fitted_devianceIncr: Plotting the deviance increment of GAMLSS

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

Plotting the deviance increment of GAMLSS

Description

There are two plotting function here:

i) fitted_devianceIncr() plots the fitted model deviance components. This is useful for identifying observations with unusual y-values (given the current fitted distribution).

iii) model_devianceIncr_diff plots the difference of deviance increments from two fitted GAMLSS model. This function is useful if the GAIC and the residuals contradict each other. For example the GAIC is better for model 1 but the residuals look lot better for model 2. This can happens if the two distributions are better suited to fit different parts of the response distribution i.e. one model fits the center better but the other fits the tail better.

Usage

fitted_devianceIncr(obj, plot = TRUE, title, quan.val = 0.99,
         annotate = TRUE, line.col = "steelblue4", 
         point.col = "darkblue", annot.col = "white",
         newdata = NULL)
         

model_devianceIncr_diff(model1, model2, against = "index", 
         tol = 20, data, newdata)

Arguments

obj	a GAMLSS fitted object
plot	whether to create just the plot or save also the values with high deviance increment
title	a tittle if needed it.
quan.val	The quantile values of the deviance increment from which the obsrevrvarion should be identify
annotate	whether to plot the quantile values above in the plot.
line.col	the colour of the line
point.col	the colour of the points
annot.col	the colour of the annotation for the deviance increment plot
model1	The first fitted GAMLSS model
model2	The second fitted GAMLSS model
against	you can plot the deviance increment an index, the response or an x-variable
tol	if the absolute value of deviance increment exceeds the tol the number of the observation is plotted
data	The data if can not be found from model1
newdata	evaluates the function in new data

Details

The functions are diagnostic tools to check unusual observations in the response.

Value

return a plot

Author(s)

Mikis Stasinopulos, Rober Rigby and Fernanda de Bastiani

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

gamlss

Examples

m1 <- gamlss(R~pb(Fl)+pb(A)+H+loc, data=rent, family=GA )
m2 <- gamlss(R~pb(Fl)+pb(A)+H+loc, data=rent, family=NO )
fitted_devianceIncr(m1)
model_devianceIncr_diff(m1,m2, against="Fl")

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