This site has moved!!
Please visit us at gamlss.org

gamlss.com

Centile plot.

Latest News (updated on 10-09-2010)

PhD opportunities in London Met

London Metropolitan University is currently advertising the availability of PhD scholarships (closing date the 1 of October). The following statistical themes are encouraged.

• Data Mining Models for the Flexible Modelling of the Location, Scale and Shape Parameters of a Response Distribution
• Comparison, Evaluation and Development of Stochastic Volatility Models
• Energy-Economy-Investment Modelling

  Please see the following pdf file for more details or URL http://www.londonmet.ac.uk/research/.

  The latest version of gamlss is now 4.0-3. The following are new features:

  • The following new distributions have been added to the gamlss.dist package.
    • LG : the logarithmic distribution
    • ZAP : zero altered (or Hurdle) poisson distribution
    • ZALG : zero altered logarithmic
    • ZINBI : zero inflated negative binomial
    • ZANBI : zero altered negative binomial
    • ZIPIG : zero inflated Poisson inverse Gaussian distribution
    • ZABI : zero altered binomial
    • ZIBI : Zero inflated binomial
    • ZABB : zero altered beta binomial
    • ZIBB : zero inflated beta binomial
    • BEINF0 : beta inflated at zero (a different parametrization of Raydonal Ospina' BEZI )
    • BEINF1 : beta inflated at one (a different parametrization of Raydonal Ospina' BEOI )

  • The package gamlss.demo is introduced for teaching purpose. It relies on the the R package rpanel of Bowman, Bowman, Gibson and Crawford (2008).

    All the gamlss gamlss.family distributions can be displayed to examine how the different values of the parameters effect the shape of the distribution. All demo distribution function can be called using demo.NAME() where NAME is the gamlss.family name. For example demo.BCT() will produce a plot of the BCT distribution.

    The package gamlss.demo also contains the following functions contributed by Paul Eilers and Brian Marx to demonstrate penalised beta splines:

    • demo.BetaSplines()
    • demo.discreteSmo()
    • demo.histSmo()
    • demo.interpolateSmo()
    • demo.PenSplines()

  • The stepGAICAll() is introduced in package gamlss implementing a specific strategy for the selection of terms of all the distribution parameters. (The function needs more testing, volunteers are welcome).

  • The function LR.test() for using the likelihood ratio test for nested GAMLSS models is introduced in the gamlss package.

  • add1() and drop1() can be used now for gamlss objects. drop1() is useful for testing the significance of terms in the model.

  • The definition of right truncation in discrete distributions has changed in the package gamlss.tr . For example right truncation at 14, excludes 14 as a possible value.

  • The function gamlssNP() has being modified by Michael Höhle to prevent situations where the likelihood calculation produced NA's.

  • The additive function pvc() is introduced in the gamlss package for fitting penalised beta splines varying coefficient models

  • The function histDist() in package gamlss has an extra "data" argument now.

  • The function stepTGD() in package gamlss for selection of terms for a distribution parameter using a test data set. (The function needs more testing and volunteers are welcome).
  A short course of GAMLSS will run at the Medical School of the University of Athens on the 1st, 3rd and 7th of June. The notes for the Athens short course can be downloaded here .

  You can find the gamlss packages reference card here.

  • Return to the top
  • Return to the main page

  What is GAMLSS

  Generalized Additive Models for Location, Scale and Shape (GAMLSS) are (semi) parametric regression type models. They are parametric, in that they require a parametric distribution assumption for the response variable, and "semi" in the sense that the modelling of the parameters of the distribution, as functions of explanatory variables, may involve using non-parametric smoothing functions.

  GAMLSS were introduced by Rigby and Stasinopoulos (2001, 2005) and Akantziliotou et al. (2002) as a way of overcoming some of the limitations associated with the popular Generalized Linear Models (GLM) and Generalized Additive Models (GAM), Nelder and Weddeburn (1972) and Hastie and Tibshirani (1990) respectively.

