boot_coef: Plotting Bootstrap Coefficients

In gamlss.ggplots: Plotting Functions for Generalized Additive Model for Location Scale and Shape

Plotting Bootstrap Coefficients

Description

The function boot_coef() plots in one or multiple pages the results from a boostrap generated by the function BayesianBoot(), NonParamatricBoot or nonpar_boot().

The function boot_coef_one() plots a single parameter.

Usage

boot_coef(x, terms = NULL, hist.col = "black", 
                hist.fill = "white", dens.fill = "#FF6666", 
                alpha = 0.2, nrow = NULL, ncol = NULL, 
                plots.per.page = 9, one.by.one = FALSE, title, ...)
                
boot_coef_one(x, par = 1, rug = TRUE, alpha = 0.2, hist.col = "black", 
                hist.fill = "white", line.col = "gray", 
                dens.fill = "#FF6666", title, ...)              

Arguments

x	a Bayesian.boot or NonParametric.boot object
terms	which terms to plot (default NULL means all terms
par	which parameter to plot
hist.col	colour of the border histogram
hist.fill	the colour of the histogram
dens.fill	the colour of the density estimate
alpha	transparity constant
nrow	how namy rows
ncol	how many columns
plots.per.page	the maxiimum plots per page
one.by.one	whether single plots
rug	whether rug is required for boot_coef_one()
line.col	the vertical line colour for boot_coef_one()
title	the title
...	for more argument

Details

The function plots in one ore multiple pages the results from a boostrap simulation

Value

Greates a ggplot object

Author(s)

Mikis Stasinopoulos

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. 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.

Stasinopoulos, M.D., Kneib, T., Klein, N., Mayr, A. and Heller, G.Z., (2024). Generalized Additive Models for Location, Scale and Shape: A Distributional Regression Approach, with Applications (Vol. 56). Cambridge University Press.

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

See Also

term.plot

Examples

data(aids)
a <- gamlss(y ~ pb(x) + qrt, data = aids, family = NBI)
registerDoParallel(cores = 2)
B1 <- BayesianBoot(a, B=100)
stopImplicitCluster()
boot_coef(B1)

gamlss.ggplots documentation built on May 29, 2024, 1:34 a.m.