randomizationSuccess
prob.randomizationSuccess
computes the probability that two groups are
equivalent given a specific sample size, number of nuisance variables,
and definition of 'equivalence' (in terms of the Cohen's d expressing the
maximum acceptable difference between the groups on any of the nuisance
variables).
pwr.randomizationSuccess
computes the sample size required to make randomization
succeed in a specified proportion of the studies with a twocell design.
'Success' is defined as the two groups differing at most with a specified
effect size on any of a given number or nuisance variables.
Usage
prob.randomizationSuccess(n = 1000,
dNonequivalence = .2,
nNuisanceVars = 100)
pwr.randomizationSuccess(dNonequivalence = 0.2,
pRandomizationSuccess = 0.95,
nNuisanceVars = 100)
Arguments
 n
 The sample size.
 dNonequivalence
 The maximum difference between the two groups that is deemed acceptable.
 pRandomizationSuccess

The desired probability that the randomization procedure succeeded in
generating two equivalent groups (i.e. differing at most with
dNonequivalence
).  nNuisanceVars
 The number of nuisance variables that the researchers assumes exists.
Details
For more details, see Peters & Gruijters (2017).
Value
For prob.randomizationSuccess
, the probability that the two groups
are equivalent. The function is vectorized, so returns either a vector
of length one, a vector of length > 1, a matrix, or an array.
For pwr.randomizationSuccess
, the required sample size. The function is
vectorized, so returns either a vector of length one, a vector of
length > 1, a matrix, or an array.
References
Peters, G. J.Y. & Gruijters, S. Why your experiments fail: sample sizes required for randomization to generate equivalent groups as a partial solution to the replication crisis (2017). http://dx.doi.org/
See Also
Examples
### To be on the safe side: sample size required to
### obtain 95% likelihood of success when assuming
### 100 nuisance variables exist.
pwr.randomizationSuccess(dNonequivalence = 0.2,
pRandomizationSuccess = 0.95,
nNuisanceVars = 100);
### Living on the edge:
pwr.randomizationSuccess(dNonequivalence = 0.2,
pRandomizationSuccess = 0.60,
nNuisanceVars = 10);
### For those with quite liberal ideas of 'equivalence':
pwr.randomizationSuccess(dNonequivalence = 0.5,
pRandomizationSuccess = 0.95,
nNuisanceVars = 100);
### And these results can be checked with
### prob.randomizationSuccess:
prob.randomizationSuccess(1212, .2, 100);
prob.randomizationSuccess(386, .2, 10);
prob.randomizationSuccess(198, .5, 100);
### Or in one go:
prob.randomizationSuccess(n=c(198, 386, 1212), c(.2, .5), c(10, 100));