gamlss: edf – R documentation

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edf

Effective degrees of freedom from gamlss model

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

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/).

Examples

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

gamlss

Generalised Additive Models for Location Scale and Shape

v5.3-4

GPL-2 | GPL-3

Authors

Mikis Stasinopoulos [aut, cre, cph], Bob Rigby [aut], Vlasios Voudouris [ctb], Calliope Akantziliotou [ctb], Marco Enea [ctb], Daniil Kiose [ctb]

Initial release

2021-03-31

edf