snormDistribution {fSeries} | R Documentation |
A collection of functions to compute density, distribution function,
quantile function and to generate random variates for the skew
normal distribution.
The functions are:
1 | [dpqr]norm | Normal distribution from R's base package, |
2 | [dpqr]snorm | Skew Normal distribution. |
dsnorm(x, mean = 0, sd = 1, xi = 1.5) 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)
mean, sd, xi |
location parameter mean ,
scale parameter sd ,
skewness parameter xi .
|
n |
number of observations. |
p |
a numeric vector of probabilities. |
x, q |
a numeric vector of quantiles. |
Symmetric Normal Distibution:
The functions for the normal distribution are part of R's
base package.
Skew Normal Distribution:
The skew normal distribution functions are defined as described
by Fernandez and Steel (2000).
All values are numeric vectors:
d*
returns the density,
p*
returns the distribution function,
q*
returns the quantile function, and
r*
generates random deviates.
Diethelm Wuertz for this R-port.
Fernandez C., Steel M.F.J. (2000); On Bayesian Modelling of Fat Tails and Skewness, Preprint, 31 pages.
sstdDistribution
,
sgedDistribution
.
## snorm - xmpSeries("\nStart: Skew Normal Distribuion: > ") par(mfrow = c(2, 2), cex = 0.75) set.seed(1953) r = rsnorm(n = 1000, mean = 1, sd = 0.5, xi = 1.5) plot(r, type = "l", main = "snorm: xi = 1.5") # Plot empirical density and compare with true density: hist(r, n = 25, probability = TRUE, border = "white", col = "steelblue4") x = seq(-4, 6, 0.1) lines(x, dsnorm(x = x, mean = 1, sd = 0.5, xi = 1.5)) # Plot df and compare with true df: plot(sort(r), (1:1000/1000), main = "Probability", col = "steelblue4") lines(x, psnorm(x, mean = 1, sd = 0.5, xi = 1.5)) # Compute quantiles: qsnorm(psnorm(q = -4:6, mean = 1, sd = 0.5, xi = 1.5), mean = 1, sd = 0.5, xi = 1.5)