I have used the package MatchIt to conduct an exact matching for treatment (treat = 1) and control groups (treat = 0) -- the matching was made through age. The variable subclass reveals the matched units.
I would like to have one control unit selected randomly for each treated unit if it is matched to more than one control. It is important that it be random.
If I have more than one treatment unit matched to only 1 control (case of subclass 4), I would like to discard such control unit as to keep the same number of controls and units for each subclass.
In the end, I expect to have an equal number of observations for which treat = 1 and treat = 0.
My real dataset is huge and consists of more than a million subclasses.
structure(list(id = c("NSW1", "NSW57", "PSID6", "PSID84", "PSID147",
"PSID349", "PSID361", "PSID400", "NSW2", "NSW6", "NSW9", "NSW60",
"NSW77", "NSW80", "NSW127", "NSW161", "NSW169", "NSW177", "NSW179",
"PSID15", "PSID31", "PSID41", "PSID62", "PSID92", "PSID93", "PSID150",
"PSID167", "PSID178", "PSID254", "PSID292", "PSID300", "PSID308",
"PSID309", "PSID314", "PSID330", "NSW3", "NSW55", "NSW109", "PSID1",
"PSID69", "PSID91", "PSID165", "PSID166", "PSID302", "PSID378",
"ASID9033", "ASID9034", "ASID9036"), treat = c(1L, 1L, 0L, 0L,
0L, 0L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 1L, 0L,
0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L,
1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L), age = c(37L,
37L, 37L, 37L, 37L, 37L, 37L, 37L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L,
22L, 22L, 22L, 22L, 22L, 22L, 22L, 22L, 30L, 30L, 30L, 30L, 30L,
30L, 30L, 30L, 30L, 30L, 29L, 29L, 29L), race = c("black", "black",
"black", "hispan", "white", "white", "white", "black", "hispan",
"black", "black", "white", "black", "black", "black", "black",
"black", "hispan", "white", "black", "hispan", "black", "white",
"white", "white", "hispan", "white", "white", "white", "white",
"black", "black", "white", "white", "black", "black", "black",
"black", "white", "black", "white", "white", "white", "white",
"white", "black", "white", "black"), married = c(1L, 0L, 1L,
0L, 1L, 1L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 0L, 1L, 1L, 0L,
1L, 0L, 1L, 1L, 1L, 0L, 0L, 1L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L,
0L, 1L, 0L, 1L, 0L, 0L, 1L, 1L, 1L, 1L, 1L, 0L, 0L), subclass = c(1L,
1L, 1L, 1L, 1L, 1L, 1L, 1L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L, 2L,
2L, 2L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 3L, 4L, 4L, 4L)), class = "data.frame", row.names = c(NA,
-48L))
Here's a (maybe a bit convoluted) way using group_split and map_dfr.
library(tidyverse)
df %>%
group_split(subclass) %>%
map_dfr(~ if(sum(.x$treat) > (nrow(.x) / 2)) bind_rows(.x[.x$treat == 0, ], sample_n(.x[.x$treat == 1, ], nrow(.x[.x$treat == 0, ])))
else if(sum(.x$treat) < (nrow(.x) / 2)) bind_rows(.x[.x$treat == 1, ], sample_n(.x[.x$treat == 0, ], nrow(.x[.x$treat == 1, ])))
else .x)
# A tibble: 34 x 6
id treat age race married subclass
<chr> <int> <int> <chr> <int> <int>
1 NSW1 1 37 black 1 1
2 NSW57 1 37 black 0 1
3 PSID400 0 37 black 0 1
4 PSID84 0 37 hispan 0 1
5 NSW2 1 22 hispan 0 2
6 NSW6 1 22 black 0 2
7 NSW9 1 22 black 0 2
8 NSW60 1 22 white 0 2
9 NSW77 1 22 black 0 2
10 NSW80 1 22 black 0 2
# ... with 24 more rows