R: Computational Intensive Functions within GAMLSS

gamlss.foreach-package {gamlss.foreach}

R Documentation

Computational Intensive Functions within GAMLSS

Description

This package is intended for functions needed parallel computations provided by the package foreach.

At the moment the following functions exist:

centiles.boot(), which is designed get bootstrap confidence intervals for centile curves

fitRolling(), rolling regression which is common in time series analysis when one step ahead forecasts is required.

fitPCR(), for univariate principal component regression. I

Details

The DESCRIPTION file:

Package:	gamlss.foreach
Type:	Package
Title:	Parallel Computations for Distributional Regression
Version:	1.1-6
Date:	2022-08-28
Author:	Mikis Stasinopoulos [aut, cre, cph], Bob Rigby [aut], Fernanda De Bastiani [aut]
Description:	Computational intensive calculations for Generalized Additive Models for Location Scale and Shape, <doi:10.1111/j.1467-9876.2005.00510.x>.
Maintainer:	Mikis Stasinopoulos <d.stasinopoulos@londonmet.ac.uk>
LazyLoad:	yes
Depends:	R (>= 2.2.1), gamlss, foreach, doParallel, methods
Imports:	gamlss.data, gamlss.dist, glmnet
License:	GPL-2 \| GPL-3
URL:	https://www.gamlss.com/
NeedsCompilation:	no
Packaged:	2021-03-04 15:27:15 UTC; dimitriosstasinopoulos

Index of help topics:

BayesianBoot            Non parametric and Bayesian Bootstrapping for
                        GAMLSS models
centiles.boot           Bootstrapping centiles curves estimated using
                        GAMLSS
fitPCR                  Function to fit simple Principal Component
                        Regression.
fitRolling              Function to Fit Rolling Regression in gamlss
fitted.PCR              Methods for PCR objects
gamlss.foreach-package
                        Computational Intensive Functions within GAMLSS
pc                      Functions to Fit Principal Component Regression
                        in GAMLSS
which.Data.Corr         Detecting Hight Pair-Wise Correlations in Data

Author(s)

Mikis Stasinopoulos, d.stasinopoulos@londonmet.ac.uk,and Bob Rigby r.rigby@londonmet.ac.uk

Maintainer: Mikis Stasinopoulos, d.stasinopoulos@londonmet.ac.uk

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, 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, 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. doi:10.1201/b21973

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

Examples


library(gamlss.foreach)
# fixed degrees of freedom
cl <- makePSOCKcluster(2)
registerDoParallel(2)
data(db)
nage <- with(db, age^0.33)
ndb <- data.frame(db, nage)
m1 <- gamlss(head~cs(nage, 12), sigma.fo=~cs(nage,4), nu.fo=~nage, 
             tau.fo=~nage, family=BCT, data=ndb)
test1 <- centiles.boot(m1, xname="nage", xvalues=seq(0.01,20,0.2),B=10, power=0.33)
test1
plot(test1)
# degrees of freedom varying
m2 <- gamlss(head~pb(nage), sigma.fo=~pb(nage), nu.fo=~pb(nage), 
             tau.fo=~pb(nage), family=BCT, data=ndb)
test2 <- centiles.boot(m2, xname="nage", xvalues=seq(0.01,20,0.2),B=10, power=0.33)
stopImplicitCluster()
test2
plot(test2)