Epi: ci.pd – R documentation

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ci.pd

Compute confidence limits for a difference of two independent proportions.

Description

The usual formula for the c.i. of at difference of proportions is inaccurate. Newcombe has compared 11 methods and method 10 in his paper looks like a winner. It is implemented here.

Usage

ci.pd(aa, bb=NULL, cc=NULL, dd=NULL,
     method = "Nc",
      alpha = 0.05, conf.level=0.95,
     digits = 3,
      print = TRUE,
detail.labs = FALSE )

Arguments

`aa`	Numeric vector of successes in sample 1. Can also be a matrix or array (see details).
`bb`	Successes in sample 2.
`cc`	Failures in sample 1.
`dd`	Failures in sample 2.
`method`	Method to use for calculation of confidence interval, see "Details".
`alpha`	Significance level
`conf.level`	Confidence level
`print`	Should an account of the two by two table be printed.
`digits`	How many digits should the result be rounded to if printed.
`detail.labs`	Should the computing of probability differences be reported in the labels.

Details

Implements method 10 from Newcombe(1998) (method="Nc") or from Agresti & Caffo(2000) (method="AC").

aa, bb, cc and dd can be vectors. If aa is a matrix, the elements [1:2,1:2] are used, with successes aa[,1:2]. If aa is a three-way table or array, the elements aa[1:2,1:2,] are used.

Value

A matrix with three columns: probability difference, lower and upper limit. The number of rows equals the length of the vectors aa, bb, cc and dd or, if aa is a 3-way matrix, dim(aa)[3].

Author(s)

Bendix Carstensen, Esa Laara. http://bendixcarstensen.com

References

RG Newcombe: Interval estimation for the difference between independent proportions. Comparison of eleven methods. Statistics in Medicine, 17, pp. 873-890, 1998.

A Agresti & B Caffo: Simple and effective confidence intervals for proportions and differences of proportions result from adding two successes and two failures. The American Statistician, 54(4), pp. 280-288, 2000.

Examples

( a <- matrix( sample( 10:40, 4 ), 2, 2 ) )
ci.pd( a )
twoby2( t(a) )
prop.test( t(a) )
( A <- array( sample( 10:40, 20 ), dim=c(2,2,5) ) )
ci.pd( A )
ci.pd( A, detail.labs=TRUE, digits=3 )

Epi

Statistical Analysis in Epidemiology

v2.44

GPL-2

Authors

Bendix Carstensen [aut, cre], Martyn Plummer [aut], Esa Laara [ctb], Michael Hills [ctb]

Initial release

2021-02-28