plot.histSmo: A Plotting Function for density estimator object histSmo

Description

Plots the estimated density or its c.d.f function or its inverse cdf function

Usage

# S3 method for histSmo
plot(x, type = c("hist", "cdf", "invcdf"), ...)

Arguments

An histSmo object

type

Different plots: a histogram and density estimator, a cdf function or an inverse cdf function.

…

for further arguments

Value

returns the relevant plot

References

Eilers, P. (2003). A perfect smoother. Analytical Chemistry, 75: 3631-3636.

Eilers, P. H. C. and Marx, B. D. (1996). Flexible smoothing with B-splines and penalties (with comments and rejoinder). Statist. Sci, 11, 89-121.

Lindsey, J.K. (1997) Applying Generalized Linear Models. New York: Springer-Verlag. ISBN 0-387-98218-3

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.

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

Examples

Run this code

# NOT RUN {
Y <- rPARETO2(1000) 
m1<- histSmo(Y, lower=0, save=TRUE)
plot(m1)
plot(m1, "cdf")
plot(m1, "invcdf")
# }