getCCC2: Determination of the square of the "Critical Correlation...

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

Description

Dr. Abernethy has considered the 10th percentile of Correlation Coefficients generated by pivotal Monte Carlo analysis to represent a critical measure by which a fit should be designated suitable for further analysis. In long-standing practice, the difference between the square of the Correlation Coefficient and the CCC2 (R^2 - CCC^2) has been used to make comparitive judgments between weibull and lognormal fitting on the same data.

Usage

getCCC2(F, model="weibull") 

Arguments

F	The quantity of complete failure data points under consideration.
model	A string defining the distribution under consideration. Only a value of "lnorm" will be treated any differently from default of "weibull".

Details

The value returned is derived from a correlation developed from previously run pivotal analysis with 10^8 random samples. Project "Abernethy Reliability Methods" has judged that only the CCC^2 derived from 2 parameter models to have usefullness in such analysis. This is seen from the "Detect Power" presentations in Appendix D of "The New Weibull Handbook, Fifth Edition". For validity of a 3rd parameter optimization on a given model over its 2 parameter fit, only the Likelihood Ratio Test will be applied. This validity check requires an LRT-P greater than 50

Value

Returns a single valued vector for the square of CCC (for comparison with R squared).

References

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

Examples

thisCCC2<-getCCC2(50, "lnorm")

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