The function takes a GAMLSS fitted model and bootstrap it to create B bootstrap samples.
NonParametricBoot(obj, data = NULL, B = 100, newdata = NULL)BayesianBoot(obj, data = NULL, B = 100, newdata = NULL)
An Bayesian.boot object with elements
the bootstrap samples
the required number of boostraps
the actual number of boostraps
the distribution parameters
the fitted coeficients from the GAMLSS model
the call from the GAMLSS model
a gamlss fitted model
a data frame
the number of boostrap samples
new data for predictAll()
Mikis Stasinopoulos, d.stasinopoulos@londonmet.ac.uk
The function NonParametric() perform non-parametric bootstraping, Efron and Tibshirani (1993) while the function BayesianBoot() perform Bayesian bootstrap
Rubin (1981)
Efron, B. and Tibshirani, R, (1993), An introduction to the bootstrap, Chapman and Hall New York, Monographs on statistics and applied probability, vulume 57.
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, tools:::Rd_expr_doi("10.1201/9780429298547"). An older version can be found in https://www.gamlss.com/.
Rubin, D. B. (1981) the bayesian bootstrap. The annals of statistics, pp. 130-134.
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, tools:::Rd_expr_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. tools:::Rd_expr_doi("10.1201/b21973")
Stasinopoulos, M. D., Rigby, R. A., and De Bastiani F., (2018) GAMLSS: a distributional regression approach, Statistical Modelling, Vol. 18, pp, 248-273, SAGE Publications Sage India: New Delhi, India. tools:::Rd_expr_doi("10.1177/1471082X18759144")
(see also https://www.gamlss.com/).
gamlss
m1 <-gamlss(y~x+qrt, data=aids, family=NBI())
registerDoParallel(cores = 2)
B1 <- BayesianBoot(m1)
summary(B1)
plot(B1)
B2 <- NonParametricBoot(m1)
stopImplicitCluster()
summary(B2)
plot(B2)
Run the code above in your browser using DataLab