gamlss: acfResid – R documentation

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acfResid

ACF plot of the residuals

Description

This plot display the ACF and PACF of the residuals of a gamlss or other fitted model (provided that they have been standardised appropriately. Is is approriate for time series data.

Usage

acfResid(obj = NULL, resid = NULL)

Arguments

`obj`	A gamlss model or othe fitted model where the resid() function applies exist
`resid`	if `obj` does not exist the argument here will be used

Details

The ACF abd PACF for the residuals r, squared residuals r^2, r^3 and r^4 are plotted

Value

The relevant plots are displayied

Author(s)

Mikis Stasinopoulos. Bob Rigby. Vlasios Voudouris and Majid Djennad

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.

Examples

library(datasets)
data(co2)
m1<- gamlss(co2~pb(as.numeric(time(co2)))+factor(cycle(co2)))
acfResid(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