对每个组进行不同的预测 [英] Make prediction for each group differently

问题描述

我有一个看起来像这样的数据集:

I have dataset that looks like this:

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
... ...            ...         ...          ...

我分别对各组进行线性回归拟合:

I fitted linear regression for each group separately:

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

现在我为每个类别使用不同的模型:

Now I have different models for each category:

aa model1
bb model2
cc model3

因此，我需要将每个模型应用于适当的类别.如何实现呢?(最好是dplyr)

So, I need to apply each model to the appropriate category. How to achieve that? (dplyr is preferable)

推荐答案

如果您嵌套测试数据的数据，并将其与模型结合在一起，则可以使用map2对经过训练的模型进行测试数据的预测.参见下面的mtcars示例.

if you nest the data of your test data, join it with the models, then you can use map2 to make predictions on the test data with the trained models. See below example with mtcars.

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

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