fitted_cdf: Plotting Cumulative Distribution Functions (cdf's) for...

In gamlss.ggplots: Plotting Generalised Additive Model for Location, Scale and Shape

Plotting Cumulative Distribution Functions (cdf's) for GAMLSS,

Description

The function family_cdf() takes a GAMLSS family distribution and plots different pdf's according to the specified parameters.

The function fitted_cdf() takes a gamlss fitted object and plots the fitted distributions for specified observations.

The function fitted_cdf_data() is similat to fitted_cdf() but also adds the data points as gray vertical lines.

The function predict_pdf() (NOT IMPLEMENTED YET) takes a fitted object and test data and plots the predictive cdf's.

Usage

fitted_cdf(model, obs, title, from = 0, to = 10, no.points = 201, 
          alpha = 1, size.line = 1.2, 
          col.fill = hcl.colors(lobs, palette = "viridis"), 
          size.seqment = 1.5, size.point = 1, 
          plot.line = TRUE, size.line.disc = 0.2, lower.tail = TRUE, ...)

fitted_cdf_data(model, obs, from, to, ...)

predict_cdf(model, newdata, title, from = 0, to = 10, no.points = 201, 
          alpha = 0.4, size.line = 1.2, 
          col.fill = hcl.colors(lobs, palette = "viridis"), 
          size.seqment = 1.5, plot.point = TRUE, size.point = 1, 
          plot.line = TRUE, size.line.disc = 0.2, lower.tail = TRUE, ...)

family_cdf(family = NO(), mu = NULL, sigma = NULL, nu = NULL, 
         tau = NULL, title, from = 0, to = 10, no.points = 201, 
         alpha = 0.4, size.line = 1.2, col.fill = hcl.colors(lobs, 
         palette = "viridis"), size.seqment = 1.5, plot.point = TRUE,  
         size.point = 1, plot.line = TRUE, lower.tail = TRUE, ...)

Arguments

family	A GAMLSS family
model	A GAMLSS fitted model
obs	observations to plot fitted distributions
newdata	for test data
mu	the mu parameter value(s)
sigma	the sigma parameter value(s)
nu	the nu parameter value(s)
tau	the tau parameter value(s)
title	a diferent title for the default
from	minimum value for the response
to	maximum value for the response
no.points	number of points (relevant for continuous responses)
alpha	trasparency factor
col.fill	the colour pallet default is hcl.colors(lobs, palette="viridis")
size.seqment	for discrete responses the size of the bars
plot.point	for discrete responses whether to put poits on the top of the bars
size.point	for discrete responses
plot.line	for discrete responses whether to joint the bars with lines
size.line.disc	for discrete responses the size of the joining lines
size.line	The size of the lines
lower.tail	if TRUE cdf is plotted if FALSE the survival function
...	for extra argumnets

Details

The functions family_cdf(), fitted_cdf(), and predict_cdf() are function to plot cdf's for a gamlss.family, fitted gamlss model or predictive gamlss model, respectively.

For discrete fitted distributions maybe increase the value of alpha for clearer plot.

For binomial type of data (discrete response with upper limit) the function family_cdf() takes the argument to as the binomial denominator, For fitted model with binomial type responses the function fitted_cdf() takes the binomial denominator form the fitted model and set the argument to to the maximum of those binomial denominators.

Value

Creates a plot

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.

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

See Also

gamlss

Examples

# function fitted_cdf
# continuous variabe
a1 <- gamlss(y~pb(x),sigma.fo=~pb(x), data=abdom, family=LO)
fitted_cdf(a1, obs=c(10,15,20), from=30, to=100)
fitted_cdf(a1, obs=c(10,15,20), from=30, to=100, lower.tail=FALSE)
# count data
p1 <- gamlss(y~pb(x)+qrt, data=aids, family=NBI)
fitted_cdf(p1, obs=c(10:15), from=10, to=130, alpha=.9)
fitted_cdf(p1, obs=c(10:15), from=10, to=130, alpha=.9, lower.tail=FALSE)
# binomial type
h<-gamlss(y~ward+loglos+year, sigma.formula=~year+ward, family=BB, data=aep) 
fitted_cdf(h, obs=c(10:15),  alpha=.9)
fitted_cdf(h, obs=c(10:15),  alpha=.9, lower.tail=FALSE)
###################################
# function predict_pdf
predict_cdf(a1, newdata=abdom[c(10,15,20), ], from=30, to=100)
predict_cdf(a1, newdata=abdom[10:20, ], from=30, to=100, lower.tail=FALSE)
# count data
predict_cdf(p1, newdata=aids[10:15, ], from=10, to=150)
predict_cdf(p1, newdata=aids[10:15, ], from=10, to=150, lower.tail=FALSE)
# binomial
predict_cdf(h, newdata=aep[10:15, ], from=0, to=20)
predict_cdf(h, newdata=aep[10:15, ], from=0, to=20, lower.tail=FALSE)
###################################
# function family_cdf
# continuous
family_cdf(from=-5,to=5, mu=0, sigma=c(.5,1,2))
family_cdf(from=-5,to=5, mu=0, sigma=c(.5,1,2), lower.tail=FALSE)
# count data 
family_cdf(NBI, to=15, mu=1, sigma=c(.5,1,2), alpha=.9, size.seqment = 3)
family_cdf(NBI, to=15, mu=1, sigma=c(.5,1,2), alpha=.9, size.seqment = 3, lower.tail=FALSE)
# binomial type
family_cdf(BB, to=15, mu=.5, sigma=c(.5,1,2),  alpha=.9, , size.seqment = 3)
family_cdf(BB, to=15, mu=.5, sigma=c(.5,1,2),  alpha=.9, , size.seqment = 3, lower.tail=FALSE)

gamlss.ggplots documentation built on Sept. 3, 2023, 5:08 p.m.