Please use the heuristic3 search algorithm with lookahead=1 instead for nearly equivalent results. Note that heuristic3 is parallelized, more memory efficient, and chooses subject to remove randomly from among equivalent choices instead of choosing the first one deterministically. This function is implemented recursively, so may run out of memory when applied to many subjects.
search_heuristic2(
condition,
covariates,
halting_test,
thresh,
props,
max_removed_per_cond,
tiebreaker = NULL,
prefer_test = TRUE,
print_info = TRUE,
given_args = NULL,
...
)All results found by search method in a list. It raises a "Convergence failure" error if it cannot find a matched set.
A factor vector containing condition labels.
A columnwise matrix containing covariates to match the conditions on.
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.
The return value of halting_test has to be greater than or equal to thresh for the matched groups.
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.
The maximum number of subjects that can be removed from each group. It must have a valid number for each group.
NULL, or a function similar to halting_test, used to decide between cases for which halting_test yields equal values.
If TRUE, prefers higher test statistic more than the expected group size proportion; default is TRUE. Used by all algorithms except exhaustive, which always
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).
The names of arguments given to the search function.
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.
In each step, it removes one subject from the set of subjects with the smallest p-value recursively.