signal.to.noise.R2: Signal to noise using squared multiple correlation...

In MBESS: The MBESS R Package

Signal to noise using squared multiple correlation coefficient

Description

Function that calculates five different signal-to-noise ratios using the squared multiple correlation coefficient.

Usage

signal.to.noise.R2(R.Square, N, p)

Arguments

R.Square	usual estimate of the squared multiple correlation coefficient (with no adjustments)
N	sample size
p	number of predictors

Details

The method of choice is phi2.UMVUE.NL, but it requires p of 5 or more. In situations where p < 5, it is suggested that phi2.UMVUE.L be used.

Value

phi2.hat	Basic estimate of the signal-to-noise ratio using the usual estimate of the squared multiple correlation coefficient: phi2.hat=R.Square/(1-R.Square)
phi2.adj.hat	Estimate of the signal-to-noise ratio using the usual adjusted R Square in place of R-Square: phi2.hat=Adj.R2/(1-Adj.R2)
phi2.UMVUE	Muirhead's (1985) unique minimum variance unbiased estimate of the signal-to-noise ratio (Muirhead uses theta-U): see reference or code for formula
phi2.UMVUE.L	Muirhead's (1985) unique minimum variance unbiased linear estimate of the signal-to-noise ratio (Muirhead uses theta-L): see reference or code for formula
phi2.UMVUE.NL	Muirhead's (1985) unique minimum variance unbiased nonlinear estimate of the signal-to-noise ratio (Muirhead uses theta-NL); requires the number of predictors to be greater than five: see reference or code for formula

Author(s)

Ken Kelley (University of Notre Dame; [e-mail])

References

Cohen, J. (1988). Statistical power analysis for the behavioral sciences (2nd ed.). Hillsdale, NJ: Lawrence Erlbaum.

Muirhead, R. J. (1985). Estimating a particular function of the multiple correlation coefficient. Journal of the American Statistical Association, 80, 923–925.

See Also

ci.R2, ss.aipe.R2

Examples

signal.to.noise.R2(R.Square=.5, N=50, p=2)
signal.to.noise.R2(R.Square=.5, N=50, p=5)
signal.to.noise.R2(R.Square=.5, N=100, p=2)
signal.to.noise.R2(R.Square=.5, N=100, p=5)

MBESS documentation built on Oct. 26, 2023, 9:07 a.m.