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.
- Vector of probabilites (p-values).
- df1, df2
- Degrees of freedom for the numerator and the denominator, respectively.
- Desired number of Omega Squared values.
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.omegasqfor 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.
domegaSq gives the density,
pomegaSq gives the distribution function,
qomegaSq gives the quantile function, and
romegaSq generates random deviates.
### 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);