add1_gevreg: Add one possible covariate on GEV parameter

In pengyuwei94/evreg: Extreme value regression modelling

Description

Add a single term to either mu, sigma, and xi based on criterion Likelihood ratio test, AIC, or the Wald test

Usage

add1_gevreg(fit, parameter = "mu", alpha = 0.05,
  criterion = "pvalue")

Arguments

fit	An object of class c("gev", "evreg") returned from gevreg summarising the current model fit.
parameter	A specified parameter that needs to add 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.

Details

When a new model is fitted in which an extra covariate is added, we use starting values based on the fit of the smaller model. The start value for the new added variable will be set to be zero.

Value

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 covariate
criterion_value	criterion value for if both input model and output model are different.

Examples

### Annual Maximum and Minimum Temperature

P0 <- gevreg(y = TMX1, data = PORTw[, -1])
add1_gevreg(P0)

pengyuwei94/evreg documentation built on Aug. 29, 2019, 1:06 p.m.