mmatcher: Multivariate Matching

In mmsample: Multivariate Matched Sampling

Description

Takes a data.frame (ds) and using the variables specified in x_vars, selects matches from the control group (group_var == 0) for members of the treatment group (group_var == 1) where possible. It returns a data.frame containing only rows which are part of a match.

The caliper width for propensity scores filters candidates prior to calculating distances, these can be widened to allow more but poorer matches. The distance measure can be one of "mahal" (default), "euclid", "norm_euclid" or "sad".

max_candidates allows the user to limit the number of matches within the calipers, effectively narrowing the calipers temporarily for treatment cases that have a large number of candidate matches.

The default seed argument ensures that given the exact same dataset, the function will return the same matches, this is because the algorithm is greedy and matches are assigned in random order.

n_per_match can be used to assign more than one control case to each treatment case and may be useful when the treatment group is small but the control group is large.

If loud is TRUE, progress updates and some summary information are printed to the console, otherwise the function prints nothing.

Usage

mmatcher(ds, group_var, x_vars = "_all_", id_var = NA, distance = "mahal",
  caliper = 0.10, seed = 12345, max_candidates = 1000, n_per_match = 1, loud = TRUE)

Arguments

ds	data.frame containing at least a group (0/1) variable and others to calculate distance
group_var	variable with 0=control and 1=treatment in ds
x_vars	list of variables to use in distance calculation
id_var	name of ID variable in ds (if present)
distance	one of "mahal", "euclid", "norm_euclid" or "sad"
caliper	proportionate width for propensity score calipers
seed	initial random seed value
max_candidates	maximum number of candidates within calipers per match
n_per_match	number of control cases to match to each treatment case
loud	print update bars and stats

Examples

treat_n <- 100
control_n <- 300
n <- treat_n + control_n
set.seed(123)

df <- data.frame(age = round(c(rnorm(control_n, 40, 15), rnorm(treat_n, 60, 15)), 2),
                 male = c(rbinom(control_n, 1, 0.4), rbinom(treat_n, 1, 0.6)),
                 grp = c(rep(0, control_n), rep(1, treat_n)))
df$age[df$age < 20 | df$age > 95] <- NA

matched_df <- mmsample::mmatcher(df, "grp", c("age", "male"))

tapply(df$age, df$grp, quantile, na.rm = TRUE)
tapply(matched_df$age, matched_df$grp, quantile, na.rm = TRUE)

table(df$male, df$grp)
table(matched_df$male, matched_df$grp)

Example output

|5|10|15|20|25|30|35|40|45|50|55|60|65|70|75|80|85|90|95|100
Matched: 78/98	Mean (95% CI) distance: 0.228 (0.001, 2.216)
$`0`
   0%   25%   50%   75%  100% 
20.24 32.65 40.80 50.33 88.62 

$`1`
     0%     25%     50%     75%    100% 
23.0100 48.8275 59.6450 69.8750 90.5600 

$`0`
     0%     25%     50%     75%    100% 
23.5600 45.4950 53.8050 64.3025 88.6200 

$`1`
     0%     25%     50%     75%    100% 
23.0100 45.8450 54.7000 63.0975 80.0300 

   
      0   1
  0 188  37
  1 112  63
   
     0  1
  0 41 36
  1 37 42

mmsample documentation built on May 1, 2019, 8:27 p.m.