我有一个包含几个模型的数据框，我想将适当的模型应用于不同数据框的每一行，然后将模型的预测值添加到该数据框的新列中
我有一个不太好的解决方案，使用for循环，要求我对应用模型的数据框进行排序。
# sort my sample data (mtcars) by cylinder, so the final data lines up
mycars <- mtcars[order(mtcars$cyl),]
# build a linear model for each number of cylinders,
# estimating mpg from displacement
by_cyl <- group_by(mycars, cyl)
models <- by_cyl %>% do(mod = lm(mpg ~ disp, data = .))
# my inelegant solution for adding the predicted mpg into the dataset
prediction <- c()
for (i in models$cyl){
temp <- filter(mycars, cyl == i)
prediction <- c(prediction, predict((models %>% filter(cyl==i))$mod[[1]], temp))
}
mycars$mpg.pred <- prediction
我希望能够避免使用for循环，并且尽可能将原始日期保留为原始顺序
答案 0 :(得分：3)
使用tidyverse，其中.fitted是预测值：
library(tidyverse)
mtcars %>%
nest(-cyl) %>%
mutate(mod = map(data, ~lm(mpg ~ disp, data = .))) %>%
mutate(pred = map(mod, broom::augment)) %>%
select(pred) %>%
unnest()
#> # A tibble: 32 x 8
#> mpg disp .fitted .resid .std.resid .hat .sigma .cooksd
#> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 21 160 19.7 -1.34 0.944 0.195 1.61 0.108
#> 2 21 160 19.7 -1.34 0.944 0.195 1.61 0.108
#> 3 21.4 258 20.0 -1.39 1.55 0.681 1.28 2.57
#> 4 18.1 225 19.9 1.79 -1.36 0.311 1.40 0.419
#> 5 19.2 168. 19.7 0.486 -0.336 0.167 1.75 0.0113
#> 6 17.8 168. 19.7 1.89 -1.30 0.167 1.44 0.170
#> 7 19.7 145 19.6 -0.0953 0.0711 0.284 1.77 0.00101
#> 8 22.8 108 26.3 3.48 -1.29 0.0920 2.70 0.0849
#> 9 24.4 147. 21.0 -3.35 1.45 0.330 2.62 0.521
#> 10 22.8 141. 21.8 -0.956 0.396 0.267 2.96 0.0286
#> # ... with 22 more rows
由reprex package（v0.3.0）于2019-06-18创建
答案 1 :(得分：0)
这是使用dplyr，tidyr::nest/unnest和broom的方法。想法是将每个分组变量（cyl）值嵌套在一行中，使线性模型适合该行的数据，然后取消嵌套。 bind_cols部分将原始数据附加到拟合的数据上。
library(tidyverse); library(broom)
bind_cols(
mycars,
mycars %>%
nest(-cyl) %>%
mutate(
fit = map(data, ~ lm(mpg ~ disp, data = .x)),
predictions = map(fit, augment)
) %>%
unnest(predictions)
)
mpg cyl disp hp drat wt qsec vs am gear carb cyl1 mpg1 disp1 .fitted .se.fit
1 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 4 22.8 108.0 26.27664 0.8551838
2 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 4 24.4 146.7 21.04665 1.6196357
3 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 4 22.8 140.8 21.84399 1.4566581
4 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 4 32.4 78.7 30.23629 1.2212018
5 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 4 30.4 75.7 30.64172 1.2945167
6 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 4 33.9 71.1 31.26337 1.4131700
7 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 4 21.5 120.1 24.64142 0.9842241
8 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 4 27.3 79.0 30.19575 1.2140755
9 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 4 26.0 120.3 24.61440 0.9875865
10 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 4 30.4 95.1 28.01997 0.9127723
...
