lslr: Least squares linear regression with many optional methods

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

Description

This function implements a wide assortment of options for linear regression fitting of distributions specific to reliability analysis for the Abernethy Reliability Methods project.

Usage

lslr(x, dist="weibull", npar=2, reg_method="XonY") 

Arguments

x	A dataframe such as generated by getPPP with column names 'data' and 'ppp'.
dist	A string defining a distribution to be fit. Implemented distributions are "weibull" (default), "lnorm", and "gumbel" (Extreme Value Type 1)
npar	Number of parameters to evaluate. This really is only looking for potential value of 3 for 3rd parameter optimization. Any other value will resolve the same as default of 2.
reg_method	A string defining a method of regression optimization whether "XonY" axis(default and "best practice for standard small sample fitting) or "YonX" axis for alternative study ("YonX" recommended for an inspection option by Abernethy).

Value

Returns a named vector with parameters of the distribution fit and goodness of fit in terms of R squared.

References

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

Examples

failures<-c(90,96,30,49,82)
suspensions<-c(100,45,10)
default_weibull_fit<-lslr(getPPP(failures,suspensions))

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