# dist-snorm: Skew normal distribution In fGarch: Rmetrics - Autoregressive Conditional Heteroskedastic Modelling

 snorm R Documentation

## Skew normal distribution

### Description

Functions to compute density, distribution function, quantile function and to generate random variates for the skew normal distribution.

### Usage

```dsnorm(x, mean = 0, sd = 1, xi = 1.5, log = FALSE)
psnorm(q, mean = 0, sd = 1, xi = 1.5)
qsnorm(p, mean = 0, sd = 1, xi = 1.5)
rsnorm(n, mean = 0, sd = 1, xi = 1.5)
```

### Arguments

 `x, q` a numeric vector of quantiles. `p` a numeric vector of probabilities. `n` the number of observations. `mean` location parameter. `sd` scale parameter. `xi` skewness parameter. `log` a logical; if TRUE, densities are given as log densities.

### Details

`dsnorm` computed the density, `psnorm` the distribution function, `qsnorm` the quantile function, and `rsnorm` generates random deviates.

numeric vector

### Author(s)

Diethelm Wuertz for the Rmetrics R-port

### References

Fernandez C., Steel M.F.J. (2000); On Bayesian Modelling of Fat Tails and Skewness, Preprint, 31 pages.

`snormFit` (fit), `snormSlider` (visualize),

`sstd` (skew Student-t), `sged` (skew GED)

### Examples

```## snorm -
# Ranbdom Numbers:
par(mfrow = c(2, 2))
set.seed(1953)
r = rsnorm(n = 1000)
plot(r, type = "l", main = "snorm", col = "steelblue")

# Plot empirical density and compare with true density:
hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue")
box()
x = seq(min(r), max(r), length = 201)
lines(x, dsnorm(x), lwd = 2)

# Plot df and compare with true df:
plot(sort(r), (1:1000/1000), main = "Probability", col = "steelblue",
ylab = "Probability")
lines(x, psnorm(x), lwd = 2)

# Compute quantiles:
round(qsnorm(psnorm(q = seq(-1, 5, by = 1))), digits = 6)
```

fGarch documentation built on Nov. 10, 2022, 5:48 p.m.