fitted_terms: Plotting fitted additive terms

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

Plotting fitted additive terms

Description

The function fitted_terms() is doing what the function term.plot() is doing for GAMLSS models but it uses ggplot2 package. The function pe_terms() is synonymous to fitted_terms() in the package gamlss.

Usage

fitted_terms(object, 
          what = c("mu", "sigma", "nu", "tau"), 
          parameter = NULL, data = NULL, terms = NULL,
          envir = environment(formula(object)), 
          partial = FALSE, rug = FALSE, rug.sides = "b", 
          rug.col = "gray",  alpha = 0.2, 
          ylim = c("common", "free"), xlabs = NULL, 
          ylabs = NULL, main = NULL, term.col = "darkred", 
          resid.col = "lightblue", resid.alpha = 0.8, 
          resid.size = 1, nrow = NULL, ncol = NULL, 
          plots.per.page = 9, one.by.one = FALSE, 
          surface.gam = FALSE, polys = NULL, 
          polys.scheme = "topo", col.ribbon = "darksalmon",
          col.shaded = "gray", ...)

pe_terms(object, 
          what = c("mu", "sigma", "nu", "tau"), 
          parameter = NULL, data = NULL, terms = NULL,
          envir = environment(formula(object)), 
          partial = FALSE, rug = FALSE, rug.sides = "b", 
          rug.col = "gray",  alpha = 0.2, 
          ylim = c("common", "free"), xlabs = NULL, 
          ylabs = NULL, main = NULL, term.col = "darkred", 
          resid.col = "lightblue", resid.alpha = 0.8, 
          resid.size = 1, nrow = NULL, ncol = NULL, 
          plots.per.page = 9, one.by.one = FALSE, 
          surface.gam = FALSE, polys = NULL, 
          polys.scheme = "topo", col.ribbon = "darksalmon",
          col.shaded = "gray", ...)          
          

Arguments

object	a gamlss fitted model
what	which distribution parameters, i.e. "mu"
parameter	which distribution parameters (equivalent to what)
data	data frame in which variables in object can be found
terms	which terms to plot (default NULL means all terms); a vector passed to lpred(..., type = "terms", terms =*)
envir	environment in which variables in object can be found
partial	logical; should partial residuals be plotted?
rug	add rug plots to the axes
rug.sides	which side the rug "b"=bottom
rug.col	the colour for the rug
alpha	the alpha for the shade
ylim	there are two options here a) "common" and b) "free". The "common"" option plots all figures with the same ylim range and therefore allows the viewer to check the relative contribution of each terms compate to the rest. In the ‘free’ option the limits are computed for each plot seperatly.
xlabs	the x label
ylabs	the y label
main	title NOT WORKING
term.col	the colour of the line for term
resid.col	the colour of the partial residuals
resid.alpha	The alpha for the partial residuals
resid.size	the size of the partial residuals
nrow	the number or rows in a mupliple plot
ncol	the number of rows in a mupliple plot
plots.per.page	the number of plots per page in multiple plots
one.by.one	whether to plot the terms one by one
surface.gam	whether to use surface plot if a ga() term is fitted
polys	for GRMF models
polys.scheme	The polygone information file for MRF models
col.ribbon	he colour of the ribbon
col.shaded	he colour of the shaded area
...	for extra argument

Value

A multiple 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

term.plot

Examples

data(aids)
a <- gamlss(y ~ pb(x) + qrt, data = aids, family = NBI)
fitted_terms(a, pages = 1)

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