These functions use the beta distribution to provide the R Squared distribution.

```
dRsq(x, nPredictors, sampleSize, populationRsq = 0)
pRsq(q, nPredictors, sampleSize, populationRsq = 0, lower.tail = TRUE)
qRsq(p, nPredictors, sampleSize, populationRsq = 0, lower.tail = TRUE)
rRsq(n, nPredictors, sampleSize, populationRsq = 0)
```

x, q

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

p

Vector of probabilites (*p*-values).

nPredictors

The number of predictors.

sampleSize

The sample size.

n

The number of R Squared values to generate.

populationRsq

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

. If anybody knows how to do this and lets me know, I'll happily integrate this of course.

lower.tail

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

`dRsq`

gives the density, `pRsq`

gives the distribution function, `qRsq`

gives the quantile function, and `rRsq`

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 R Squared values ### with 2 predictors and 100 participants rRsq(10, 2, 100); ### Probability of finding an R Squared of ### .15 with 4 predictors and 100 participants pRsq(.15, 4, 100, lower.tail = FALSE); ### Probability of finding an R Squared of ### .15 with 15 predictors and 100 participants pRsq(.15, 15, 100, lower.tail=FALSE); # }