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Confidence intervals for a proportion

Source: R/statpsych3.R
ci.prop.Rd

Computes adjusted Wald (Agresi-Coull), Wilson, and exact confidence intervals for a population proportion. The Wilson confidence interval uses a continuity correction.

For more details, see Section 1.5 of Bonett (2021, Volume 3)

Usage

ci.prop(alpha, f, n)

Arguments

alpha

alpha level for 1-alpha confidence

f

number of participants who have the attribute

n

sample size

Value

Returns a 2-row matrix. The columns of row 1 are:

  • Estimate - adjusted estimate of proportion

  • SE - standard error of adjusted estimate

  • LL - lower limit of the adjusted Wald confidence interval

  • UL - upper limit of the adjusted Wald confidence interval

The columns of row 2 are:

  • Estimate - ML estimate of proportion

  • SE - standard error of ML estimate

  • LL - lower limit of the Wilson confidence interval

  • UL - upper limit of the Wilson confidence interval

The columns of row 3 are:

  • Estimate - ML estimate of proportion

  • SE - standard error of ML estimate

  • LL - lower limit of the exact confidence interval

  • UL - upper limit of the exact confidence interval

References

Bonett DG (2021). Statistical Methods for Psychologists https://dgbonett.sites.ucsc.edu/.

Agresti A, Coull BA (1998). “Approximate is better than 'exact' for interval estimation of binomial proportions.” The American Statistician, 52(2), 119–126. ISSN 0003-1305, doi:10.1080/00031305.1998.10480550 .

Examples

ci.prop(.05, 120, 300)
#>                 Estimate         SE        LL        UL
#> Adjusted Wald  0.4013158 0.02811287 0.3462156 0.4564160
#> Wilson with cc 0.4000000 0.02828427 0.3445577 0.4580464
#> Exact          0.4000000 0.02828427 0.3441290 0.4578664

# Should return:
#                 Estimate         SE        LL        UL
# Adjusted Wald  0.4013158 0.02811287 0.3462156 0.4564160
# Wilson with cc 0.4000000 0.02828427 0.3445577 0.4580464
# Exact          0.4000000 0.02828427 0.3441290 0.4578664