predictionInterval-package {predictionInterval}

Prediction Interval Functions

Description

A common problem faced by journal reviewers and authors is the question of whether the results of a replication study are consistent with the original published study. One solution to this problem is to examine the effect size from the original study and generate the range of effect sizes that could reasonably be obtained (due to random sampling) in a replication attempt (i.e., calculate a prediction interval).This package has functions that calculate the prediction interval for the correlation (i.e., r), standardized mean difference (i.e., d-value), and mean.

Details

Package:	predictionInterval
Type:	Package
Version:	1.0.0
Date:	2016-08-19
License:	MIT License + file LICENSE

pi.r creates a prediction interval for a correlation (i.e., r )
pi.d creates a prediction interval for a standardized mean difference (i.e., d )
pi.m creates a prediction interval for a mean (i.e., M )

pi.r.demo demonstrates PI capture percentage for a correlation (i.e., r )
pi.d.demo demonstrates PI capture percentage for a standardized mean difference (i.e., d )
pi.m.demo demonstrates PI capture percentage for a mean (i.e., M )

Author(s)

Author:	David J. Stanley [e-mail]
Maintainer:	David J. Stanley [e-mail]

References

Spence, J.R. & Stanley, D.J.(in prep). Prediction Interval: What to expect when you're expecting a replication.


Also:

Cumming, G. & Maillardet, R. (2006). Confidence intervals and replication: where will the next mean fall? Psychological Methods, 11(3), 217-227.

Estes, W.K. (1997). On the communication of information by displays of standard error and confidence intervals. Psychonomic Bulleting & Review, 4(3), 330-341.

Zou, G.Y. (2007). Toward using a confidence intervals to compare correlations. Psychological Methods, 12(4), 399-413.

Examples

pi.r(r=.35,n=100,rep.n=200)
pi.d(d=.65,n1=50,n2=50,rep.n1=100,rep.n2=100)
pi.m(M=2.53,SD=1.02,n=40,rep.n=80)