LLln: Log Likelihood for log-normal fitted data, failures and...

In WeibullR: Weibull Analysis for Reliability Engineering

Log Likelihood for log-normal fitted data, failures and suspensions only

Description

Determination of log-likelihood values for the basic distributions covered by R should be ubiquitous, but an implementation is provided here to enable likelihood ratio testing particularly for comparison of 3-parameter optimized models with their 2-parameter counterpart.

Usage

LLln(x, s=NULL, Mulog, Sigmalog)

Arguments

x	A vector of failure data.
s	An optional vector of suspension data.
Mulog	The mean parameter from a log-normal fit.
Sigmalog	The standard deviation parameter from a log-normal fit.

Details

Function LLln can only handle datasets with failure and suspension data. For data including intervals wblrLikelihood can be used. This function is somewhat unique among likelihood functions in that it will scan the suspension data for removal of negative values. Such negative values are likely to be entered as data is provided for a 3-parameter fit by explicitly providing the original data with vector subtraction by the optimized t0. The primary intention for likelihood determination is to permit likelihood ratio testing for comparison of 3-parameter optimized models with their 2-parameter counterpart.

Value

Returns a log-likelihood value.

References

Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition"

Marie Laure Delignette-Muller, Christophe Dutang (2015). "fitdistrplus: An R Package for Fitting Distributions". Journal of Statistical Software, 64(4), 1-34. URL http://www.jstatsoft.org/v64/i04/.

Examples

failures<-c(90,96,30,49,82)
suspensions<-c(100,45,10)
fit<-lslr(getPPP(failures, suspensions),dist="lnorm")
LL<-LLln(failures, suspensions, fit[1], fit[2])

WeibullR documentation built on June 26, 2022, 1:06 a.m.