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)

## Arguments

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 `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. |

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. |

## Value

`domegaSq`

gives the density, `pomegaSq`

gives the
distribution function, `qomegaSq`

gives the quantile function, and
`romegaSq`

generates random deviates.

## Details

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

and
`convert.f.to.omegasq()`

to provide the Omega Squared
distribution.

## See also

## Examples

### Generate 10 random Omega Squared values
romegaSq(10, 66, 3);

#> [1] 0.3771096 0.2769963 0.8639086 -1.2657636 0.5854294 -0.3364410
#> [7] -1.1854040 -0.6081699 0.5009081 0.1671413

### Probability of findings an Omega Squared
### value smaller than .06 if it's 0 in the population
pomegaSq(.06, 66, 3);

#> [1] 0.4280867