getPercentilePlottingPositions: Determination of percentile plotting positions for linear...

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

Description

Determination of plotting positions to be used for linear regression of life data analysis is a highly debated topic. This function implements a wide assortment of options for the Abernethy Reliability Methods project.

Usage

getPercentilePlottingPositions(x, s=NULL, interval=NULL, ppos="Benard",
 aranks="Johnson", ties=NULL) 

Arguments

x	A vector of failure data.
s	An optional vector of suspension data.
interval	reserved argument for interval censored data - NOT YET IMPLEMENTED.
ppos	A string defining a plotting position method. Implemented options include "Benard" (as default), "beta" for the incomplete beta function, "mean" also known as Herd-Johnson, "Hazen" or modified Kaplan-Meier, "Kaplan-Meier" with modification for final complete failure, and "Blom.
aranks	A string defining the method for establishing adjusted ranks when suspension data (right censored) are present. Implemented options include "Johnson" (as default) and "KMestimator"
ties	A string defining a method of eliminating ties, or duplicate time valued data, from plotting. Implemented options include "highest" (used as 'Inspection Option #1' by Abernethy), "mean", "lowest", and "sequential".

Value

Returns a dataframe with the failure data (as potentially reduced by ties argument), the probability plotting positions, and adjusted ranks.

References

Dr. Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition" Leonard C. Johnson (1964) "The Statistical Treatment of Fatigue Experiments"

Examples

failures<-c(90,96,30,49,82)
suspensions<-c(100,45,10)
median_percentile_ranks<-getPPP(failures,suspensions)[,2]

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