prec_prop: Sample size or precision for a proportion
In a-lenz/presize: Precision Based Sample Size Calculation

Description Usage Arguments Details Value References See Also Examples

View source: R/descriptive_stats.R

prec_prop returns the sample size or the precision for the provided proportion

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prec_prop(p, n = NULL, conf.width = NULL, conf.level = 0.95,
  method = c("wilson", "agresti-coull", "exact", "wald"),
  tol = .Machine$double.eps^0.25)

`p`	proportion
`n`	number of observations
`conf.width`	precision (the full width of the conficende interval)
`conf.level`	confidence level
`method`	The method to use to calculate precision. Exactly one method may be provided. Methods can be abbreviated.
`tol`	numerical tolerance used in root finding, the default providing (at least) four significant digits

Exactly one of the parameters n, 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 mehtod 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 method. Agresti-coull can be abbreviated by ac.

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 wich the confidence interval is calculated is adjusted by a term (i.e. p + term \pm ci). This adjusted p is returned in padj.

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