Description Usage Arguments Details Value Note Author(s) References See Also Examples

Compute power of test, or determine parameters to obtain target power.

1 2 |

`h` |
Effect size |

`n1` |
Number of observations in the first sample |

`n2` |
Number of observations in the second sample |

`sig.level` |
Significance level (Type I error probability) |

`power` |
Power of test (1 minus Type II error probability) |

`alternative` |
a character string specifying the alternative hypothesis, must be one of "two.sided" (default), "greater" or "less" |

Exactly one of the parameters 'h','n1', 'n2', 'power' and 'sig.level' must be passed as NULL, and that parameter is determined from the others. Notice that the last one has non-NULL default so NULL must be explicitly passed if you want to compute it.

Object of class '"power.htest"', a list of the arguments (including the computed one) augmented with 'method' and 'note' elements.

'uniroot' is used to solve power equation for unknowns, so you may see errors from it, notably about inability to bracket the root when invalid arguments are given.

Stephane Champely <champely@univ-lyon1.fr> but this is a mere copy of Peter Dalgaard work (power.t.test)

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

ES.h, pwr.2p.test, power.prop.test

1 2 3 4 5 | ```
## Exercise 6.3 P. 200 from Cohen (1988)
pwr.2p2n.test(h=0.30,n1=80,n2=245,sig.level=0.05,alternative="greater")
## Exercise 6.7 p. 207 from Cohen (1988)
pwr.2p2n.test(h=0.20,n1=1600,power=0.9,sig.level=0.01,alternative="two.sided")
``` |

```
difference of proportion power calculation for binomial distribution (arcsine transformation)
h = 0.3
n1 = 80
n2 = 245
sig.level = 0.05
power = 0.7532924
alternative = greater
NOTE: different sample sizes
difference of proportion power calculation for binomial distribution (arcsine transformation)
h = 0.2
n1 = 1600
n2 = 484.6646
sig.level = 0.01
power = 0.9
alternative = two.sided
NOTE: different sample sizes
```

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