Description Usage Arguments Value Author(s) References Examples

View source: R/power.indep.cor.R

This function can be used to conduct power analyses for the comparison of independent correlation coefficients, as tested via Fisher's z-test. It can (1) determine the power for detecting a significant difference between 2 correlations given N ('post hoc'), and (2) determine the required N to achieve a desired power ('a-priori').

1 2 | ```
power.indep.cor(r1, r2, n1 = NULL, n2 = NULL, power = NULL,
sig.level = 0.05, alternative = "two.sided")
``` |

`r1` |
Value of first Pearson correlation coefficient, must be passed. |

`r2` |
Value of second correlation coefficient, must be passed. |

`n1` |
Sample size of group 1; pass only for 'post hoc' analysis. |

`n2` |
Sample size of group 2; pass only for 'post hoc' analysis. |

`power` |
The statistical power; pass only for 'a-priori' analysis. |

`sig.level` |
The employed alpha level, defaults to 0.05. |

`alternative` |
Must be 'greater', 'less', or 'two.sided'. 'greater' (r1 > r2) or 'less' (r1 < r2) will result in one sided tests (to be used if a directional hypothesis exists). Default test is two.sided. |

A `list`

that contains all passed and computed values.

`r1` |
passed correlation coefficient r1 |

`r2` |
passed correlation coefficient r2 |

`q` |
The effect size. Represents the difference between the z-transformed values of r1 and r2 |

`n1` |
sample size in group 1, either passed ('post hoc'), or computed ('a-priori') |

`n2` |
sample size in group 1, either passed ('post hoc'), or computed ('a-priori') |

`power` |
Power to detect a significant difference between the two correlation coefficients r1 and r2, either passed ('a-priori') or computed ('post hoc') |

`sig.level` |
The significance level that was employed for the power analysis |

`hypothesis` |
Passed argument 'alternative' |

Martin Papenberg [email protected]

Fisher RA. *Statistical Methods for Research
Workers*. Edinburgh, Scotland: Oliver and Boyd; 1925. Available:
http://psychclassics.yorku.ca.

Cohen, J. (1969). *Statistical power analysis for the
behavioural sciences*. New York: Academic Press.

1 2 3 4 5 | ```
## A priori analysis
power.indep.cor(r1 = 0.4, r2 = 0.3, power = 0.8)
## Post-hoc analysis (one-sided)
power.indep.cor(r1 = 0.4, r2 = 0.3, n1 = 450, n2 = 450, alternative = "greater")
``` |

m-Py/cower documentation built on July 8, 2018, 3:17 p.m.

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