wilson.ci: Confidence Intervals for Proportions

In fastR: Data sets and utilities for Foundations and Applications of Statistics by R Pruim

Description

Alternatives to codeprop.test() and binom.test().

Usage

wilson.ci(x, n = 100, conf.level = 0.95)
wald.ci(x, n = 100, conf.level = 0.95) 

Arguments

x	number of 'successes'
n	number of trials
conf.level	confidence level

Details

wald.ci() produces Wald confidence intervals. wilson.ci() produces Wilson confidence intervals (also called “plus-4” confidence intervals) which are Wald intervals computed from data formed by adding 2 successes and 2 failures. The Wilson confidence intervals have better coverage rates for small samples.

Value

Lower and upper bounds of a two-sided confidence interval.

Author(s)

Randall Pruim

References

A. Agresti and B. A. Coull, Approximate is better then ‘exact’ for interval estimation of binomial proportions, American Statistician 52 (1998), 119–126.

Examples

prop.test(12,30)
prop.test(12,30, correct=FALSE)
wald.ci(12,30)
wilson.ci(12,30)
wald.ci(12+2,30+4)

Example output

Loading required package: mosaic
Loading required package: dplyr

Attaching package: 'dplyr'

The following objects are masked from 'package:stats':

    filter, lag

The following objects are masked from 'package:base':

    intersect, setdiff, setequal, union

Loading required package: lattice
Loading required package: ggformula
Loading required package: ggplot2
Loading required package: ggstance

Attaching package: 'ggstance'

The following objects are masked from 'package:ggplot2':

    GeomErrorbarh, geom_errorbarh


New to ggformula?  Try the tutorials: 
	learnr::run_tutorial("introduction", package = "ggformula")
	learnr::run_tutorial("refining", package = "ggformula")
Loading required package: mosaicData
Loading required package: Matrix

The 'mosaic' package masks several functions from core packages in order to add 
additional features.  The original behavior of these functions should not be affected by this.

Note: If you use the Matrix package, be sure to load it BEFORE loading mosaic.

Attaching package: 'mosaic'

The following object is masked from 'package:Matrix':

    mean

The following object is masked from 'package:ggplot2':

    stat

The following objects are masked from 'package:dplyr':

    count, do, tally

The following objects are masked from 'package:stats':

    IQR, binom.test, cor, cor.test, cov, fivenum, median, prop.test,
    quantile, sd, t.test, var

The following objects are masked from 'package:base':

    max, mean, min, prod, range, sample, sum

Loading required package: mosaicCalc
Loading required package: mosaicCore

Attaching package: 'mosaicCore'

The following objects are masked from 'package:dplyr':

    count, tally


Attaching package: 'mosaicCalc'

The following object is masked from 'package:stats':

    D


Attaching package: 'fastR'

The following object is masked from 'package:graphics':

    panel.smooth


	1-sample proportions test with continuity correction

data:  12 out of 30
X-squared = 0.83333, df = 1, p-value = 0.3613
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
 0.2322334 0.5924978
sample estimates:
  p 
0.4 


	1-sample proportions test without continuity correction

data:  12 out of 30
X-squared = 1.2, df = 1, p-value = 0.2733
alternative hypothesis: true p is not equal to 0.5
95 percent confidence interval:
 0.2459063 0.5767964
sample estimates:
  p 
0.4 

[1] 0.2246955 0.5753045
attr(,"conf.level")
[1] 0.95
[1] 0.2463368 0.5771926
attr(,"conf.level")
[1] 0.95
[1] 0.2463368 0.5771926
attr(,"conf.level")
[1] 0.95

fastR documentation built on May 2, 2019, 5:53 p.m.