pareto_optimise: Locally optimal fitting of convoluted Pareto distribution to...

In mpadge/paretoconv: Calculates the N-Fold Convolution of Two Pareto Distributions

Description

Locally optimal fitting of convoluted Pareto distribution to observed data

Usage

pareto_optimise(m, x0 = 1, n = 1, check_non_conv = TRUE,
  quiet = TRUE)

Arguments

m	A poweRlaw::displ object containing data to be modelled
x0	Initial guess for lower limit of Pareto distribution
n	Initial guess for number of convolutions
check_non_conv	If TRUE first checks whether a non-convoluted model may be optimal
quiet	If FALSE, progress information is dumped to screen

Value

Position of the local optimum as quantified by x0 and n, along with associated Kolmogorow-Smirnov statistic quantifying maximal distance from convoluted Pareto Cumulative Distribution Function (CDF) and empirical CDF of model m.

Note

This function only finds local optima - it is up to the user to ensure that the given start values are near the global optimum. Values of n = 0 are NOT searched.

mpadge/paretoconv documentation built on March 9, 2020, 9:54 p.m.