gamlss.ggplots (version 2.1-12)

boot_coef: 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, ...)

Value

Greates a ggplot object

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

Author

Mikis Stasinopoulos

Details

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

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

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