答案 2 :(得分：0)
还应考虑R的by + do.call：
df_list <- by(mycars, mycars$cyl, function(sub)
within(sub, pred - predict(lm(mpg ~ disp, data = sub)))
)
final_df <- do.call(rbind, unname(df_list))
输出
final_df
# mpg cyl disp hp drat wt qsec vs am gear carb pred
# Datsun 710 22.8 4 108.0 93 3.85 2.320 18.61 1 1 4 1 26.27664
# Merc 240D 24.4 4 146.7 62 3.69 3.190 20.00 1 0 4 2 21.04665
# Merc 230 22.8 4 140.8 95 3.92 3.150 22.90 1 0 4 2 21.84399
# Fiat 128 32.4 4 78.7 66 4.08 2.200 19.47 1 1 4 1 30.23629
# Honda Civic 30.4 4 75.7 52 4.93 1.615 18.52 1 1 4 2 30.64172
# Toyota Corolla 33.9 4 71.1 65 4.22 1.835 19.90 1 1 4 1 31.26337
# Toyota Corona 21.5 4 120.1 97 3.70 2.465 20.01 1 0 3 1 24.64142
# Fiat X1-9 27.3 4 79.0 66 4.08 1.935 18.90 1 1 4 1 30.19575
# Porsche 914-2 26.0 4 120.3 91 4.43 2.140 16.70 0 1 5 2 24.61440
# Lotus Europa 30.4 4 95.1 113 3.77 1.513 16.90 1 1 5 2 28.01997
# Volvo 142E 21.4 4 121.0 109 4.11 2.780 18.60 1 1 4 2 24.51980
# Mazda RX4 21.0 6 160.0 110 3.90 2.620 16.46 0 1 4 4 19.65881
# Mazda RX4 Wag 21.0 6 160.0 110 3.90 2.875 17.02 0 1 4 4 19.65881
# Hornet 4 Drive 21.4 6 258.0 110 3.08 3.215 19.44 1 0 3 1 20.01211
# Valiant 18.1 6 225.0 105 2.76 3.460 20.22 1 0 3 1 19.89314
# Merc 280 19.2 6 167.6 123 3.92 3.440 18.30 1 0 4 4 19.68621
# Merc 280C 17.8 6 167.6 123 3.92 3.440 18.90 1 0 4 4 19.68621
# Ferrari Dino 19.7 6 145.0 175 3.62 2.770 15.50 0 1 5 6 19.60473
# Hornet Sportabout 18.7 8 360.0 175 3.15 3.440 17.02 0 0 3 2 14.96452
# Duster 360 14.3 8 360.0 245 3.21 3.570 15.84 0 0 3 4 14.96452
# Merc 450SE 16.4 8 275.8 180 3.07 4.070 17.40 0 0 3 3 16.61772
# Merc 450SL 17.3 8 275.8 180 3.07 3.730 17.60 0 0 3 3 16.61772
# Merc 450SLC 15.2 8 275.8 180 3.07 3.780 18.00 0 0 3 3 16.61772
# Cadillac Fleetwood 10.4 8 472.0 205 2.93 5.250 17.98 0 0 3 4 12.76551
# Lincoln Continental 10.4 8 460.0 215 3.00 5.424 17.82 0 0 3 4 13.00112
# Chrysler Imperial 14.7 8 440.0 230 3.23 5.345 17.42 0 0 3 4 13.39380
# Dodge Challenger 15.5 8 318.0 150 2.76 3.520 16.87 0 0 3 2 15.78916
# AMC Javelin 15.2 8 304.0 150 3.15 3.435 17.30 0 0 3 2 16.06403
# Camaro Z28 13.3 8 350.0 245 3.73 3.840 15.41 0 0 3 4 15.16087
# Pontiac Firebird 19.2 8 400.0 175 3.08 3.845 17.05 0 0 3 2 14.17916
# Ford Pantera L 15.8 8 351.0 264 4.22 3.170 14.50 0 1 5 4 15.14123
# Maserati Bora 15.0 8 301.0 335 3.54 3.570 14.60 0 1 5 8 16.12294