gamlss.dist (version 6.1-1)

RG: The Reverse Gumbel distribution for fitting a GAMLSS

Description

The function RG defines the reverse Gumbel distribution, a two parameter distribution, for a gamlss.family object to be used in GAMLSS fitting using the function gamlss(). The functions dRG, pRG, qRG and rRG define the density, distribution function, quantile function and random generation for the specific parameterization of the reverse Gumbel distribution.

Usage

RG(mu.link = "identity", sigma.link = "log")
dRG(x, mu = 0, sigma = 1, log = FALSE)
pRG(q, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
qRG(p, mu = 0, sigma = 1, lower.tail = TRUE, log.p = FALSE)
rRG(n, mu = 0, sigma = 1)

Value

RG() returns a gamlss.family object which can be used to fit a Gumbel distribution in the gamlss() function.

dRG() gives the density, pGU() gives the distribution function, qRG() gives the quantile function, and rRG()

generates random deviates.

Arguments

mu.link

Defines the mu.link, with "identity" link as the default for the mu parameter. other available link is "inverse", "log" and "own"

sigma.link

Defines the sigma.link, with "log" link as the default for the sigma parameter, other links are the "inverse", "identity" and "own"

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 length(n) > 1, the length is taken to be the number required

Author

Mikis Stasinopoulos, Bob Rigby and Calliope Akantziliotou

Details

The specific parameterization of the reverse Gumbel distribution used in RG is $$f(y|\mu,\sigma)= \frac{1}{\sigma} \hspace{1mm} \exp\left\{-\left(\frac{y-\mu}{\sigma}\right)-\exp\left[-\left(\frac{y-\mu)}{\sigma}\right)\right]\right\}$$ for \(y=(-\infty,\infty)\), \(\mu=(-\infty,+\infty)\) and \(\sigma>0\) see pp. 370-371 of Rigby et al. (2019).

References

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.

Rigby, R. A., Stasinopoulos, D. M., Heller, G. Z., and De Bastiani, F. (2019) Distributions for modeling location, scale, and shape: Using GAMLSS in R, Chapman and Hall/CRC, tools:::Rd_expr_doi("10.1201/9780429298547"). An older version can be found in https://www.gamlss.com/.

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, tools:::Rd_expr_doi("10.18637/jss.v023.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. tools:::Rd_expr_doi("10.1201/b21973")

(see also https://www.gamlss.com/).

See Also

gamlss.family

Examples

plot(function(x) dRG(x, mu=0,sigma=1), -3, 6, 
 main = "{Reverse Gumbel  density mu=0,sigma=1}")
RG()# gives information about the default links for the Gumbel distribution      
dat<-rRG(100, mu=10, sigma=2) # generates 100 random observations 
# library(gamlss)
# gamlss(dat~1,family=RG) # fits a constant for each parameter mu and sigma