Confidence intervals for Binomial proportions

This function obtains the confidence interval for a proportion.

ci_p(x, n, conf.level = 0.95, intervalType = "wald")

Arguments

x

a number or a vector with the number of successes.

n

a number or a vector with the number of trials.

conf.level

confidence level for the returned confidence interval. By default is 0.95.

intervalType

type of confidence interval, possible choices are: "wald", "agresti_coull", "rindskopf", "clopper_pearson", "add_4", "arcsine_cc", "arcsine", "arcsine_ac", "wilson", "ci_p_jeffreys", "hpd_jeffreys"

Value

A dataframe with the input information and the confidence interval.

See also

ci_p_wald, ci_p_agresti_coull, ci_p_rindskopf, ci_p_clopper_pearson, ci_p_add_4, ci_p_arcsine_cc, ci_p_arcsine, ci_p_arcsine_ac, ci_p_wilson, ci_p_jeffreys, ci_p_hpd_jeffreys

Author

Freddy Hernandez, fhernanb@unal.edu.co

Examples

# Examples for Wald with single values
ci_p(x=5, n=15, intervalType="wald", conf.level=0.90)
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1331281 0.5335385        0.9
ci_p(x=7, n=17, intervalType="wal", conf.level=0.95)
#>   x  n proportion     lower     upper conf.level
#> 1 7 17  0.4117647 0.1778143 0.6457151       0.95
ci_p(x=9, n=19, intervalType="wa", conf.level=0.97)
#>   x  n proportion     lower     upper conf.level
#> 1 9 19  0.4736842 0.2251028 0.7222656       0.97

# Examples for alternative IC
ci_p(x=5, n=15, intervalType="wald", conf.level=0.90)
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1331281 0.5335385        0.9
ci_p(x=5, n=15, intervalType="agresti_coull", conf.level=0.90)
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1863938 0.5504483        0.9
ci_p(x=5, n=15, intervalType="rindskopf", conf.level=0.90)
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1805812 0.5545718        0.9
ci_p(x=5, n=15, intervalType="clopper_pearson", conf.level=0.90)
#>   x  n proportion    lower     upper conf.level
#> 1 5 15  0.3333333 0.141664 0.5774437        0.9
ci_p(x=5, n=15, intervalType="arcsine_cc", conf.level=0.90)
#>       x  n proportion     lower     upper conf.level
#> lower 5 15  0.3333333 0.1289439 0.5770747        0.9
ci_p(x=5, n=15, intervalType="arcsine", conf.level=0.90)
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1538988 0.5423803        0.9

# Examples for multiple values
ci_p(x=c(5, 7, 9),
     n=c(15, 17, 19),
     intervalType="wald",
     conf.level=c(0.90, 0.95, 0.97))
#>   x  n proportion     lower     upper conf.level
#> 1 5 15  0.3333333 0.1331281 0.5335385       0.90
#> 2 7 17  0.4117647 0.1778143 0.6457151       0.95
#> 3 9 19  0.4736842 0.2251028 0.7222656       0.97