LLln: Log Likelihood for log-normal fitted data

In abremPivotals: Linear Rank Regression Models with Pivotal Monte Carlo Methods for Reliability Analysis

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

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 providing the original and explicitly subtracting 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

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

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])

abremPivotals documentation built on May 2, 2019, 6:52 p.m.