In each step, it removes one subject from the set of subjects that were removed on most paths after "lookahead" steps, preferring one with the smallest associate p-value.
search_heuristic4(
condition,
covariates,
halting_test,
thresh,
props,
max_removed_per_cond,
tiebreaker = NULL,
min_preserved = length(levels(condition)),
lookahead = 2,
prefer_test = TRUE,
print_info = TRUE,
max_removed_per_step = 1,
max_removed_percent_per_step = 0.5,
ratio_for_slowdown = 0.5,
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.
The minimum number of preserved subjects. It can be used to ensure that the search will not take forever to run, but instead fail when a solution is not found when preserving this number of subjects.
The lookahead to use: a positive integer. It is used by the heuristic3 and heuristic4 algorithms, with a default of 2. The running time is O(N ^ lookahead), wheren N is the number of subjects.
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 number of equivalent subjects that can be removed in each step. (The actual allowed number may be less depending on the p-value / theshold ratio.) This parameters is used by the heuristic3 and heuristic4 algorithms, with a default value of 1.
The percentage of remaining subjects that can be removed in each step. Used when max_removed_per_step > 1, with a default value of 0.5.
The p-value / threshold ratio at which it starts removing subjects one by one. Used when max_removed_per_step > 1, with a default value of 0.5.
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.
Note that this algorithm is not deterministic, as it chooses one possible subject for removal randomly when there are multiple apparently equivalent ones. In practice it means that it may return different results on different runs (including the case that it fails to converge to a solution in one run, but converges in another run). If print_info = TRUE (the default), you will see a message about "Random choices" if the algorithm needed to make such random decisions.