compare_ldamatch_outputs: Compares outputs of ldamatch runs.

Description

It favors, in decreasing order of priority, fewer excluded subjects, better balance (i.e. subsamples that diverge less from the expected proportions, which are by default the proportions of the input groups), and better (i.e. larger) test statistic for the matched groups. The preference order for the last two items can be reversed by specifying prefer_test = TRUE.

Usage

compare_ldamatch_outputs(
  is.in1,
  is.in2,
  condition,
  covariates = matrix(),
  halting_test = NA,
  props = prop.table(table(condition)),
  prefer_test = is.null(props),
  tiebreaker = NULL
)

Value

A number that is > 0 if is.in1 is a better solution than is.in2, < 0 if is.in1 is a worse solution than is.in2, or 0 if the two solutions are equivalent (not necessarily identical).

Arguments

is.in1: A logical vector for output 1, TRUE iff row is in the match.
is.in2: A logical vector for output 2, TRUE iff row is in the match.
condition: A factor vector containing condition labels.
covariates: A columnwise matrix containing covariates to match the conditions on.
halting_test: A function to apply to `covariates` (in matrix form) which is TRUE iff the conditions are matched. Signature: halting_test(condition, covariates, thresh). The following halting tests are part of this package: t_halt, U_halt, l_halt, ad_halt, ks_halt, wilks_halt, f_halt. You can create the intersection of two or more halting tests using create_halting_test.
props: Either the desired proportions (percentage) of the sample for each condition as a named vector, or the names of the conditions for which we prefer to preserve the subjects, in decreasing order of preference. If not specified, the (full) sample proportions are used. This is preferred among configurations with the same taken into account by the other methods to some extent. For example, c(A = 0.4, B = 0.4, C = 0.2) means that we would like the number of subjects in groups A, B, and C to be around 40%, 40%, and 20% of the total number of subjects, respectively. Whereas c("A", "B", "C") means that if possible, we would like to keep all subjects in group A, and prefer keeping subjects in B, even if it results in losing more subjects from C.
prefer_test: If TRUE, it prioritizes the test statistic more than the group size proportion.
tiebreaker: NULL, or a function similar to halting_test, used to decide between cases for which halting_test yields equal values.