Before conducting an experiment, one should compute the power - or, preferably, estimate the precision of the expected results. There are numerous way to achieve this, here’s one using the R package pwr.
Package pwr
library(pwr)The workhorse function here is pwr.f2.test. Note that f2 refers to the effect size \(f^2\) (see here), defined as:
\[f^2 = \frac{R^2}{1-R^2}\].
See for details of the function its help page:
help("pwr.f2.test")pwr.f2.test(u = NULL, v = NULL, f2 = NULL, sig.level = 0.05, power = NULL)Note that u is the number of predictors (\(k\)) minus 1, that’s the numerator degrees of freedom (df). The intercepts counts a predictor, too in this case!
v is the denominator df, defined as \(n-p\), where \(n\) is the sample size (see here for some details on df).
By the way, if you wonder, what df actually are, read this nice little explanation.
Let’s look at a example. In a power analysis before the experiment, one states the desired level of power, and the expected effect size, for example:
# example: k=3 predictors, n=100 observations, R^2 = .1, sig.level = .05 (default)
pwr.f2.test(u = 2,
v = 97,
f2 = .1/.9,
power = NULL)##
## Multiple regression power calculation
##
## u = 2
## v = 97
## f2 = 0.1111111
## sig.level = 0.05
## power = 0.8447176Given this values, we will have a power of about 84% (if all assumptions are met).
The other way round: How large should the sample be to get a power of .8, given \(R^2=.^1\)?
# example: k=3 predictors, power = .8, R^2 = .1
pwr.f2.test(u = 2,
f2 = .1/.9,
power = .8,
v = NULL)##
## Multiple regression power calculation
##
## u = 2
## v = 86.77678
## f2 = 0.1111111
## sig.level = 0.05
## power = 0.8v = 87, ie, \(n=v+p=87+3=90\). That’s the estimated sample size we need.