如何解决组内超采样
我想过采样,以便在数据集中每个组中的二进制因变量上保持平衡。
所以我的数据如下:
library(dplyr)
library(purrr)
library(tidyr)
seed(123)
# example data
(data <- tibble(
country = c("France","France","UK","UK"),YES = c(0,1,1),X = rnorm(9,1)
))
# A tibble: 9 x 3
country YES X
<chr> <dbl> <dbl>
1 France 0 -1.12
2 France 0 -0.200
3 France 1 0.781
4 UK 0 0.100
5 UK 0 0.0997
6 UK 0 -0.380
7 UK 0 -0.0160
8 UK 1 -0.0265
9 UK 1 0.860
我正在尝试通过过度采样在法国和英国实现YES平衡。在法国,我想有4个观测值,在英国,我想有8个观测值,这样一个随机样本可能看起来像这样):
# A tibble: 12 x 3
country YES X
<chr> <dbl> <dbl>
1 France 0 -1.12
2 France 0 -0.200
3 France 1 0.781
3 France 1 0.781
4 UK 0 0.100
5 UK 0 0.0997
6 UK 0 -0.380
7 UK 0 -0.0160
8 UK 1 -0.0265
9 UK 1 0.860
8 UK 1 -0.0265
8 UK 1 -0.0265
我的方法是这样的:
# oversample 1's within each country
(n_data <- data %>%
group_by(country) %>%
nest(.key = "original") %>%
mutate(os = map(original,~ group_by(.,YES))) %>%
mutate(os = map(os,~ slice_sample(.,replace = TRUE,prop = 1))))
# A tibble: 2 x 3
# Groups: country [2]
country original os
<chr> <list> <list>
1 France <tibble [3 x 2]> <tibble [3 x 2]>
2 UK <tibble [6 x 2]> <tibble [6 x 2]>
Warning message:
`.key` is deprecated
因此在OS中,尺寸应为4 x 2和8 x2。有人知道如何执行此操作吗?
解决方法
这似乎太复杂了,但是每个步骤似乎都清晰而稳健:
data %>%
count(country,YES) %>%
group_by(country) %>%
## Figure out how many additional rows are needed
mutate(
goal_rows = max(n),extra_rows = goal_rows - n
) %>%
select(country,YES,extra_rows) %>%
## Keep only the country/YES combinations that need extra rows
filter(extra_rows > 0) %>%
## Join back to original data
left_join(data,by = c("country","YES")) %>%
group_by(country) %>%
## Randomly keep the appropriate number of rows
mutate(rand = rank(runif(n()))) %>%
filter(rand <= extra_rows) %>%
select(-extra_rows,-rand) %>%
## Combine oversampled rows with original data
bind_rows(data) %>%
arrange(country,YES)
# # A tibble: 12 x 3
# # Groups: country [2]
# country YES X
# <chr> <dbl> <dbl>
# 1 France 0 1.88
# 2 France 0 -0.0793
# 3 France 1 0.812
# 4 France 1 0.812
# 5 UK 0 -1.66
# 6 UK 0 -0.797
# 7 UK 0 0.639
# 8 UK 0 -0.141
# 9 UK 1 -0.207
# 10 UK 1 1.30
# 11 UK 1 -0.207
# 12 UK 1 1.30
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