Description Usage Arguments Details Value See Also
Creates a matched group via backward selection.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  match_groups(
condition,
covariates,
halting_test,
thresh = 0.2,
method = ldamatch::matching_methods,
props = prop.table(table(condition)),
replicates = get("RND_DEFAULT_REPLICATES", .ldamatch_globals),
min_preserved = length(levels(condition)),
print_info = get("PRINT_INFO", .ldamatch_globals),
max_removed_per_cond = NULL,
tiebreaker = NULL,
lookahead = 2,
all_results = FALSE,
prefer_test = TRUE,
max_removed_per_step = 1,
max_removed_percent_per_step = 0.5,
ratio_for_slowdown = 0.5
)

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:

thresh 
The return value of halting_test has to be greater than or equal to thresh for the matched groups. 
method 
The choice of search method, one of "random",
You can get more information about each method on the
help page for "search_<method_name>"
(e.g. " 
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. 
replicates 
The maximum number of random replications to be performed. This is only used for the "random" method. 
min_preserved 
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. 
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

max_removed_per_cond 
A named integer vector, containing the maximum number of subjects that can be removed from each group. Specify 0 for groups if you want to preserve all of their subjects. If you do not specify a value for a group, it defaults to 2 less than the group size. Values outside the valid range of 0..(N1) (where N is the number of subjects in the group) are corrected without a warning. 
tiebreaker 
NULL, or a function similar to halting_test, used to decide between cases for which halting_test yields equal values. 
lookahead 
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. 
all_results 
If TRUE, returns all results found by method in a list. (A list is returned even if there is only one result.) If FALSE (the default), it returns the first result (a logical vector). 
prefer_test 
If TRUE, prefers higher test statistic more than the expected group size proportion; default is TRUE. Used by all algorithms except exhaustive, which always 
max_removed_per_step 
The number of equivalent subjects that can be removed in each step. (The actual allowed number may be less depending on the pvalue / theshold ratio.) This parameters is used by the heuristic3 and heuristic4 algorithms, with a default value of 1. 
max_removed_percent_per_step 
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. 
ratio_for_slowdown 
The pvalue / 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 exhaustive, heuristic3, and heuristic4 search methods use the foreach
package to parallelize computation.
To take advantage of this, you must register a cluster.
For example, to use all but one of the CPU cores, run:
doParallel::registerDoParallel(cores = max(1, parallel::detectCores()  1))
To use sequential processing without getting a warning, run:
foreach::registerDoSEQ()
A logical vector that contains TRUE for the conditions that are in the matched groups; or if all_results = TRUE, a list of such vectors.
calc_p_value
for calculating the test statistic for
a group setup.
calc_metrics
for calculating multiple metrics about
the goodness of the result.
compare_ldamatch_outputs
for comparing multiple
different results from this function.
search_heuristic2, search_heuristic3, search_heuristic4, search_random, search_exhaustive
for
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