confint_2group_diff

A range of functions to compute bootstraps for the mean difference

calculate_weighted_delta

 calculate_weighted_delta (group_var, differences)

Compute the weighted deltas.

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calculate_group_var

 calculate_group_var (control_var, control_N, test_var, test_N)

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compute_interval_limits

 compute_interval_limits (bias, acceleration, n_boots, ci=95)

Returns the indexes of the interval limits for a given bootstrap.

Supply the bias, acceleration factor, and number of bootstraps.

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compute_meandiff_bias_correction

 compute_meandiff_bias_correction (bootstraps, effsize)

Computes the bias correction required for the BCa method of confidence interval construction.

	Type	Details
bootstraps		An numerical iterable, comprising bootstrap resamples of the effect size.
effsize		The effect size for the original sample.
Returns	bias: numeric	The bias correction value for the given bootstraps and effect size.

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compute_delta2_bootstrapped_diff

 compute_delta2_bootstrapped_diff (x1:numpy.ndarray, x2:numpy.ndarray,
                                   x3:numpy.ndarray, x4:numpy.ndarray,
                                   is_paired:str=None, resamples:int=5000,
                                   random_seed:int=12345)

Bootstraps the effect size deltas’ g.

	Type	Default	Details
x1	np.ndarray		Control group 1
x2	np.ndarray		Test group 1
x3	np.ndarray		Control group 2
x4	np.ndarray		Test group 2
is_paired	str	None
resamples	int	5000	The number of bootstrap resamples to be taken for the calculation of the confidence interval limits.
random_seed	int	12345	`random_seed` is used to seed the random number generator during bootstrap resampling. This ensures that the confidence intervals reported are replicable.
Returns	tuple

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compute_bootstrapped_diff

 compute_bootstrapped_diff (x0, x1, is_paired, effect_size,
                            resamples=5000, random_seed=12345)

Bootstraps the effect_size for 2 groups.

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compute_meandiff_jackknife

 compute_meandiff_jackknife (x0, x1, is_paired, effect_size)

Given two arrays, returns the jackknife for their effect size.

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create_repeated_indexes

 create_repeated_indexes (data)

Convenience function. Given an array-like with length N, returns a generator that yields N indexes [0, 1, …, N].

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create_jackknife_indexes

 create_jackknife_indexes (data)

Given an array-like, creates a jackknife bootstrap.

For a given set of data Y, the jackknife bootstrap sample J[i] is defined as the data set Y with the ith data point deleted.

	Type	Details
data
Returns	Generator that yields all jackknife bootstrap samples.