gamlss-package {gamlss} | R Documentation |
Generalized Additive Models for Location Scale and Shape
Description
Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables.
Details
The DESCRIPTION file:
Package: | gamlss |
Title: | Generalized Additive Models for Location Scale and Shape |
Version: | 5.4-22 |
Date: | 2024-03-18 |
Authors@R: | c(person("Mikis", "Stasinopoulos", role = c("aut", "cre", "cph"), email = "d.stasinopoulos@gre.ac.uk", comment = c(ORCID = "0000-0003-2407-5704")), person("Robert", "Rigby", role = "aut", email = "r.rigby@gre.ac.uk", comment = c(ORCID = "0000-0003-3853-1707")), person("Vlasios", "Voudouris", role = "ctb"), person("Calliope", "Akantziliotou", role = "ctb"), person("Marco", "Enea", role = "ctb"), person("Daniil", "Kiose", role = "ctb", comment = c(ORCID = "0000-0002-3596-5748")), person("Achim", "Zeileis", role = "ctb", email = "Achim.Zeileis@R-project.org", comment = c(ORCID = "0000-0003-0918-3766")) ) |
Description: | Functions for fitting the Generalized Additive Models for Location Scale and Shape introduced by Rigby and Stasinopoulos (2005), <doi:10.1111/j.1467-9876.2005.00510.x>. The models use a distributional regression approach where all the parameters of the conditional distribution of the response variable are modelled using explanatory variables. |
License: | GPL-2 | GPL-3 |
URL: | https://www.gamlss.com/ |
BugReports: | https://github.com/gamlss-dev/gamlss/issues |
Depends: | R (>= 3.3.0), graphics, stats, splines, utils, grDevices, gamlss.data (>= 5.0-0), gamlss.dist (>= 4.3.1), nlme, parallel |
Imports: | MASS, survival, methods |
Suggests: | distributions3 (>= 0.2.1) |
LazyLoad: | yes |
NeedsCompilation: | yes |
Author: | Mikis Stasinopoulos [aut, cre, cph] (<https://orcid.org/0000-0003-2407-5704>), Robert Rigby [aut] (<https://orcid.org/0000-0003-3853-1707>), Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb] (<https://orcid.org/0000-0002-3596-5748>), Achim Zeileis [ctb] (<https://orcid.org/0000-0003-0918-3766>) |
Maintainer: | Mikis Stasinopoulos <d.stasinopoulos@gre.ac.uk> |
Index of help topics:
.binom Lists used by GAMLSS IC Gives the GAIC for a GAMLSS Object LR.test Likelihood Ratio test for nested GAMLSS models Q.stats A function to calculate the Q-statistics Rsq Generalised (Pseudo) R-squared for GAMLSS models VC.test Vuong and Clarke tests acfResid ACF plot of the residuals additive.fit Implementing Backfitting in GAMLSS bfp Functions to fit fractional polynomials in GAMLSS bp Bucket plot calibration Calibrating centile curves centiles Plots the centile curves for a GAMLSS object centiles.com Comparing centiles from different GAMLSS models centiles.pred Creating predictive centiles values centiles.split Plots centile curves split by x for a GAMLSS object coef.gamlss Extract Model Coefficients in a GAMLSS fitted model cs Specify a Smoothing Cubic Spline Fit in a GAMLSS Formula deviance.gamlss Global Deviance of a GAMLSS model devianceIncr The global deviance increment dtop Detrended transformed Owen's plot edf Effective degrees of freedom from gamlss model find.hyper A function to select values of hyper-parameters in a GAMLSS model fitDist Fitting Different Parametric 'gamlss.family' Distributions. fitted.gamlss Extract Fitted Values For A GAMLSS Model fittedPlot Plots The Fitted Values of a GAMLSS Model formula.gamlss Extract the Model Formula in a GAMLSS fitted model gamlss Generalized Additive Models for Location Scale and Shape gamlss-package Generalized Additive Models for Location Scale and Shape gamlss.control Auxiliary for Controlling GAMLSS Fitting gamlss.cs Support for Function cs() and scs() gamlss.fp Support for Function fp() gamlss.lo Support for Function lo() gamlss.ps Support for Functions