confint_1group
A range of functions to compute bootstraps for a single sample.
summary_ci_1group
def summary_ci_1group(
x:np.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
): # `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.
Given an array-like x, returns func(x), and a bootstrap confidence interval of func(x).
compute_1group_bias_correction
def compute_1group_bias_correction(
x, bootstraps, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
):
Call self as a function.
compute_1group_bootstraps
def compute_1group_bootstraps(
x, func, resamples:int=5000, random_seed:int=12345, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
):
Bootstraps func(x), with the number of specified resamples.
compute_1group_acceleration
def compute_1group_acceleration(
jack_dist
):
Returns the accaleration value based on the jackknife distribution.
compute_1group_jackknife
def compute_1group_jackknife(
x, func, args:VAR_POSITIONAL, kwargs:VAR_KEYWORD
):
Returns the jackknife bootstraps for func(x).
create_bootstrap_indexes
def create_bootstrap_indexes(
array, resamples:int=5000, random_seed:int=12345
):
Given an array-like, returns a generator of bootstrap indexes to be used for resampling.