
domegaSq(x, df1, df2, populationOmegaSq = 0)
pomegaSq(q, df1, df2, populationOmegaSq = 0, lower.tail = TRUE)
qomegaSq(p, df1, df2, populationOmegaSq = 0, lower.tail = TRUE)
romegaSq(n, df1, df2, populationOmegaSq = 0)
userfriendlyscience
. If anybody has the inverse of convert.ncf.to.omegasq
for me, I'll happily integrate this.
domegaSq
gives the density, pomegaSq
gives the distribution function, qomegaSq
gives the quantile function, and romegaSq
generates random deviates.convert.omegasq.to.f
and convert.f.to.omegasq
to provide the Omega Squared distribution.
convert.omegasq.to.f
, convert.f.to.omegasq
, df
, pf
, qf
, rf
### Generate 10 random Omega Squared values
romegaSq(10, 66, 3);
### Probability of findings an Omega Squared
### value smaller than .06 if it's 0 in the population
pomegaSq(.06, 66, 3);
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