View source: R/signal.to.noise.R2.R

signal.to.noise.R2 | R Documentation |

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

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

`R.Square` |
usual estimate of the squared multiple correlation coefficient (with no adjustments) |

`N` |
sample size |

`p` |
number of predictors |

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.

`phi2.hat` |
Basic estimate of the signal-to-noise ratio using the usual estimate of the squared multiple correlation coefficient: |

`phi2.adj.hat` |
Estimate of the signal-to-noise ratio using the usual adjusted R Square in place of |

`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 |

Ken Kelley (University of Notre Dame; KKelley@ND.Edu)

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.

`ci.R2`

, `ss.aipe.R2`

```
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)
```

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