zepid.base.Diagnostics¶
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class
zepid.base.
Diagnostics
(alpha=0.05)¶ Generates the Sensitivity, Specificity, and the corresponding (1-alpha)% confidence intervals, comparing test results to disease status from pandas DataFrame
Sensitivity is calculated from
\[Se = \frac{TP}{P}\]Wald standard error is
\[SE_{Wald} = \left(\frac{1}{TP} - \frac{1}{P}\right)^{\frac{1}{2}}\]Specificity is calculated from
\[Sp = \frac{FN}{N}\]Wald standard error is
\[SE_{Wald} = \left(\frac{1}{FN} - \frac{1}{N}\right)^{\frac{1}{2}}\]Note
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 Examples
Calculate the sensitivity and specificity in a data set
>>> from zepid import Diagnostics, load_sample_data >>> df = load_sample_data(False) >>> diag = Diagnostics() >>> diag.fit(df, test='art', disease='dead') # Note this example is not great... ART is a treatment not test >>> diag.summary()
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__init__
(alpha=0.05)¶ Initialize self. See help(type(self)) for accurate signature.
Methods
__init__
([alpha])Initialize self. fit
(df, test, disease)Calculates sensitivity and specificity summary
([decimal])Prints the results -