forward_AIC: AIC-based Forward Selection on GEV Parameter
In pengyuwei94/evreg: Extreme value regression modelling

Description Usage Arguments Details Value Examples

Significance controlled variable selection selects variables in either mu, sigma, and xi with forward direction based on AIC.

forward_AIC(fit, do_mu = TRUE, do_sigma = FALSE, do_xi = FALSE)

forward_AIC_mu(fit)

forward_AIC_sigma(fit)

forward_AIC_xi(fit)

`fit`	An object of class `c("gev", "evreg")` returned from `gevreg` summarising the current model fit.
`do_mu`	do forward selection on mu if `do_mu` equals TRUE. Default is TRUE.
`do_sigma`	do forward selection on sigma if `do_sigma` equals TRUE. Default is FALSE.
`do_xi`	do forward selection on xi if `do_xi` equals TRUE. Default is FALSE.

Add details.

An object (a list) of class c("gev", "evreg") summarising the new model fit (which may be the same as fit) and containing the following additional components

`Input_fit`	The input object of the class `c("gev", "evreg")`.
`Note`	A message that tells if a covariate has been added or not.
`Output_fit`	A list that contains formulae for the parameter, and the output object of the class `c("gev", "evreg")` if the output fit is different from the input fit.
`added_covariate`	A character vector shows added covariates
`AIC`	AIC values for both input model and output model if two models are different.

### Fremantle sea levels

f0 <- gevreg(SeaLevel, data = evreg::fremantle[,-1])
forward_AIC_mu(f0)


### Annual Maximum and Minimum Temperature

P0 <- gevreg(y = TMX1, data = PORTw[, -1])
forward_AIC(P0)