Fitting several regression models with dplyr
Solution 1:
The easiest way to do this, circa May 2015 is to use broom. broom contains three functions that deal with complex returned objects from statistical operations by groups: tidy (which deals with coefficient vectors from statistical operations by groups), glance (which deals with summary statistics from statistical operations by groups), and augment (which deals with observation level results from statistical operations by groups).
Here is a demonstration of its use to extract the various results of linear regression by groups into tidy data_frames.
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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) dfHourCoefwhich gives,
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) dfHourPredwhich gives,
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) dfHourSummwhich gives,
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 .. ... ... ... ... ... ... .. ... ... ... ... ...
Solution 2:
In dplyr 0.4, you can do:
df.h %>% do(model = lm(price ~ wind + temp, data = .))
Solution 3:
As of mid 2020, tchakravarty's answer will fail. In order to circumvent the new approach of broom and dpylr seem to interact, the following combination of broom::tidy, broom::augment and broom::glance can be used. We just have to use them inside do() and later unnest() the 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
Credits to Bob Muenchen's Blog for inspiration on that.