edf: Effective degrees of freedom from gamlss model

Description Usage Arguments Value Note Author(s) References See Also Examples

View source: R/edf.R

Description

The functions edf() and edfAll() can be used to obtained the effective degrees of freedom for different additive terms for the distribution parameters in a gamlss model.

Usage

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edf(obj, what = c("mu", "sigma", "nu", "tau"),
    parameter= NULL, print = TRUE, ...)
edfAll(obj, ...)

Arguments

obj

A gamlss fitted model

what

which of the four parameters mu, sigma, nu or tau.

parameter

equivalent to what

print

whether to print the label

...

for extra arguments

Value

The function edfAll() re turns a list of edf for all the fitted parameters. The function edf() a vector of edf.

Note

The edf given are the ones fitted in the backfitting so the usually contained (depending on the additive term) the contatnt and the linear part.

Author(s)

Mikis Stasinopoulos d.stasinopoulos@londonmet.ac.uk

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.

(see also https://www.gamlss.com/).

See Also

gamlss

Examples

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library(gamlss.data)
data(usair)
m1<- gamlss(y~pb(x1)+pb(x2)+pb(x6), data=usair)
edfAll(m1)
edf(m1)

Example output

Loading required package: splines
Loading required package: gamlss.data
Loading required package: gamlss.dist
Loading required package: MASS
Loading required package: nlme
Loading required package: parallel
 **********   GAMLSS Version 5.0-2  ********** 
For more on GAMLSS look at http://www.gamlss.org/
Type gamlssNews() to see new features/changes/bug fixes.

GAMLSS-RS iteration 1: Global Deviance = 340.3973 
GAMLSS-RS iteration 2: Global Deviance = 340.3971 
$mu
  pb(x1)   pb(x2)   pb(x6) 
2.562967 2.000000 2.000000 

$sigma
numeric(0)

Effective df for mu model 
  pb(x1)   pb(x2)   pb(x6) 
2.562967 2.000000 2.000000 

gamlss documentation built on March 31, 2021, 5:10 p.m.