class zepid.base.Specificity(alpha=0.05)

Generates the sensitivity and (1-alpha)% confidence interval, comparing test results to disease status from pandas dataframe

Specificity is calculated from

\[Sp = \frac{FN}{N}\]

Wald standard error is

\[SE_{Wald} = (\frac{1}{FN} - \frac{1}{N})^{\frac{1}{2}}\]


Disease & Test must be coded as (1: yes, 0:no)

Parameters:alpha (float, optional) – Alpha value to calculate two-sided Wald confidence intervals. Default is 95% confidence interval


Calculate the specificity in a data set >>> from zepid import Specificity, load_sample_data >>> df = load_sample_data(False) >>> spec = Specificity() >>> spec.fit(df, test=’art’, disease=’dead’) # Note this example is not great… ART is a treatment not test >>> spec.summary()


Initialize self. See help(type(self)) for accurate signature.


__init__([alpha]) Initialize self.
fit(df, test, disease) Calculates specificity
summary([decimal]) Prints the summary results