resid_wp: Worm plot using ggplot2

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

Worm plot using ggplot2

Description

The function produces worm plot of the residuals of a fitted model. A worm plot is a de-trended normal QQ-plot so departure from normality is highlighted.

The function plot_wp() it is similar to the gamlss package function wp() when the argument xvar is not used.

Usage

resid_wp(obj, resid, value = 3, points_col = "steelblue4", 
         poly_col = "darkred", 
         check_overlap = TRUE, title, ylim)

model_wp(obj, ..., title)  

resid_wp_wrap(obj, resid, value = 3, xvar = NULL, n_inter = 4, 
         points_col = "steelblue4", poly_col = "darkred", 
         alpha_bound = 0.1, check_overlap = TRUE, title, ylim)
         
model_wp_wrap(obj, ..., xvar = NULL, value = 3, n_inter = 4, 
         points_col = "steelblue4", alpha_bound = 0.1, 
         check_overlap = TRUE, ylim, title)         

Arguments

obj	a GAMLSS fitted object or any other fitted model where the resid() method works (preferably the residuals should be standardised or better normalised quantile residuals. Note for model_wp only gamlss object are accepted.)
resid	if object is missing this argument can be used to specify the residual vector (again it should a normalised quantile residual vector)
value	A cut off point to indicate large residuals, default is value=3
xvar	the x term for which the worm plot will be plotted against
n_inter	the number of intervals for continuous x-term
points_col	the color of the points in the plot
poly_col	the colour of the fitted polynomial in the plot
check_overlap	to check for overlap when plotting the observation numbers
alpha_bound	the transparency parameter for the coinfidence bound
title	required title
ylim	if the y limit should be different from the default max(y)+.1
...	extra GAMLSS models

Value

A worm plot is produced

Author(s)

Mikis Stasinopoulos, Bob Rigby and Fernanda De Bastiani

References

van Buuren and Fredriks M. (2001) Worm plot: simple diagnostic device for modelling growth reference curves. Statistics in Medicine, 20, 1259–1277

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

wp

Examples

data(abdom)
# with data
a<-gamlss(y~pb(x),sigma.fo=~pb(x,1),family=LO,data=abdom)
resid_wp(a)
resid_wp(resid=resid(a))

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