y_hist: Histogram and density plot.

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

Histogram and density plot.

Description

The function y_hist() creates a histogram and a density plot for a continuous variable.

The functions y_acf() and y_pacf() plot the autocorrolation and partial autocorrolation functions for y.

The function y_dots() is design for long tail right skewed variables. It is a plot emphasising the right tail of the distribution for such variables.

Usage

y_hist(y, data, with.density = TRUE, hist.col = "black",
         hist.fill = "white", dens.fill = "#FF6666", 
         binwidth = (max(y)-min(y))/20,  from, to, title)
         
y_acf(x, data, title)

y_pacf(x, data, title)

y_dots(y, data, value=3, point.size = 2, point.col = "gray", 
          quantile = c(.10, .50, .90),
          line.col = c("black","red", "black"),
          line.type = c("dotted", "solid",  "dotted"),
          line.size = c(1,1,1), x.axis.col = "black", 
          x.axis.line.type = "solid", seed = 123, from, to, title)

Arguments

y, x	a continuous variable
data	where to find argument y
value	value to identify outliers i.e. for upper tail an outliers is if it is greater than Q_3+value*IQ
with.density	whether a density is required, default is TRUE
hist.col	the colour of lines of the histogram
hist.fill	the colour of the histogram
dens.fill	the color of the density plot
binwidth	the binwidth for the histogram
from	where to start the histogram (you may have to change binwidth)
to	where to finish the histogram (you may have to change binwidth)
point.size	the size of the points in y_dots
point.col	the colour of the points in y_dots
quantile	the quantile values to plot in y_dots, the default is 0.10, .50 and .90
line.col	the color of the vertical lines indicating the 0.10, .50 and .90 quantiles in y_dots
line.type	the type of the verical lines indicationg the 0.10, .50 and .90 quantiles in y_dots
line.size	the size of the verical lines indication the 0.10, .50 and .90 quantiles in y_dots
x.axis.col	the colour of the x-axis
x.axis.line.type	the type of the x-axix
seed	the seed to jitter the y
title	use this for a different title

Value

A ggplot is returned

Author(s)

Mikis Stasinopoulos, Bob 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

plot.ecdf

Examples

library(ggplot2)
y <- rBCT(1000, mu=3, sigma=.1, nu=-1, tau=5)
y_hist(y)
gg <- y_hist(y, with.dens=FALSE)
gg + stat_function(fun = dBCT, args=list(mu=3, sigma=.1,  nu=-1, tau=5), 
                 colour = "black")
gg + stat_function(fun = dBCT, args=list(mu=3, sigma=.1,  nu=-1, tau=5), 
                  geom = "area", alpha=0.5, fill="pink", color="black", n=301)
                  
y_acf(diff(EuStockMarkets[,1]))    

y_dots(rent$R)

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