
##tibble:24 x 4
#小时整理(扫视)(扩大)(扩大)
#
# 1 1
# 2 2
# 3 3
# 4 4
# 5 5
# 6 6
# 7 7
# 8 8
# 9 9
# 10 10
##…还有14行
模拟
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
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
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