Description Usage Arguments Details Value Author(s) References Examples
The functions SIMPLEX()
define the simplex distribution, a two parameter distribution, for a gamlss.family
object to be used in GAMLSS fitting
using the function gamlss()
. SIMPLEX()
has mean equal to the parameter mu
and sigma
as scale parameter, see below.
The functions dSIMPLEX
, pSIMPLEX
qSIMPLEX
and rSIMPLEX
define the density, comulative distribution function, quantile function and random
generation for the simplex distribution.
1 2 3 4 5 |
mu.link |
the |
sigma.link |
the |
x,q |
vector of quantiles |
mu |
vector of location parameter values |
sigma |
vector of scale parameter values |
log, log.p |
logical; if TRUE, probabilities p are given as log(p). |
lower.tail |
logical; if TRUE (default), probabilities are P[X <= x], otherwise, P[X > x] |
p |
vector of probabilities. |
n |
number of observations. If |
The simplex distribution is given as
f(y|mu,sigma) = 1/(2*pi*sigma^2*(y*(1-y))^3)^(1/2) exp(-(1/(2*sigma^2)) * ((y-mu)^2)/(y*(1-y)*mu^2*(1-mu)^2))
for y in (0,1), 0<μ<1 and σ>0.
SIMPLEX()
returns a gamlss.family
object which can be used to fit a simplex distribution in the gamlss()
function.
Bob Rigby, Mikis Stasinopoulos and Fernanda De Bastiani
Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized additive models for location, scale and shape,(with discussion), Appl. Statist., 54, part 3, pp 507-554.
Stasinopoulos D. M. Rigby R.A. (2007) Generalized additive models for location scale and shape (GAMLSS) in R. Journal of Statistical Software, Vol. 23, Issue 7, Dec 2007, http://www.jstatsoft.org/v23/i07.
Stasinopoulos D. M., Rigby R.A., Heller G., Voudouris V., and De Bastiani F., (2017) Flexible Regression and Smoothing: Using GAMLSS in R, Chapman and Hall/CRC.
1 2 3 4 5 | SIMPLEX()# default links for the simplex distribution
plot(function(y) dSIMPLEX(y, mu=.5 ,sigma=1), 0.001, .999)
plot(function(y) pSIMPLEX(y, mu=.5 ,sigma=1), 0.001, 0.999)
plot(function(y) qSIMPLEX(y, mu=.5 ,sigma=1), 0.001, 0.999)
plot(function(y) qSIMPLEX(y, mu=.5 ,sigma=1, lower.tail=FALSE), 0.001, .999)
|
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