The Agresti-Coull confidence interval for the binomial probability

Source: R/AgrestiCoull_CI_1x2.R

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

AgrestiCoull_CI_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

Agresti A, Coull BA (1998) Approximate is better than "exact" for interval estimation of binomial proportions. The American Statistician; 52:119-126

See also

Wald_CI_1x2

Examples

AgrestiCoull_CI_1x2(singh_2010["1st", "X"], singh_2010["1st", "n"])
#> The Agresti-Coull CI: estimate = 0.4693 (95% CI 0.4271 to 0.5115)
AgrestiCoull_CI_1x2(singh_2010["2nd", "X"], singh_2010["2nd", "n"])
#> The Agresti-Coull CI: estimate = 0.4952 (95% CI 0.4471 to 0.5432)
AgrestiCoull_CI_1x2(singh_2010["3rd", "X"], singh_2010["3rd", "n"])
#> The Agresti-Coull CI: estimate = 0.6140 (95% CI 0.5411 to 0.6870)
with(singh_2010["4th", ], AgrestiCoull_CI_1x2(X, n)) # alternative syntax
#> The Agresti-Coull CI: estimate = 0.7143 (95% CI 0.5878 to 0.8408)
AgrestiCoull_CI_1x2(ligarden_2010["X"], ligarden_2010["n"])
#> The Agresti-Coull CI: estimate = 0.7500 (95% CI 0.5602 to 0.9398)