spdfit-methods: Method: Fitting the Semi-Parametric Distribution

In spd: Semi Parametric Distribution

Description

The semi-parametric distribution fitting method.

Usage

spdfit(data, upper = 0.9, lower = 0.1, tailfit="GPD", type = c("mle", "pwm"), 
kernelfit = c("normal", "box", "epanech", "biweight", "triweight"), 
information = c("observed", "expected"), title = NULL, description = NULL, ...)

Arguments

data	An object coercible to a matrix.
upper	Upper tail cutoff for fitting the generalized pareto or other distribution.
lower	Lower tail cutoff for fitting the generalized pareto or other distribution.
tailfit	Distribution to Use for fitting the tails.
type	A character string selecting the desired estimation method, either "mle" for the maximum likelihood method or "pwm" for the probability weighted moment method. By default, the first will be selected.
kernelfit	Type of kernel to fit to the interior of the distribution.
information	Whether tail distribution standard errors should be calculated with "observed" or "expected" information. This only applies to the maximum likelihood method; for the probability-weighted moments method "expected" information is used if possible.
title	A character string which allows for a project title.
description	A character string which allows for a brief description.
...	Control parameters and plot parameters optionally passed to the optimization and/or plot function. Parameters for the optimization function are passed to components of the control argument of optim.

Value

Returns an object of class SPD.

Examples

## Not run: 
library(MASS)
x<-SP500/100
fit<-spdfit(x)
show(fit)
#plot(fit,which="all")

## End(Not run)

spd documentation built on May 2, 2019, 1:51 p.m.