Description Details Author(s) References See Also Examples

Power calculations along the lines of Cohen (1988) using in particular the same notations for effect sizes. Examples from the book are given.

Package: | pwr |

Type: | Package |

Version: | 1.3-0 |

Date: | 2020-03-16 |

License: | GPL (>= 3) |

This package contains functions for basic power calculations using effect sizes and notations from Cohen (1988) : pwr.p.test: test for one proportion (ES=h) pwr.2p.test: test for two proportions (ES=h) pwr.2p2n.test: test for two proportions (ES=h, unequal sample sizes) pwr.t.test: one sample and two samples (equal sizes) t tests for means (ES=d) pwr.t2n.test: two samples (different sizes) t test for means (ES=d) pwr.anova.test: test for one-way balanced anova (ES=f) pwr.r.test: correlation test (ES=r) pwr.chisq.test: chi-squared test (ES=w) pwr.f2.test: test for the general linear model (ES=f2) ES.h: computing effect size h for proportions tests ES.w1: computing effect size w for the goodness of fit chi-squared test ES.w2: computing effect size w for the association chi-squared test cohen.ES: computing effect sizes for all the previous tests corresponding to conventional effect sizes (small, medium, large)

Stephane Champely, based on previous works by Claus Ekstrom and Peter Dalgaard, with contributions of Jeffrey Gill, Stephan Weibelzahl, Clay Ford, Aditya Anandkumar and Robert Volcic.

Maintainer: Helios De Rosario-Martinez <helios.derosario@gmail.com>

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

power.t.test,power.prop.test,power.anova.test

1 2 3 4 5 6 7 8 9 10 11 | ```
## Exercise 8.1 P. 357 from Cohen (1988)
pwr.anova.test(f=0.28,k=4,n=20,sig.level=0.05)
## Exercise 6.1 p. 198 from Cohen (1988)
pwr.2p.test(h=0.3,n=80,sig.level=0.05,alternative="greater")
## Exercise 7.3 p. 251
pwr.chisq.test(w=0.346,df=(2-1)*(3-1),N=140,sig.level=0.01)
## Exercise 6.5 p. 203 from Cohen (1988)
pwr.p.test(h=0.2,n=60,sig.level=0.05,alternative="two.sided")
``` |

```
Balanced one-way analysis of variance power calculation
k = 4
n = 20
f = 0.28
sig.level = 0.05
power = 0.5149793
NOTE: n is number in each group
Difference of proportion power calculation for binomial distribution (arcsine transformation)
h = 0.3
n = 80
sig.level = 0.05
power = 0.5996777
alternative = greater
NOTE: same sample sizes
Chi squared power calculation
w = 0.346
N = 140
df = 2
sig.level = 0.01
power = 0.8854053
NOTE: N is the number of observations
proportion power calculation for binomial distribution (arcsine transformation)
h = 0.2
n = 60
sig.level = 0.05
power = 0.3408451
alternative = two.sided
```

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