for smoothers gamlss.random Support for Functions random() and re() gamlss.scope Generate a Scope Argument for Stepwise GAMLSS gamlssML Maximum Likelihood estimation of a simple GAMLSS model gamlssVGD A Set of Functions for selecting Models using Validation or Test Data Sets and Cross Validation gen.likelihood A function to generate the likelihood function from a GAMLSS object getPEF Getting the partial effect function from a continuous term in a GAMLSS model getQuantile Getting the partial quantile function for a term getSmo Extracting Smoother information from a GAMLSS fitted object glim.control Auxiliary for Controlling the inner algorithm in a GAMLSS Fitting histDist This function plots the histogram and a fitted (GAMLSS family) distribution to a variable histSmo Density estimation using the Poisson trick lms A function to fit LMS curves for centile estimation lo Specify a loess fit in a GAMLSS formula loglogSurv Survival function plots for checking the tail behaviour of the data lpred Extract Linear Predictor Values and Standard Errors For A GAMLSS Model model.frame.gamlss Extract a model.frame, a model matrix or terms from a GAMLSS object for a given distributional parameter numeric.deriv An internal GAMLSS function for numerical derivatives par.plot A function to plot parallel plot for repeated measurement data pcat Reduction for the Levels of a Factor. pdf.plot Plots Probability Distribution Functions for GAMLSS Family plot.gamlss Plot Residual Diagnostics for an GAMLSS Object plot.histSmo A Plotting Function for density estimator object histSmo plot2way Function to plot two interaction in a GAMLSS model polyS Auxiliary support for the GAMLSS predict.gamlss Extract Predictor Values and Standard Errors For New Data In a GAMLSS Model print.gamlss Prints a GAMLSS fitted model prodist.gamlss Extracting Fitted or Predicted Probability Distributions from gamlss Models prof.dev Plotting the Profile Deviance for one of the Parameters in a GAMLSS model prof.term Plotting the Profile: deviance or information criterion for one of the terms (or hyper-parameters) in a GAMLSS model ps P-Splines Fits in a GAMLSS Formula quantSheets Quantile Sheets random Specify a random intercept model in a GAMLSS formula refit Refit a GAMLSS model residuals.gamlss Extract Residuals from GAMLSS model ri Specify ridge or lasso Regression within a GAMLSS Formula rqres.plot Creating and Plotting Randomized Quantile Residuals rvcov Robust Variance-Covariance matrix of the parameters from a fitted GAMLSS model stepGAIC Choose a model by GAIC in a Stepwise Algorithm summary.gamlss Summarizes a GAMLSS fitted model term.plot Plot regression terms for a specified parameter of a fitted GAMLSS object update.gamlss Update and Re-fit a GAMLSS Model wp Worm plot z.scores Z-scores for lms objects
Author(s)
Mikis Stasinopoulos [aut, cre, cph] (<https://orcid.org/0000-0003-2407-5704>), Robert Rigby [aut] (<https://orcid.org/0000-0003-3853-1707>), Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb] (<https://orcid.org/0000-0002-3596-5748>), Achim Zeileis [ctb] (<https://orcid.org/0000-0003-0918-3766>)
Maintainer: Mikis Stasinopoulos <d.stasinopoulos@gre.ac.uk>
References
Nelder, J. A. and Wedderburn, R. W. M. (1972). Generalized linear models. J. R. Statist. Soc. A., 135 370-384.
Hastie, T. J. and Tibshirani, R. J. (1990). Generalized Additive Models. Chapman and Hall, London.
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. 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, https://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.
(see also https://www.gamlss.com/).
See Also
Examples
data(abdom)
mod<-gamlss(y~pb(x),sigma.fo=~pb(x),family=BCT, data=abdom, method=mixed(1,20))
plot(mod)
rm(mod)