gamlss() function.gamlss.family(object,...)
as.gamlss.family(object)
as.family(object)
## S3 method for class 'gamlss.family':
print(x,...)BCTBCTgamlss.family. This object is used to define the family in the gamlss() fit.gamlss function.
The following table display their names and their abbreviations in R. Note that the different distributions can be fitted
using their R abbreviations
(and optionally excluding the brackets) i.e. family=BI(), family=BI are equivalent.
BE() 2
Beta Binomial BB() 2
Beta one inflated BEOI() 3
Beta zero inflated BEZI() 3
Beta inflated BEINF() 4
Binomial BI() 1
Box-Cox Cole and Green BCCG() 3
Box-Cox Power Exponential BCPE() 4
Box-Cox-t BCT() 4
Delaport DEL() 3
Exponential EXP() 1
Exponential Gaussian exGAUS() 3
Exponential generalized Beta type 2 EGB2() 4
Gamma GA() 2
Generalized Beta type 1 GB1() 4
Generalized Beta type 2 GB2() 4
Generalized Gamma GG() 3
Generalized Inverse Gaussian GIG() 3
Generalized t GT() 4
Geometric GEOM() 1
Gumbel GU() 2
Inverse Gamma IGAMMA() 2
Inverse Gaussian IG() 2
Johnson's SU JSU() 4
Logarithmic LG() 1
Logistic LO() 2
log-Normal LOGNO() 2
log-Normal (Box-Cox) LNO() 3 (1 fixed)
Negative Binomial type I NBI() 2
Negative Binomial type II NBII() 2
Normal Exponential t NET() 4 (2 fixed)
Normal NO() 2
Normal Family NOF() 3 (1 fixed)
Pareto type 2 PARETO2() 2
Pareto type 2 original PARETO2o() 2
Power Exponential PE() 3
Power Exponential type 2 PE2() 3
Poison PO() 1
Poisson inverse Gaussian PIG() 2
Reverse generalized extreme RGE() 3
Reverse Gumbel RG() 2
Skew Power Exponential type 1 SEP1() 4
Skew Power Exponential type 2 SEP2() 4
Skew Power Exponential type 3 SEP3() 4
Skew Power Exponential type 4 SEP4() 4
Shash SHASH() 4
Shash original SHASHo() 4
Shash original 2 SHASH() 4
Sichel (original) SI() 3
Sichel (mu as the maen) SICHEL() 3
Skew t type 1 ST1() 3
Skew t type 2 ST2() 3
Skew t type 3 ST3() 3
Skew t type 4 ST4() 3
Skew t type 5 ST5() 3
t-distribution TF() 3
Waring WARING() 1
Weibull WEI() 2
Weibull(PH parameterization) WEI2() 2
Weibull (mu as mean) WEI3() 2
Yule YULE() 1
Zero adjusted binomial ZABI() 2
Zero inflated binomial ZIBI() 2
Zero adjusted logarithmic ZALG() 2
Zero inflated poisson ZIP() 2
Zero inf. poiss.(mu as mean) ZIP2() 2
Zero adjusted poisson ZAP() 2
Zero adjusted IG ZAIG() 2
}
Note that some of the distributions are in the package gamlss.dist.
The parameters of the distributions are in order, mu for location, sigma for scale (or dispersion),
and nu and tau for shape.
More specifically for the BCCG family mu is the median, sigma approximately the coefficient of variation, and nu the skewness parameter.
The parameters for BCPE distribution have the same interpretation with the extra fourth parameter tau modelling
the kurtosis of the distribution. The parameters for BCT have the same interpretation except that
$\sigma [(\tau/(\tau-2))^{0.5}]$ is
approximately the coefficient of variation.
All of the distribution in the above list are also provided with the corresponding d, p, q and r functions
for density (pdf), distribution function (cdf), quantile function and random generation function respectively, (see individual distribution for details).Stasinopoulos D. M., Rigby R.A. and Akantziliotou C. (2006) Instructions on how to use the GAMLSS package in R.
Accompanying documentation in the current GAMLSS help files, (see also
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,
BE,BB,BEINF,BI,LNO,BCT,
BCPE,BCCG,
GA,GU,JSU,IG,LO,
NBI,NBII,NO,PE,PO,
RG,PIG,TF,WEI,WEI2,
ZIPnormal<-NO(mu.link="log", sigma.link="log")
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