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至12)拟合线性模型。我正在使用扫帚包装为每个年级水平有效地创建一个模型。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,收到以下错误:
Error in lm.fit(x, y, offset = offset, singular.ok = singular.ok, ...) : 0 (non-NA) cases
我知道这是因为所有一年级的学生在survey_response列中都具有NA值。我不知道如何解决此问题,而无需对1年级运行单独的回归分析,而该回归分析会完全降低调查答复列/变量。如果该特定的年级子集仅包含空值,是否可以告诉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,我很乐意在评论中进行澄清。
我希望有人可以提供帮助!
您可以使用tryCatch
或类似方法捕获错误,并在发生错误时返回一些错误值(例如NA
)。在后续步骤中检查该错误值,并相应执行功能。
library(broom)
library(dplyr)
library(tidyr)
library(purrr)
df_train %>%
group_by(grade) %>%
nest() %>%
mutate(fit = map(data, ~tryCatch(lm(score ~ attendance_rate + test_growth +
binary_flag + survey_response, data = .x), error = function(e) NA)),
tidied = map(fit, ~if(any(is.na(.x))) NA else tidy(.x)),
augmented = map(fit, ~if(any(is.na(.x))) NA else augment(.x)),
glanced = map(fit, ~if(any(is.na(.x))) NA else glance(.x)))
我们可以使用possibly
中的purrr
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]>