stats.proportion.proportion_confint()
statsmodels.stats.proportion.proportion_confint
-
statsmodels.stats.proportion.proportion_confint(count, nobs, alpha=0.05, method='normal')
[source] -
confidence interval for a binomial proportion
Parameters: count : int or array
number of successes
nobs : int
total number of trials
alpha : float in (0, 1)
significance level, default 0.05
method : string in [‘normal’]
method to use for confidence interval, currently available methods :
-
normal
: asymptotic normal approximation -
agresti_coull
: Agresti-Coull interval -
beta
: Clopper-Pearson interval based on Beta distribution -
wilson
: Wilson Score interval -
jeffreys
: Jeffreys Bayesian Interval -
binom_test
: experimental, inversion of binom_test
Returns: ci_low, ci_upp : float
lower and upper confidence level with coverage (approximately) 1-alpha. Note: Beta has coverage coverage is only 1-alpha on average for some other methods.)
Notes
Beta, the Clopper-Pearson interval has coverage at least 1-alpha, but is in general conservative. Most of the other methods have average coverage equal to 1-alpha, but will have smaller coverage in some cases.
Method “binom_test” directly inverts the binomial test in scipy.stats. which has discrete steps.
- TODO: binom_test intervals raise an exception in small samples if one
- interval bound is close to zero or one.
References
http://en.wikipedia.org/wiki/Binomial_proportion_confidence_interval
- Brown, Lawrence D.; Cai, T. Tony; DasGupta, Anirban (2001). “Interval
- Estimation for a Binomial Proportion”, Statistical Science 16 (2): 101–133. doi:10.1214/ss/1009213286. TODO: Is this the correct one ?
-
© 2009–2012 Statsmodels Developers
© 2006–2008 Scipy Developers
© 2006 Jonathan E. Taylor
Licensed under the 3-clause BSD License.
http://www.statsmodels.org/stable/generated/statsmodels.stats.proportion.proportion_confint.html