prec_prop returns the sample size or the precision for the provided
proportion.
Usage
prec_prop(
p,
n = NULL,
conf.width = NULL,
conf.level = 0.95,
method = c("wilson", "agresti-coull", "exact", "wald"),
...
)Value
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.
Details
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.
References
Brown LD, Cai TT, DasGupta A (2001) Interval Estimation for a Binomial Proportion, Statistical Science, 16:2, 101-117, doi:10.1214/ss/1009213286
See also
binom.test, binom.confint
in package binom, and binconf in package
Hmisc
Examples
# 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.
#>