  In GAMLSS the exponential family distribution assumption for the response variable, y, is relaxed and replaced by a general distribution family, including highly skew and/or kurtotic continuous and discrete distributions. The systematic part of the model is expanded to allow modelling not only the mean (or location) but all the parameters of the distribution of y as linear and/or nonlinear parametric and/or additive non-parametric functions of explanatory variables and/or random effects.

  Hence GAMLSS is especially suited to modelling a response variable which does not follow an exponential family distribution, (eg. leptokurtic or platykurtic and/or positive or negative skew response data, or overdispersed counts) or which exhibit heterogeneity (eg. where the scale or shape of the distribution of the response variable changes with explanatory variables(s)).

  How to use GAMLSS

  The GAMLSS framework of statistical modelling is implemented in a series of packages in R, (R Development Core Team, 2007), a free software, see URL http://www.R-project.org. The packages can be downloaded from the R library, CRAN, or from here (especially for the newer and possibly untested versions).

  For new users of GAMLSS we recommend the second edition of the manual . For GAMLSS in action look at the paper published in the Journal of Statistical Software. For more examples and other topics look at the short course booklet given at the Utrecht short course.

  What distributions can be used

  The form of the distribution assumed for the response variable y, is very general. There are around 50 different distributions available in the current implementation of GAMLSS. This table displays their names and their abbreviations in R for most of them. New distributions can be added easily. Truncated versions of these distributions can be used using the package gamlss.tr. Censored (or interval) response variables can be used using the package gamlss.cens .

  What additive terms can be used

  There are several additive terms available in the current GAMLSS implementation. These include cubic splines, cs(), varying coefficient, vc(), penalized splines, ps(), loess lo(), fractional polynomials, fp(), power polynomials, pp(), random effects random() and ra() and non-linear terms, nl().

  • Return to the top

  Why should I use GAMLSS

  If your response variable is count (discrete) data it is very likely that the Poisson distribution will not fit well. GAMLSS provides a variety of discrete distributions (including the negative binomial) that you can try out. The dispersion parameter can be also modelled as a function of explanatory variables.

  For continuous response variables GAMLSS provides a variety of different distributions some of which could deal with skewness, some with kurtosis and some with both skewness and kurtosis. For situations where extreme outliers exist in the response variable some of the distribution possess robust properties. For reasonably large number of observations, say more that 1000, the probability is that the exponential family distributions available within the generalized linear model framework will not adequately fit the data.

  For centile estimation the WHO Multicentre Growth Reference Study Group have recommended gamlss and the BCPE distributions for the construction of the WHO Child Growth Standards.

  • Return to the top

  How to learn more about GAMLSS

  The original manual, now in its second edition, provides information on how to use the R-package gamlss. For examples using the package gamlss(), the recent Journal of Statistical Software paper is suitable. For statisticians wanted to know more about the theory and the algorithms we recommend the Royal Statistical Society read paper.

  References

  Akantziliotou, K. Rigby, R. A. and Stasinopoulos, D. M. (2002) The R implementation of Generalized Additive Models for Location, Scale and Shape in Statistical modelling in Society: Proceedings of the 17th International Workshop on statistical modelling, ed: Stasinopoulos, M. and Touloumi, G., 75-83, Chania, Greece

  Hastie, T. J. and Tibshirani, R. J. (1990), Generalized Additive Models,Chapman and Hall, London.

  Nelder, J. A. and Wedderburn, R. W. M., (1972) Generalized linear models, J. R. Statist. Soc. A., 135, 370-384.

  Rigby, R. A. and Stasinopoulos, D. M. (2001), The GAMLSS project: a flexible approach to statistical modelling, in :New Trends in Statistical Modelling: Proceedings of the 16th International Workshop on Statistical Modelling, ed:Klein, B. and Korsholm, L, 249-256, Odense, Denmark

  Rigby, R. A. and Stasinopoulos D. M. (2005). Generalized Additive Models for Location, Scale and Shape, (with discussion). Appl. Statist., 54, pp 507-554.

  R Development Core Team (2007). R: A Language and Environment for Statistical Computing. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0, URL http://www.R-project.org .

  • Return to the top