The Wilson score confidence interval with continuity correction for the binomial probability

Source: R/Wilson_score_CI_CC_1x2.R

Described in Chapter 2 "The 1x2 Table and the Binomial Distribution"

Wilson_score_CI_CC_1x2(X, n, alpha = 0.05)

Arguments

X

the number of successes

n

the total number of observations

alpha

the nominal level, e.g. 0.05 for 95% CIs

Value

An object of the contingencytables_result class, basically a subclass of base::list(). Use the utils::str() function to see the specific elements returned.

References

Reference Wilson EB (1927) Probable inference, the law of succession, and statistical inference. Journal of the American Statistical Association; 22209-212

Examples

# The number of 1st order male births (Singh et al. 2010)
Wilson_score_CI_CC_1x2(singh_2010["1st", "X"], singh_2010["1st", "n"])
#> The Wilson score CI: estimate = 0.4690 (95% CI 0.4261 to 0.5124)
# The number of 2nd order male births (Singh et al. 2010)
Wilson_score_CI_CC_1x2(singh_2010["2nd", "X"], singh_2010["2nd", "n"])
#> The Wilson score CI: estimate = 0.4951 (95% CI 0.4459 to 0.5444)
# The number of 3rd order male births (Singh et al. 2010)
Wilson_score_CI_CC_1x2(singh_2010["3rd", "X"], singh_2010["3rd", "n"])
#> The Wilson score CI: estimate = 0.6168 (95% CI 0.5382 to 0.6899)
# The number of 4th order male births (Singh et al. 2010)
with(singh_2010["4th", ], Wilson_score_CI_CC_1x2(X, n)) # alternative syntax
#> The Wilson score CI: estimate = 0.7333 (95% CI 0.5779 to 0.8490)
# Ligarden et al. (2010)
Wilson_score_CI_CC_1x2(ligarden_2010["X"], ligarden_2010["n"])
#> The Wilson score CI: estimate = 0.8125 (95% CI 0.5369 to 0.9503)