MRRln3p: Median rank regression for log-normal distribution with third...

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

Description

Determination of log-normal fitting parameterswith third parameter optimization. Goodness of fit measures and confidence interval bounds can be returned with optional graphical display.

Usage

MRRln3p(x, s=NULL, bounds=FALSE, CI=0.90, show=FALSE) 

Arguments

x	A vector of failure data.
s	An optional vector of suspension data.
bounds	A logical argument defining whether confidence interval bounds should be calculated by pivotal analysis.
CI	A value for the double-sided confidence interval. Applies only if bounds is set to TRUE
show	A logical argument defining whether a simple graphical output is desired.

Details

This function is intended to provide a simple casual method of standard lognoirmal fitting based on default methods, without options. It also provides an example for handling the pivotal values returned from pivotalMC and the likelihood ratio test.

Value

When bounds is set to FALSE this function returns a vector with named elements for Eta, Beta, Rsqr, AbPval (Abernethy's P-value), and LL (log-likelihood). When the bounds argument is set to TRUE a list is returned with the vector as described and a dataframe of confidence interval bound values at a fixed set of descriptive quantiles, dq<-c(.01, .02, .05, .10, .15, .20, .30, .40, .50, .60, .70, .80, .90, .95, .99), suitable for comparison with other software.

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<-MRRln3p(failures, suspensions)

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