MLEw2pContour: Likelihood Contour for Weibull

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

Description

MLEw2pContour This function generates points for a display of the likelihood contour at given confidence limit for the 2-parameter Weibull distribution.

Usage

MLEw2pContour(x,s=NULL,CL=0.9,DF=1,MLEfit=NULL,ptDensity=100,RadLimit=1e-5,debias=FALSE,
   show=FALSE)

Arguments

x	A vector of failure data.
s	An optional vector of suspension data.
CL	a two-sided confidence limit.
DF	an integer value indicating degrees of freedom to apply to the Chi square test, which defaults to DF=1 for confidence bound use. Should be set to 2 for comparison of two models each with 2 parameters.
MLEfit	an optional argument to use a fit made external to the function.
ptDensity	an integer value for the number of points to be plotted around the circumference of the contour.
RadLimit	a convergence limit for the contour radials based on specific units of Eta/Eta_hat and Beta/Beta_hat.
debias	a logical value indicating whether the RBA and FF adjustments should be applied to output.
show	a logical value indicating whether a graphical output is desired (independent of abrem activity).

Details

The contour points (p1,p2) identified as satisfying the root of the equation, (log(ML(p1_hat,p2_hat))-log(RL(p1,p2))*FF - chisquare(CL,DF)/2=0 ,where ML is Maximum Likelihood for the data,and RL is Ratioed Likelihood for the data at selected points for the contour. Depending on the value of debias, the contour points are then modified by the RBA (median basis) on the Wiebull shape parameter, Beta.

Value

A dataframe of plotting points for the contour.

References

Dr. Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition" Tao Pang,(1997) "An Introduction to Computational Physics"

Examples

fig3cF<-c(1500,2250,4000,4300,7000)
fig3cS<-c(1750,5000)
Contour<-MLEw2pContour(fig3cF,fig3cS,debias=TRUE)

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