MLEln2p_cpp: Lognormal 2-parameter MLE calculation.

In debias: Functions implementing MLE,its bias reduction for small samples and related issues

Description

MLEln2p_cpp is a wrapper function to a fast C++ implementation optimizing parameters of the 2-parameter lognormal distribution for a set of data consisting of failures, or alternatively failures and suspensions.

Usage

 MLEln2p_cpp(x, s=NULL, MRRfit=NULL)

Arguments

x	A vector of failure data.
s	An optional vector of suspension data.
MRRfit	An optional vector such as produced by MRRln2pxy or MRRln2pyx having parameter order [1] Mulog, [2] Sigmalog. If not provided, this function will calculate a suitable estimate of the parameters to initiate the optimization.

Details

This function calls a C++ function that performs the Nelder-Meade simplex optimization of the negative log-likelihood function. The code is streamlined for the two-parameter, only, case.

Value

A vector containing results in the following order: Mulog, Sigmalog, Log-Likelihood.

References

Mike Hutt,a GNU implementation in C http://www.mikehutt.com/neldermead.html Sasa Singer and John Nelder (2009) Nelder-Mead algorithm. Scholarpedia, 4(7):2928. Botao Jia, Simplex-Optimization-Algorithm-and-Implemetation-in-C-Programming.htm, codeguru.com/cpp/article.php/c17505/

Examples

failures<-c(90,96,30,49,82)
suspensions<-c(100,45,10)
fit_result<-MLEln2p_cpp(failures,suspensions)

debias documentation built on May 2, 2019, 4:49 p.m.