| IC {gamlss} | R Documentation |
IC is a function to calculate the Generalised Akaike information criterion (GAIC) for a given penalty k for a fitted GAMLSS object.
The function AIC.gamlss is the method associated with a GAMLSS object of the generic function AIC.
The function GAIC is a synonymous of the function AIC.gamlss.
The function extractAIC is a the method associated a GAMLSS object of the generic function extractAIC and it is
mainly used in the stepAIC function.
The function Rsq compute a generalisation of the R-squared for not normal models.
IC(object, k = 2) ## S3 method for class 'gamlss' AIC(object, ..., k = 2, c = FALSE) GAIC(object, ..., k = 2, c = FALSE ) ## S3 method for class 'gamlss' extractAIC(fit, scale, k = 2, c = FALSE, ...)
object |
an gamlss fitted model |
fit |
an gamlss fitted model |
... |
allows several GAMLSS object to be compared using a GAIC |
k |
the penalty with default |
c |
whether the corrected AIC, i.e. AICc, should be used, note that it applies only when |
scale |
this argument is not used in gamlss |
The function IC returns the GAIC for given penalty k of the GAMLSS object.
The function AIC returns a matrix contains the df's and the GAIC's for given penalty k.
The function GAIC returns identical results to AIC.
The function extractAIC returns vector of length two with the degrees of freedom and the AIC criterion.
Mikis Stasinopoulos mikis.stasinopoulos@gamlss.org
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.
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, http://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 http://www.gamlss.org/).
data(abdom) mod1<-gamlss(y~pb(x),sigma.fo=~pb(x),family=BCT, data=abdom) IC(mod1) mod2<-gamlss(y~pb(x),sigma.fo=~x,family=BCT, data=abdom) AIC(mod1,mod2,k=3) GAIC(mod1,mod2,k=3) extractAIC(mod1,k=3) rm(mod1,mod2)