Compute power of test or determine parameters to obtain target power (same as power.anova.test).

1 2 |

`d` |
Effect size d=mu-mu0 |

`n` |
Number of observations |

`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 'd','n','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.

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 | ```
## Power at mu=105 for H0:mu=100 vs. H1:mu>100 (sigma=15) 20 obs. (alpha=0.05)
sigma<-15
c<-100
mu<-105
d<-(mu-c)/sigma
pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="greater")
## Sample size of the test for power=0.80
pwr.norm.test(d=d,power=0.8,sig.level=0.05,alternative="greater")
## Power function of the same test
mu<-seq(95,125,l=100)
d<-(mu-c)/sigma
plot(d,pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="greater")$power,
type="l",ylim=c(0,1))
abline(h=0.05)
abline(h=0.80)
## Power function for the two-sided alternative
plot(d,pwr.norm.test(d=d,n=20,sig.level=0.05,alternative="two.sided")$power,
type="l",ylim=c(0,1))
abline(h=0.05)
abline(h=0.80)
``` |

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