Description Usage Arguments Examples

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.

1 2 |

`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 |

mmatcher <- function( 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 = "_all_", # list of variables for to use in distance calculation id_var = NA, # ID variable in ds distance = "mahal", # "mahal", "euclid", "norm_euclid", "sad" caliper = 0.10, # proportionate width for calipers seed = 12345, # initial random seed value max_candidates = 1000, # maximum number of candidates within calipers per match n_per_match = 1, # number of controls per treatment loud = TRUE # print update bars )

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 | ```
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)
``` |

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