Bootstrap

bootstrap

 bootstrap (x1:<built-infunctionarray>, x2:<built-infunctionarray>=None,
            paired:bool=False, stat_function:<built-
            infunctioncallable>=<function mean>, smoothboot:bool=False,
            alpha_level:float=0.05, reps:int=5000)

Computes the summary statistic and a bootstrapped confidence interval.

	Type	Default	Details
x1	array		The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.
x2	array	None	The data in a one-dimensional array form. Only x1 is required. If x2 is given, the bootstrapped summary difference between the two groups (x2-x1) is computed. NaNs are automatically discarded.
paired	bool	False	Whether or not x1 and x2 are paired samples. If ‘paired’ is None then the data will not be treated as paired data in the subsequent calculations. If ‘paired’ is ‘baseline’, then in each tuple of x, other groups will be paired up with the first group (as control). If ‘paired’ is ‘sequential’, then in each tuple of x, each group will be paired up with the previous group (as control).
stat_function	callable	mean	The summary statistic called on data.
smoothboot	bool	False	Taken from seaborn.algorithms.bootstrap. If True, performs a smoothed bootstrap (draws samples from a kernel destiny estimate).
alpha_level	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.
reps	int	5000	Number of bootstrap iterations to perform.
Returns	An `bootstrap` object reporting the summary statistics, percentile CIs, bias-corrected and accelerated (BCa) CIs, and the settings used:		`summary`: float. The summary statistic. `is_difference`: boolean. Whether or not the summary is the difference between two groups. If False, only x1 was supplied. `is_paired`: string, default None The type of the experiment under which the data are obtained `statistic`: callable The function used to compute the summary. `reps`: int The number of bootstrap iterations performed. `stat_array`:array A sorted array of values obtained by bootstrapping the input arrays. `ci`:float The size of the confidence interval reported (in percentage). `pct_ci_low,pct_ci_high`:floats The upper and lower bounds of the confidence interval as computed by taking the percentage bounds. `pct_low_high_indices`:array An array with the indices in `stat_array` corresponding to the percentage confidence interval bounds. `bca_ci_low, bca_ci_high`: floats The upper and lower bounds of the bias-corrected and accelerated(BCa) confidence interval. See Efron 1977. `bca_low_high_indices`: array An array with the indices in `stat_array` corresponding to the BCa confidence interval bounds. `pvalue_1samp_ttest`: float P-value obtained from scipy.stats.ttest_1samp. If 2 arrays were passed (x1 and x2), returns ‘NIL’. See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_1samp.html `pvalue_2samp_ind_ttest`: float P-value obtained from scipy.stats.ttest_ind. If a single array was given (x1 only), or if `paired` is not None, returns ‘NIL’. See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_ind.html `pvalue_2samp_related_ttest`: float P-value obtained from scipy.stats.ttest_rel. If a single array was given (x1 only), or if `paired` is None, returns ‘NIL’. See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.ttest_rel.html `pvalue_wilcoxon`: float P-value obtained from scipy.stats.wilcoxon. If a single array was given (x1 only), or if `paired` is None, returns ‘NIL’. The Wilcoxons signed-rank test is a nonparametric paired test of the null hypothesis that the related samples x1 and x2 are from the same distribution. See https://docs.scipy.org/doc/scipy-1.0.0/reference/scipy.stats.wilcoxon.html `pvalue_mann_whitney`: float Two-sided p-value obtained from scipy.stats.mannwhitneyu. If a single array was given (x1 only), returns ‘NIL’. The Mann-Whitney U-test is a nonparametric unpaired test of the null hypothesis that x1 and x2 are from the same distribution. See https://docs.scipy.org/doc/scipy-1.0.0/reference/generated/scipy.stats.mannwhitneyu.html

bca

 bca (data, alphas, stat_array, stat_function, ostat, reps)

Subroutine called to calculate the BCa statistics. Borrowed heavily from scikits.bootstrap code.

jackknife_indexes

 jackknife_indexes (data)

From the scikits.bootstrap package. Given an array, returns a list of arrays where each array is a set of jackknife indexes.

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