drop1_gevreg: Drop one possible covariate on GEV parameter
In pengyuwei94/evreg: Extreme value regression modelling

Description Usage Arguments Details Value

Drop a single term to either mu, sigma, and xi based on criterion Likelihood ratio test, AIC, or p value from Wald test

1 2	drop1_gevreg(fit, parameter = "mu", alpha = 0.05, criterion = "pvalue")

`fit`	An object of class `c("gev", "evreg")` returned from `gevreg` summarising the current model fit.
`parameter`	A specified parameter that needs to drop one covariate. Equals `"mu"` by default.
`alpha`	Significance level if criterion equals pvalue or LRT. Default value is 0.05.
`criterion`	Either based `LRT`(Likelihood ratio test), `AIC`, or `pvalue`(by Wald test). Default criterion is `pvalue`.

Non-zero components of inital value of xi may mean that the likelihood is zero at the starting values. This is because for xi not equal to zero, there is a constraint on the parameter space. To avoid this set all components of initial value of xi to 0, i.e. the Gumbel case.

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 dropitional components

`Input_fit`	The input object of the class `c("gev", "evreg")`.
`Note`	A message that tells if a covariate has been droped 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.
`droped_covariate`	A character vector shows droped covariate
`criterion_value`	criterion value for if both input model and output model are different.