ci_prop_agresti_coull

Calculates the <code>Agresti-Coull</code> interval (created by <code>Alan Agresti</code> and <code>Brent Coull</code>) by
(for 95% CI) adding two successes and two failures to the data and then using the Wald formula to construct a CI.

This calculates a variety of different CIs for proportions
and difference of proportions that are commonly used in the
pharmaceutical industry including Wald, Wilson, Clopper-Pearson,
Agresti-Coull and Jeffreys for proprotions. And Miettinen-Nurminen
(1985) <doi:10.1002/sim.4780040211>, Wald, Haldane, and Mee
<https://www.lexjansen.com/wuss/2016/127_Final_Paper_PDF.pdf> for
difference in proportions.

Christina Fillmore

cicalc

Calculate Confidence Intervals

GlaxoSmithKline Research & Development Limited 

Mike Sprys

Dan Lythgoe

ci_prop_agresti_coull function

<dl><dt>x</dt>
<dd>(<code>binary</code>/<code>numeric</code>/<code>logical</code>) 
vector of a binary values, i.e. a logical vector, or numeric with values <code>c(0, 1)</code></dd>
<dt>conf.level</dt>
<dd>(<code>scalar numeric</code>) a scalar in (0,1) indicating the confidence level. Default is 0.95</dd>
<dt>data</dt>
<dd>(<code>data.frame</code>) Optional data frame containing the variables specified in <code>x</code> and <code>by</code>.</dd></dl>

Arguments

Agresti-Coull CI — ci_prop_agresti_coull

<dl>

<dt>x</dt>
<dd>(<code>binary</code>/<code>numeric</code>/<code>logical</code>) 
vector of a binary values, i.e. a logical vector, or numeric with values <code>c(0, 1)</code></dd>


<dt>conf.level</dt>
<dd>(<code>scalar numeric</code>) a scalar in (0,1) indicating the confidence level. Default is 0.95</dd>


<dt>data</dt>
<dd>(<code>data.frame</code>) Optional data frame containing the variables specified in <code>x</code> and <code>by</code>.</dd>

</dl>

ci_prop_agresti_coull: Agresti-Coull CI

Description

Usage

Value

Arguments

Details