| histSmo_plot {gamlss.ggplots} | R Documentation |
Supporting histSmo()
Description
This function helps to plot density estimates created by the histSmo() function.
Usage
histSmo_plot(x, col_fill_bar = gray(0.5), col_bar = "pink",
col_line = "darkblue", width_line = 1, title, xlabel)
Arguments
x |
a |
col_fill_bar |
The fill colour of the bars |
col_bar |
the colour of the border of thebars |
col_line |
the colour of the lines |
width_line |
the width of the lines |
title |
title if needed |
xlabel |
x axis lable if needed. |
Details
This function supports histSmo().
Value
A plot
Author(s)
Mikis Stasinopulos, Rober Rigby and Fernanda de Bastiani
References
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
Examples
a1 <-histSmo(abdom$y)
gg1 <-histSmo_plot(a1)
gg1