以编程方式为变量的每个可能值创建一个虚拟对象，并将这些虚拟对象传递给公式

Question

我发现 NHL shift data 并想评估一个模型，在该模型中，进球数遵循泊松分布，具体取决于两队谁在场上。

我的观点是，我们已经很清楚谁能得分（进球和助攻），但也许有人真的很擅长帮助他的球队得分而无需得分 sheet（也许是产生失误？）或者只是非常擅长阻止对方得分。

我可以创建如下所示 "data" 的数据集。每支球队通常有 5 名球员在冰上，但我只放了 2 名球员以使示例易于理解。

基本上，每个轮班我都有一行，我知道轮班的结果 (goal_for)，shift_duration 并且我有一个为球队效力的球员的 ID 列表(for_players) 和对方 (against_players)。

我想做的 是获取 "data" 数据集并创建 "model_data"，其中包含一个虚拟变量，指示一名球员是否在冰上对于给定的班次。然后我会为我的泊松模型创建一个公式，其中将包含所有的假人并将其传递给模型。我也可以放弃一个虚拟人，一个虚拟人反对，但我也可以让 mgcv:gam 为我做。

我怀疑这会涉及一些！！和 quos()，但我不知道该怎么做。

data <- tibble(
  shift_id = c(1, 2, 3, 4, 5, 6, 7, 8,9,10),
  shift_duration = c(12, 7, 30, 11, 14, 16, 19, 32,11,12),
  goal_for = c(1, 1, 0, 0, 1, 1, 0, 0,0,0),
  for_players = list(
    c("A", "B"),
    c("A", "C"),
    c("B", "C"),
    c("A", "C"),
    c("B", "C"),
    c("A", "B"),
    c("B", "C"),
    c("A", "B"),
    c("B", "C"),
    c("A", "B")
  ),
  against_players = list(
    c("X", "Z"),
    c("Y", "Z"),
    c("X", "Y"),
    c("X", "Y"),
    c("X", "Z"),
    c("Y", "Z"),
    c("X", "Y"),
    c("Y", "Z"),
    c("X", "Y"),
    c("Y", "Z")
  )
)


(black magic goes here)

model_data <- tibble(
  shift_id = c(1, 2, 3, 4, 5, 6, 7, 8, 9, 10),
  shift_duration = c(12, 7, 30, 11, 14, 16, 19, 32, 11, 12),
  goal_for = c(1, 1, 0, 0, 1, 1, 0, 0, 0, 0),
  for_player_A = c(1, 1, 0, 1, 0, 1, 0, 1, 0, 1),
  for_player_B = c(1, 0, 1, 0, 1, 1, 1, 1, 1, 1),
  for_player_C = c(0, 1, 1, 1, 1, 0, 1, 0, 1, 0),
  against_player_X = c(1, 0, 1, 1, 1, 0, 1, 0, 1, 0),
  against_player_Y = c(0, 1, 1, 1, 0, 1, 1, 1, 1, 1),
  against_player_Z = c(1, 1, 0, 0, 1, 1, 0, 1, 0, 1)
)



mod.gam <- mgcv::gam(
  data = model_data,
  formula =  goal_for ~ offset(log(shift_duration)) + for_player_A + for_player_B  + for_player_C +
    against_player_X + against_player_Y + against_player_Z,
  family = poisson(link = log)
)

数据看起来像这样：

> data
# A tibble: 10 x 5
   shift_id shift_duration goal_for for_players against_players
      <dbl>          <dbl>    <dbl> <list>      <list>         
 1     1.00          12.0      1.00 <chr [2]>   <chr [2]>      
 2     2.00           7.00     1.00 <chr [2]>   <chr [2]>      
 3     3.00          30.0      0    <chr [2]>   <chr [2]>      
 4     4.00          11.0      0    <chr [2]>   <chr [2]>      
 5     5.00          14.0      1.00 <chr [2]>   <chr [2]>      
 6     6.00          16.0      1.00 <chr [2]>   <chr [2]>      
 7     7.00          19.0      0    <chr [2]>   <chr [2]>      
 8     8.00          32.0      0    <chr [2]>   <chr [2]>      
 9     9.00          11.0      0    <chr [2]>   <chr [2]>      
10    10.0           12.0      0    <chr [2]>   <chr [2]>

模型数据 看起来像这样：

> model_data
# A tibble: 10 x 9
   shift_id shift_duration goal_for for_player_A for_player_B for_player_C against_player_X against_player_Y against_player_Z
      <dbl>          <dbl>    <dbl>        <dbl>        <dbl>        <dbl>            <dbl>            <dbl>            <dbl>
 1     1.00          12.0      1.00         1.00         1.00         0                1.00             0                1.00
 2     2.00           7.00     1.00         1.00         0            1.00             0                1.00             1.00
 3     3.00          30.0      0            0            1.00         1.00             1.00             1.00             0   
 4     4.00          11.0      0            1.00         0            1.00             1.00             1.00             0   
 5     5.00          14.0      1.00         0            1.00         1.00             1.00             0                1.00
 6     6.00          16.0      1.00         1.00         1.00         0                0                1.00             1.00
 7     7.00          19.0      0            0            1.00         1.00             1.00             1.00             0   
 8     8.00          32.0      0            1.00         1.00         0                0                1.00             1.00
 9     9.00          11.0      0            0            1.00         1.00             1.00             1.00             0   
10    10.0           12.0      0            1.00         1.00         0                0                1.00             1.00

模型的结果：

Family: poisson 
Link function: log 

Formula:
goal_for ~ offset(log(shift_duration)) + for_player_A + for_player_B + 
    for_player_C + against_player_X + against_player_Y + against_player_Z

Parametric coefficients:
                  Estimate Std. Error z value Pr(>|z|)
(Intercept)       -22.0296  4317.9341  -0.005    0.996
for_player_A        0.0000     0.0000      NA       NA
for_player_B       -2.3026     2.0000  -1.151    0.250
for_player_C       -0.1542     1.4142  -0.109    0.913
against_player_X    1.6094     1.4142   1.138    0.255
against_player_Y    0.0000     0.0000      NA       NA
against_player_Z   20.2378  4317.9339   0.005    0.996


Rank: 5/7
R-sq.(adj) =  0.353   Deviance explained = 73.6%
UBRE = 0.26435  Scale est. = 1         n = 10

Answer 1

您可以使用 tidyr...

中的函数将 data 数据框转换为 model_data 数据框

library(dplyr)
library(tidyr)

model_data <-
  data %>% 
  unnest(for_players, .drop = F) %>% 
  spread(for_players, for_players, sep = '_') %>% 
  unnest(against_players, .drop = F) %>% 
  spread(against_players, against_players, sep = '_') %>% 
  mutate_at(vars(-(1:3)), funs(as.numeric(!is.na(.))))

以编程方式为变量的每个可能值创建一个虚拟对象，并将这些虚拟对象传递给公式

programmatically create a dummy for each possible value of a variable, and pass these dummies to a formula

r

dplyr

tidyr

gam