我正在使用dplyr和broom组合（请参见下文）并按照Fitting several regression models with dplyr来按组提取回归的回归系数。但是-我也对每个回归的R2值感兴趣（不仅对于整个模型）。我尝试使用增强和扫视功能-但无法提取R2值。是否有捷径可寻？
提前谢谢了！
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
hour term estimate std.error statistic p.value
1 1 (Intercept) 92.173945687 21.82132710 4.2240302 5.102470e-04
2 1 wind -0.020840948 0.01335945 -1.5600151 1.361653e-01
3 1 temp -0.162495052 0.90573269 -0.1794073 8.596220e-01
4 2 (Intercept) 53.569821889 20.90439474 2.5626105 1.957638e-02
5 2 wind 0.006492773 0.01273038 0.5100220 6.162329e-01
6 2 temp -0.493028932 0.78353239 -0.6292387 5.370978e-01
7 3 (Intercept) 93.949047453 14.55042590 6.4567902 4.483106e-06
8 3 wind -0.010084298 0.01179878 -0.8546902 4.039553e-01
9 3 temp -0.096177966 0.68416185 -0.1405778 8.897647e-01
10 4 (Intercept) 68.429142611 20.37382251 3.3586796 3.497149e-03
最佳答案
Broom::glance为我工作：
dfHourCoef = glance(dfHour, fitHour)
dfHourCoef
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 0.08223448 -0.01973947 34.02159 0.80642638 0.4619401 3 -102.2460 212.4921 216.6701 20834.44 18
2 2 0.07546305 -0.02726328 36.19379 0.73460277 0.4935356 3 -103.5458 215.0915 219.2696 23579.83 18
3 3 0.02395679 -0.08449245 37.17711 0.22090326 0.8039358 3 -104.1087 216.2174 220.3955 24878.47 18
4 4 0.04916169 -0.05648701 40.38246 0.46533173 0.6352725 3 -105.8454 219.6909 223.8690 29353.38 18
5 5 0.16704225 0.07449138 34.47921 1.80486969 0.1930220 3 -102.5266 213.0532 217.2313 21398.69 18
6 6 0.13615197 0.04016886 41.64294 1.41849921 0.2678774 3 -106.4909 220.9818 225.1599 31214.42 18
7 7 0.01979010 -0.08912211 39.22426 0.18170693 0.8353563 3 -105.2343 218.4687 222.6467 27693.76 18
8 8 0.00171480 -0.10920578 31.29634 0.01545971 0.9846722 3 -100.4927 208.9853 213.1634 17630.30 18
9 9 0.19935534 0.11039483 36.02611 2.24094183 0.1351943 3 -103.4482 214.8965 219.0746 23361.85 18
10 10 0.16950148 0.07722387 36.99197 1.83686463 0.1879532 3 -104.0038 216.0077 220.1858 24631.31 18
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