prof_term: Plotting the profile deviance of one fitted term

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

Plotting the profile deviance of one fitted term

Description

Plotting the profile deviance of a fitted term in GAMLSS

Usage

prof_term(model = NULL, criterion = c("GD", "GAIC"), 
      penalty = 2.5,  from = NULL, to = NULL, 
      step = NULL, length = 7, xlabel = NULL, plot = TRUE, 
      perc = 95, start.prev = TRUE, line.col = "darkgreen", 
      dash.line.type = 3, dash.line.size = 0.8, text.size = 5, title)

Arguments

model	a GAMLSS fitted model
criterion	whether Global deviance or GAIC
penalty	the penalty k for GAIC
from	start from
to	finish at
step	using step
length	if the step is left NULL then length is considered for evaluating the grid for the parameter. It has a default value of 11.
xlabel	if a x label is required
plot	whether tto plot the function
perc	what percentage confidence interval is required
start.prev	whether to start from the previous fitted model parameters values or not (default is TRUE)
line.col	the colour of the plotting line
dash.line.type	the type of verical dash line for CI's
dash.line.size	The size of the dash lines
text.size	the size of text
title	the title

Details

This function is the ggplot2 version of the original prof.term() function.

Value

creates a plot

Author(s)

Mikis Stasinopoulos

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

prof.term

Examples

data(aids)
# fitting a linear model
gamlss(y~x+qrt,family=NBI,data=aids)
# testing the linear beta parameter
mod<-quote(gamlss(y ~ offset(this * x) + qrt, data = aids, family = NBI))
prof_term(mod, from = .06, to=0.13)

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