prec_prop returns the sample size or the precision for the provided
proportion.
prec_prop(
p,
n = NULL,
conf.width = NULL,
conf.level = 0.95,
method = c("wilson", "agresti-coull", "exact", "wald"),
...
)proportion.
number of observations.
precision (the full width of the confidence interval).
confidence level.
The method to use to calculate precision. Exactly one method may be provided. Methods can be abbreviated.
other arguments to uniroot (e.g. tol).
Object of class "presize", a list of arguments (including the
computed one) augmented with method and note elements. In the wilson and
agresti-coull formula, the p from which the confidence interval is
calculated is adjusted by a term (i.e. \(p + term \pm ci\)). This
adjusted p is returned in padj.
Exactly one of the parameters n or conf.width must be passed as NULL,
and that parameter is determined from the other.
The wilson, agresti-coull, exact, and wald method are implemented. The
wilson method is suggested for small n (< 40), and the agresti-coull method
is suggested for larger n (see reference). The wald method is not suggested,
but provided due to its widely distributed use.
uniroot is used to solve n for the agresti-coull,
wilson, and exact methods. Agresti-coull can be abbreviated by ac.
Brown LD, Cai TT, DasGupta A (2001) Interval Estimation for a Binomial Proportion, Statistical Science, 16:2, 101-117, doi:10.1214/ss/1009213286
binom.test, binom.confint
in package binom, and binconf in package
Hmisc
# CI width for 15\% with 50 participants
prec_prop(0.15, n = 50)
#> Warning: more than one method was chosen, 'wilson' will be used
#>
#> precision for a proportion with Wilson confidence interval.
#>
#> p padj n conf.width conf.level lwr upr
#> 1 0.15 0.1749717 50 0.1971842 0.95 0.07637956 0.2735638
#>
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.
#>
# number of participants for 15\% with a CI width of 0.2
prec_prop(0.15, conf.width = 0.2)
#> Warning: more than one method was chosen, 'wilson' will be used
#>
#> sample size for a proportion with Wilson confidence interval.
#>
#> p padj n conf.width conf.level lwr upr
#> 1 0.15 0.1756455 48.58521 0.2 0.95 0.07564555 0.2756455
#>
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.
#>
# confidence interval width for a range of scenarios between 10 and 90\% with
# 100 participants via the wilson method
prec_prop(p = 1:9 / 10, n = 100, method = "wilson")
#>
#> precision for a proportion with Wilson confidence interval.
#>
#> p padj n conf.width conf.level lwr upr
#> 1 0.1 0.1147974 100 0.1191365 0.95 0.05522914 0.1743657
#> 2 0.2 0.2110980 100 0.1554622 0.95 0.13336693 0.2888292
#> 3 0.3 0.3073987 100 0.1768997 0.95 0.21894885 0.3958485
#> 4 0.4 0.4036993 100 0.1885961 0.95 0.30940129 0.4979974
#> 5 0.5 0.5000000 100 0.1923369 0.95 0.40383153 0.5961685
#> 6 0.6 0.5963007 100 0.1885961 0.95 0.50200259 0.6905987
#> 7 0.7 0.6926013 100 0.1768997 0.95 0.60415145 0.7810511
#> 8 0.8 0.7889020 100 0.1554622 0.95 0.71117083 0.8666331
#> 9 0.9 0.8852026 100 0.1191365 0.95 0.82563434 0.9447709
#>
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.
#>
# number of participants for a range of scenarios between 10 and 90\% with
# a CI of 0.192 via the wilson method
prec_prop(p = 1:9 / 10, conf.width = .192, method = "wilson")
#>
#> sample size for a proportion with Wilson confidence interval.
#>
#> p padj n conf.width conf.level lwr upr
#> 1 0.1 0.1353927 39.57381 0.192 0.95 0.0393927 0.2313927
#> 2 0.2 0.2167537 64.94554 0.192 0.95 0.1207537 0.3127537
#> 3 0.3 0.3087050 84.41747 0.192 0.95 0.2127050 0.4047050
#> 4 0.4 0.4038339 96.35634 0.192 0.95 0.3078339 0.4998339
#> 5 0.5 0.5000000 100.36478 0.192 0.95 0.4040000 0.5960000
#> 6 0.6 0.5961661 96.35634 0.192 0.95 0.5001661 0.6921661
#> 7 0.7 0.6912950 84.41747 0.192 0.95 0.5952950 0.7872950
#> 8 0.8 0.7832463 64.94554 0.192 0.95 0.6872463 0.8792463
#> 9 0.9 0.8646073 39.57381 0.192 0.95 0.7686073 0.9606073
#>
#> NOTE: padj is the adjusted proportion, from which the ci is calculated.
#>