1.2.3-SNAPSHOT

- (test-statistic-distribution test-statistic n k)

Create a distribution of the test-statistic over the possible

random samples of treatment units from the possible units.

There are two methods for generating the distribution. The

first method is enumerating all possible randomizations and

performing the test statistic on each. This gives the exact

distribution, but is only feasible for small problems.

The second method uses a combination-distribution to sample

for the space of possible treatment assignments and applies

the test statistic the sampled randomizations. While the

resulting distribution is not exact, it is tractable for

larger problems.

The algorithm automatically chooses between the two methods

by computing the number of possible randomizations and

comparing it to *test-statistic-iterations*. If the exact

distribution requires fewer than *test-statistic-iterations*

the enumeration method is used. Otherwise, it draws

*test-statistic-iterations* total samples for the simulated

method.

By default, the algorithm uses parallel computation. This is

controlled by the function *test-statistic-map*, which is

bound to pmap by default. Bind it to map to use a single

thread for computation.

Arguments:

test-statistic A function that takes two vectors and summarizes

the difference between them

n The number of total units in the pool

k The number of treatment units per sample

See also:

combination-distribution, pdf, cdf, draw, support

References:

http://en.wikipedia.org/wiki/Sampling_distribution

http://en.wikipedia.org/wiki/Exact_test

http://en.wikipedia.org/wiki/Randomization_test

http://en.wikipedia.org/wiki/Lady_tasting_tea

Examples:

© incanter
See http://incanter.org for copyright and license details.

(defn test-statistic-distribution " Create a distribution of the test-statistic over the possible random samples of treatment units from the possible units. There are two methods for generating the distribution. The first method is enumerating all possible randomizations and performing the test statistic on each. This gives the exact distribution, but is only feasible for small problems. The second method uses a combination-distribution to sample for the space of possible treatment assignments and applies the test statistic the sampled randomizations. While the resulting distribution is not exact, it is tractable for larger problems. The algorithm automatically chooses between the two methods by computing the number of possible randomizations and comparing it to *test-statistic-iterations*. If the exact distribution requires fewer than *test-statistic-iterations* the enumeration method is used. Otherwise, it draws *test-statistic-iterations* total samples for the simulated method. By default, the algorithm uses parallel computation. This is controlled by the function *test-statistic-map*, which is bound to pmap by default. Bind it to map to use a single thread for computation. Arguments: test-statistic A function that takes two vectors and summarizes the difference between them n The number of total units in the pool k The number of treatment units per sample See also: combination-distribution, pdf, cdf, draw, support References: http://en.wikipedia.org/wiki/Sampling_distribution http://en.wikipedia.org/wiki/Exact_test http://en.wikipedia.org/wiki/Randomization_test http://en.wikipedia.org/wiki/Lady_tasting_tea Examples: " [test-statistic n k] ; for now returns entire set of computed values, should summarize via frequencies (*test-statistic-map* test-statistic ; *t-s-m* is bound to pmap by default (let [cd (combination-distribution n k)] (if (> (nCk n k) *test-statistic-iterations*) ; simulated method (repeatedly *test-statistic-iterations* #(draw cd)) ; exact method (combinations (range 0 n) k)))))

© incanter
See http://incanter.org for copyright and license details.

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