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)Degrees of freedom for the numerator and the denominator, respectively.
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.
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.
Vector of probabilites (p-values).
Desired number of Omega Squared values.
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] -0.1627837 0.8525135 0.8628938 -0.1083560 0.6353297 0.5771397
#> [7] -1.0905677 0.8042521 0.6963578 -0.2544407
### Probability of findings an Omega Squared
### value smaller than .06 if it's 0 in the population
pomegaSq(.06, 66, 3);
#> [1] 0.4280867