These functions use some conversion to and from the *F* distribution to provide the Omega Squared distribution.

```
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)
```

x, q

Vector of quantiles, or, in other words, the value(s) of Omega Squared.

p

Vector of probabilites (*p*-values).

df1, df2

Degrees of freedom for the numerator and the denominator, respectively.

n

Desired number of Omega Squared values.

populationOmegaSq

The value of Omega Squared in the population; this determines the center of the Omega Squared distribution. This has not been implemented yet in this version of `userfriendlyscience`

. If anybody has the inverse of `convert.ncf.to.omegasq`

for me, I'll happily integrate this.

lower.tail

logical; if TRUE (default), probabilities are the likelihood of finding an Omega Squared smaller than the specified value; otherwise, the likelihood of finding an Omega Squared larger than the specified value.

`domegaSq`

gives the density, `pomegaSq`

gives the distribution function, `qomegaSq`

gives the quantile function, and `romegaSq`

generates random deviates.

The functions use `convert.omegasq.to.f`

and `convert.f.to.omegasq`

to provide the Omega Squared distribution.

# NOT RUN { ### 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); # }