扫帚包 - lm.fit（x，y，offset = offset，singular.ok =singular.ok，...）中的错误：0（非 NA）案例

Question

我有一个学生属性和考试成绩的数据框，我正在尝试为每个年级（1 到 12）拟合一个线性模型。我正在使用 broom 包为每个年级有效地创建模型。下面是一个简化的示例数据集和我正在使用的代码。

#start df creation 

grade <- rep(1:12, each = 40)
attendance_rate <- round(runif(480, min=25, max=100), 1)
test_growth <- round(runif(480, min = -12, max = 38))
binary_flag <- round(runif(480, min = 0, max = 1))
score <- round(runif(480, min = 92, max = 370))
survey_response <- round(runif(480, min = 1, max = 4))

df <- data.frame(grade, attendance_rate, test_growth, binary_flag, score, survey_response) 

df$survey_response[df$grade == 1] <- NA

# end df creation

#create train test split for each grade level
set.seed(123)

df_train <- lapply(split(seq(1:nrow(df)), df$grade), function(x) sample(x, floor(.6*length(x))))
df_test <- mapply(function(x,y) setdiff(x,y), x = split(seq(1:nrow(df)), df$grade), y = df_train)

df_train <- df[unlist(df_train),]

df_test <- df[unlist(df_test),]



#create models
models_nested <- df_train %>%
  group_by(grade) %>% nest() %>% 
  mutate(
    fit = map(data, ~ lm(score ~ attendance_rate + test_growth + binary_flag + survey_response, data = .x)),
    tidied = map(fit, tidy),
    augmented = map(fit, augment),
    glanced = map(fit, glance)
  )

不幸的是，当我尝试运行以 models_nested 开头的代码块时，我收到以下错误：

Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 
  0 (non-NA) cases

我知道这种情况正在发生，因为所有 1 年级的学生在survey_response 列中都有一个 NA 值。如果不对一年级进行单独的回归，完全删除调查响应列/变量，我不知道如何解决这个问题。如果特定成绩子集仅包含空值，有没有办法告诉 lm 函数简单地忽略变量？我显然想在其他年级模型的回归中保留该变量。

我已尽力将这个问题弄清楚，但如有必要，我很乐意在 cmets 中澄清。

编辑 6/9/2020：我不想为一年级模型返回 NA，我只是希望一年级的线性模型在没有survey_response 列的情况下运行。我希望将survey_response 列包含在所有其他年级级别模型中。

希望有人能帮忙！

Answer 1

我们可以检查survey_response 中的NA 值并相应地使用模型。

library(broom)
library(dplyr)
library(tidyr)
library(purrr)

df_train %>%
   group_by(grade) %>% 
   nest() %>% 
    mutate(fit = map(data, ~ if(all(is.na(.x$survey_response)))
              lm(score ~ attendance_rate + test_growth + binary_flag, data = .x) 
              else lm(score ~ attendance_rate + test_growth + binary_flag + survey_response, data = .x)),
        tidied = map(fit, tidy),
        augmented = map(fit, augment),
        glanced = map(fit, glance))


#   grade data              fit    tidied           augmented          glanced          
#   <int> <list>            <list> <list>           <list>             <list>           
# 1     1 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 2     2 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 3     3 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 4     4 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 5     5 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 6     6 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 7     7 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 8     8 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
# 9     9 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
#10    10 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
#11    11 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>
#12    12 <tibble [24 × 5]> <lm>   <tibble [4 × 5]> <tibble [24 × 11]> <tibble [1 × 11]>

Answer 2

我们可以从purrr使用possibly

library(broom)
library(dplyr)
library(tidyr)
library(purrr)

poslm <- possibly(lm, otherwise = NA)
df_train %>%
   group_by(grade) %>% 
   nest() %>% 
   mutate(fit = map(data, ~ poslm(score ~ attendance_rate + test_growth + 
              binary_flag + survey_response, data = .x)), 
         tidied = map(fit, possibly(tidy, otherwise = NA)),
            augmented = map(fit, possibly(augment, otherwise = NA)),
          glanced = map(fit, possibly(glance, otherwise = NA)))
# A tibble: 12 x 6
# Groups:   grade [12]
#   grade data              fit       tidied           augmented          glanced          
#   <int> <list>            <list>    <list>           <list>             <list>           
# 1     1 <tibble [24 × 5]> <lgl [1]> <lgl [1]>        <lgl [1]>          <lgl [1]>        
# 2     2 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 3     3 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 4     4 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 5     5 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 6     6 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 7     7 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 8     8 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
# 9     9 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
#10    10 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
#11    11 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>
#12    12 <tibble [24 × 5]> <lm>      <tibble [5 × 5]> <tibble [24 × 12]> <tibble [1 × 11]>