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:
This package was not yet installed at build time.
Index: This package was not yet installed at build time.
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/).
See Also
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)