Last data update: 2014.03.03

R: Finds matching using depth-first search recursively.
search_heuristic2R Documentation

Finds matching using depth-first search recursively.

Description

In each step, it removes one subject from the set of subjects with the smallest p-value recursively.

Usage

search_heuristic2(condition, covariates, halting_test, thresh, props,
  max_removed, tiebreaker = NULL, prefer_test = TRUE, print_info = FALSE,
  ...)

Arguments

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.

thresh

The return value of halting_test has to be greater than or equal to thresh for the matched groups.

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 enforced by the "heuristic1" method, preferred among configurations with the same number of total subjects by the "exhaustive" method, and 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.

max_removed

The maximum number of subjects that can be removed from each group. It must have a valid number for each group.

tiebreaker

NULL, or a function similar to halting_test, used to decide between cases for which halting_test yields equal values.

prefer_test

If TRUE, prefers higher test statistic more than the group size proportion; default is TRUE.

print_info

If TRUE, prints summary information on the input and the results, as well as progress information for the exhaustive search and random algorithms. Default: TRUE; can be changed using set_param("PRINT_INFO", FALSE).

...

Consumes extra parameters that are not used by the search algorithm at hand; this function gives a warning about the ones whose value is not NULL that their value is not used.

Value

All results found by search method in a list. It raises a "Convergence failure" error if it cannot find a matched set.

Results