confint_1group

A range of functions to compute bootstraps for a single sample.

summary_ci_1group

 summary_ci_1group (x:<built-infunctionarray>, func, resamples:int=5000,
                    alpha:float=0.05, random_seed:int=12345,
                    sort_bootstraps:bool=True, *args, **kwargs)

Given an array-like x, returns func(x), and a bootstrap confidence interval of func(x).

	Type	Default	Details
x	array		An numerical iterable.
func			The function to be applied to x.
resamples	int	5000	The number of bootstrap resamples to be taken of func(x).
alpha	float	0.05	Denotes the likelihood that the confidence interval produced does not include the true summary statistic. When alpha = 0.05, a 95% confidence interval is produced.
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.
sort_bootstraps	bool	True
args	VAR_POSITIONAL
kwargs	VAR_KEYWORD
Returns	A dictionary with the following five keys:		`summary`: float. The outcome of func(x). `func`: function. The function applied to x. `bca_ci_low`: float `bca_ci_high`: float. The bias-corrected and accelerated confidence interval, for the given alpha. `bootstraps`: array. The bootstraps used to generate the confidence interval. These will be sorted in ascending order if `sort_bootstraps` was True.

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compute_1group_bias_correction

 compute_1group_bias_correction (x, bootstraps, func, *args, **kwargs)

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compute_1group_bootstraps

 compute_1group_bootstraps (x, func, resamples=5000, random_seed=12345,
                            *args, **kwargs)

Bootstraps func(x), with the number of specified resamples.

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compute_1group_acceleration

 compute_1group_acceleration (jack_dist)

Returns the accaleration value based on the jackknife distribution.

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compute_1group_jackknife

 compute_1group_jackknife (x, func, *args, **kwargs)

Returns the jackknife bootstraps for func(x).

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create_bootstrap_indexes

 create_bootstrap_indexes (array, resamples=5000, random_seed=12345)

Given an array-like, returns a generator of bootstrap indexes to be used for resampling.