This functions plots the profile deviance of one of the (four) parameters in a GAMLSS model. It can be used if one
of the parameters mu
, sigma
, nu
or tau
is a constant (not a function of explanatory variables) to obtain
a profile confidence intervals.
prof.dev(object, which = NULL, min = NULL, max = NULL,
step = NULL, length = 7, startlastfit = TRUE,
plot = TRUE, perc = 95, col="darkgreen")
Return a profile plot (if the argument plot=TRUE
) and an ProfLikelihood.gamlss
object if saved. The object contains:
the values at the grid where the parameter was evaluated
the function which approximates the points using splines
the minimum values in the grid
te maximum values in the grid
the value of the parameter maximising the Profile deviance (or GAIC)
the profile confidence interval (if global deviance is used)
which criterion was used
A fitted GAMLSS model
which parameter to get the profile deviance e.g. which="tau"
the minimum value for the parameter e.g. min=1
the maximum value for the parameter e.g. max=20
how often to evaluate the global deviance (defines the step length of the grid for the parameter) e.g. step=1
the length if step is not set, default equal 7
whether to start fitting from the last fit or not, default value is startlastfit=TRUE
whether to plot, plot=TRUE
or save the results, plot=FALSE
what % confidence interval is required
The colour of the profile line
Calliope Akantziliotou, Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk and Bob Rigby
A dense grid (i.e. small step) evaluation of the global deviance can take a long time, so start with a sparse grid (i.e. large step) and decrease gradually the step length for more accuracy.
This function can be use to provide likelihood based confidence intervals for a parameter for which a constant model (i.e. no explanatory model) is fitted and
consequently for checking the adequacy of a particular values of the parameter. This can be used to check the adequacy of one distribution (e.g. Box-Cox Cole and Green)
nested within another (e.g. Box-Cox power exponential). For example one can test whether a Box-Cox Cole and Green (Box-Cox-normal) distribution
or a Box-Cox power exponential is appropriate by plotting the profile of the parameter tau
.
A profile deviance showing support for tau=2
indicates adequacy of the Box-Cox Cole and Green (i.e. Box-Cox normal) distribution.
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.
(see also https://www.gamlss.com/).
gamlss
, prof.term
if (FALSE) {
data(abdom)
h<-gamlss(y~pb(x), sigma.formula=~pb(x), family=BCT, data=abdom)
prof.dev(h,"nu",min=-2.000,max=2)
rm(h)}
Run the code above in your browser using DataLab