按组添加模型预测 [英] Add predictions for models by group

问题描述

我正在按数据集中的组估计回归模型，然后希望为所有组添加正确的拟合值.

I'm estimating regressions models by groups in my dataset and then I wish to add the correct fitted values for all groups.

我正在尝试以下操作:

library(dplyr)
library(modelr)

df <- tribble(
  ~year, ~country, ~value,
  2001, "France", 55, 
  2002, "France", 53, 
  2003, "France", 31, 
  2004, "France", 10, 
  2005, "France", 30, 
  2006, "France", 37, 
  2007, "France", 54, 
  2008, "France", 58, 
  2009, "France", 50, 
  2010, "France", 40, 
  2011, "France", 49, 
  2001, "USA", 55,
  2002, "USA", 53,
  2003, "USA", 64,
  2004, "USA", 40,
  2005, "USA", 30,
  2006, "USA", 39,
  2007, "USA", 55,
  2008, "USA", 53,
  2009, "USA", 71,
  2010, "USA", 44,
  2011, "USA", 40
)

rmod <- df %>% 
  group_by(country) %>% 
  do(fitModels = lm("value ~ year", data = .))

df <- df %>% 
  add_predictions(rmod)

这会引发错误:

Error in UseMethod("predict") : 
  no applicable method for 'predict' applied to an object of class "c('rowwise_df', 'tbl_df', 'tbl', 'data.frame')"

我想用一栏列出该国家/地区的每个拟合值，或者用一栏列出每个国家/地区的预测值.当在 do()调用后将模型另存为列表时， add_predictions()函数似乎不起作用.

I would like to get either one column with each of the fitted values for the country or one column with predictions per country. Somehow the add_predictions() function doesn't seem to work when the models are saved as a list after a do() call.

推荐答案

还有其他几种方法可以用来攻击此问题.

There are a couple of additional ways you can attack this.

可能是最直接的，但是您丢失了中间模型:

Probably the most direct, but you lose the intermediate model:

rmod <- df %>%
  group_by(country) %>%
  mutate(fit = lm(value ~ year)$fitted.values) %>%
  ungroup
rmod
# # A tibble: 22 × 4
#     year country value      fit
#    <dbl>   <chr> <dbl>    <dbl>
# 1   2001  France    55 38.13636
# 2   2002  France    53 39.00000
# 3   2003  France    31 39.86364
# 4   2004  France    10 40.72727
# 5   2005  France    30 41.59091
# 6   2006  France    37 42.45455
# 7   2007  France    54 43.31818
# 8   2008  France    58 44.18182
# 9   2009  France    50 45.04545
# 10  2010  France    40 45.90909
# # ... with 12 more rows

另一种方法是使用整洁"模型将数据，模型和结果封装到框架内的单个单元格中.

Another way uses a "tidy" model for enclosing data, models, and results into individual cells within the frame:

rmod <- df %>%
  group_by(country) %>%
  nest() %>%
  mutate(mdl = map(data, ~ lm(value ~ year, data=.))) %>%
  mutate(fit = map(mdl, ~ .$fitted.values))
rmod
# # A tibble: 2 × 4
#   country              data      mdl        fit
#     <chr>            <list>   <list>     <list>
# 1  France <tibble [11 × 2]> <S3: lm> <dbl [11]>
# 2     USA <tibble [11 × 2]> <S3: lm> <dbl [11]>

此方法的优点是，您可以根据需要访问模型的其他属性，也许是 summary(filter(rmod，country =="France")$ mdl [[1]]).( [[1]] 是必需的，因为使用 tibble 时， $ mdl 将始终返回 list .)

The advantage to this method is that you can, as needed, access other properties of the model as-needed, perhaps summary( filter(rmod, country == "France")$mdl[[1]] ). (The [[1]] is required because with tibbles, $mdl will always return a list.)

您可以按以下步骤提取/取消嵌套:

And you can extract/unnest it as follows:

select(rmod, -mdl) %>% unnest()
# # A tibble: 22 × 4
#    country      fit  year value
#      <chr>    <dbl> <dbl> <dbl>
# 1   France 38.13636  2001    55
# 2   France 39.00000  2002    53
# 3   France 39.86364  2003    31
# 4   France 40.72727  2004    10
# 5   France 41.59091  2005    30
# 6   France 42.45455  2006    37
# 7   France 43.31818  2007    54
# 8   France 44.18182  2008    58
# 9   France 45.04545  2009    50
# 10  France 45.90909  2010    40
# # ... with 12 more rows

(不幸的是，这些列已重新排序，但这很美观，而且很容易修复.)

(The columns are re-ordered, unfortunately, but that's aesthetic and easily remedied.)

编辑

如果您想/需要在此处使用 modelr -specifics，请尝试:

If you want/need to use modelr-specifics here, try:

rmod <- df %>%
  group_by(country) %>%
  nest() %>%
  mutate(mdl = map(data, ~ lm(value ~ year, data=.))) %>%
  mutate(fit = map(mdl, ~ .$fitted.values)) %>%
  mutate(data = map2(data, mdl, add_predictions))
rmod
# # A tibble: 2 x 4
#   country data              mdl      fit       
#   <chr>   <list>            <list>   <list>    
# 1 France  <tibble [11 x 3]> <S3: lm> <dbl [11]>
# 2 USA     <tibble [11 x 3]> <S3: lm> <dbl [11]>
select(rmod, -mdl, -fit) %>% unnest()
# # A tibble: 22 x 4
#    country  year value  pred
#    <chr>   <dbl> <dbl> <dbl>
#  1 France  2001.   55.  38.1
#  2 France  2002.   53.  39.0
#  3 France  2003.   31.  39.9
#  4 France  2004.   10.  40.7
#  5 France  2005.   30.  41.6
#  6 France  2006.   37.  42.5
#  7 France  2007.   54.  43.3
#  8 France  2008.   58.  44.2
#  9 France  2009.   50.  45.0
# 10 France  2010.   40.  45.9
# # ... with 12 more rows

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