以不同方式对每个组进行预测

Question

我的数据集看起来像这样:

Category Weekly_Date             a             b
   <chr>    <date>           <dbl>         <dbl>
 1   aa     2018-07-01        36.6          1.4
 2   aa     2018-07-02        5.30          0   
 3   bb     2018-07-01        4.62          1.2
 4   bb     2018-07-02        3.71          1.5
 5   cc     2018-07-01        3.41          12
... ...            ...         ...          ...

我分别为每个组拟合线性回归:

fit_linreg <- train %>%
              group_by(Category) %>%
              do(model = lm(Target ~ Unit_price + Unit_discount, data = .)) 

现在我对每个类别都有不同的模型:

aa model1
bb model2
cc model3

所以,我需要将每个模型应用到适当的类别.怎么实现呢？(dplyr更好)

Answer 1

如果嵌套测试数据的数据,将其与模型连接,则可以使用map2使用经过训练的模型对测试数据进行预测.请参阅下面的mtcars示例.

library(tidyverse)

x <- mtcars %>% 
  group_by(gear) %>% 
  do(model = lm(mpg ~ hp + wt, data = .)) 

x
Source: local data frame [3 x 2]
Groups: <by row>

# A tibble: 3 x 2
   gear model   
* <dbl> <list>  
1     3 <S3: lm>
2     4 <S3: lm>
3     5 <S3: lm>

mtcars %>% 
  group_by(gear) %>% 
  nest %>% 
  inner_join(x) %>% 
  mutate(preds = map2(model, data, predict)) %>% 
  unnest(preds)

  Joining, by = "gear"
# A tibble: 32 x 2
    gear preds
   <dbl> <dbl>
 1     4  22.0
 2     4  21.2
 3     4  25.1
 4     4  26.0
 5     4  22.2
 6     4  17.8
 7     4  17.8
 8     4  28.7
 9     4  32.3
10     4  30.0
# ... with 22 more rows