backward_AIC: Backward Elimination on GEV Parameter based on AIC
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 backward direction based on AIC.

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

backward_AIC_mu(fit)

backward_AIC_sigma(fit)

backward_AIC_xi(fit)

`fit`	An object of class `c("gev", "evreg")` returned from `gevreg` summarising the current model fit.
`do_mu`	do backward selection on mu if `do_mu` equals TRUE. Default is TRUE.
`do_sigma`	do backward selection on sigma if `do_sigma` equals TRUE. Default is FALSE.
`do_xi`	do backward 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 covariates have been dropped 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.
`dropped_covariate`	A character vector shows dropped covariates
`AIC`	AIC values for both input model and output model if two models are different.

### Oxford and Worthing annual maximum temperatures

ow$year <- (ow$year - 1901) / (1980 - 1901)
ow1 <- gevreg(y = temp, data = ow[-3], mu = ~loc + year, sigma = ~loc,
xi = ~loc, sigmalink = identity)
backward_AIC_mu(ow1)


### Annual Maximum and Minimum Temperature

P6 <- gevreg(TMX1, data = PORTw[,-1], mu = ~MTMAX + AOindex + STDTMAX + STDMIN + MDTR)
P7 <- gevreg(TMX1, data = PORTw[,-1], sigma = ~MTMAX + STDTMAX + STDMIN + MDTR)
P8 <- gevreg(TMX1, data = PORTw[,-1], xi = ~MTMAX + STDTMAX + STDMIN + MDTR)
backward_AIC_mu(P6)
backward_AIC_sigma(P7)
backward_AIC_xi(P8)