Betanorm | R Documentation |

Density, distribution function, quantile function and random generation for the univariate beta-normal distribution.

```
dbetanorm(x, shape1, shape2, mean = 0, sd = 1, log = FALSE)
pbetanorm(q, shape1, shape2, mean = 0, sd = 1,
lower.tail = TRUE, log.p = FALSE)
qbetanorm(p, shape1, shape2, mean = 0, sd = 1,
lower.tail = TRUE, log.p = FALSE)
rbetanorm(n, shape1, shape2, mean = 0, sd = 1)
```

`x, q` |
vector of quantiles. |

`p` |
vector of probabilities. |

`n` |
number of observations.
Same as |

`shape1, shape2` |
the two (positive) shape parameters of the standard beta
distribution. They are called |

`mean, sd` |
the mean and standard deviation of the univariate
normal distribution
( |

`log, log.p` |
Logical.
If |

`lower.tail` |
Logical. If |

The function `betauninormal`

, the VGAM family function
for estimating the parameters,
has not yet been written.

`dbetanorm`

gives the density,
`pbetanorm`

gives the distribution function,
`qbetanorm`

gives the quantile function, and
`rbetanorm`

generates random deviates.

T. W. Yee

Gupta, A. K. and Nadarajah, S. (2004).
*Handbook of Beta Distribution and Its Applications*,
pp.146–152.
New York: Marcel Dekker.

```
## Not run:
shape1 <- 0.1; shape2 <- 4; m <- 1
x <- seq(-10, 2, len = 501)
plot(x, dbetanorm(x, shape1, shape2, m = m), type = "l",
ylim = 0:1, las = 1,
ylab = paste0("betanorm(",shape1,", ",shape2,", m=",m, ", sd=1)"),
main = "Blue is density, orange is the CDF",
sub = "Gray lines are the 10,20,...,90 percentiles", col = "blue")
lines(x, pbetanorm(x, shape1, shape2, m = m), col = "orange")
abline(h = 0, col = "black")
probs <- seq(0.1, 0.9, by = 0.1)
Q <- qbetanorm(probs, shape1, shape2, m = m)
lines(Q, dbetanorm(Q, shape1, shape2, m = m),
col = "gray50", lty = 2, type = "h")
lines(Q, pbetanorm(Q, shape1, shape2, m = m),
col = "gray50", lty = 2, type = "h")
abline(h = probs, col = "gray50", lty = 2)
pbetanorm(Q, shape1, shape2, m = m) - probs # Should be all 0
## End(Not run)
```

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