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

```
pomegaSq(q, df1, df2, populationOmegaSq = 0, lower.tail = TRUE)
qomegaSq(p, df1, df2, populationOmegaSq = 0, lower.tail = TRUE)
romegaSq(n, df1, df2, populationOmegaSq = 0)
domegaSq(x, df1, df2, populationOmegaSq = 0)
```

- df1, df2
Degrees of freedom for the numerator and the denominator, respectively.

- 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

`ufs`

. 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.

- p
Vector of probabilites (

*p*-values).- n
Desired number of Omega Squared values.

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

`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.

```
### Generate 10 random Omega Squared values
romegaSq(10, 66, 3);
#> [1] 0.62600614 -0.29242407 -0.19426325 0.05764934 0.73700643 0.50090808
#> [7] 0.16714134 0.91569706 0.90378854 0.67836235
### Probability of findings an Omega Squared
### value smaller than .06 if it's 0 in the population
pomegaSq(.06, 66, 3);
#> [1] 0.4280867
```