lslr: Least squares linear regression with many optional methods

In WeibullR: Weibull Analysis for Reliability Engineering

Least squares linear regression with many optional methods

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", abpval=TRUE) 

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 the order of axes presented for regression, whether "XonY" axis(default) or "YonX" axis for alternative study.
abpval	A logical indicating whether to include the Abernethy P value in the result. When set to False the output vector will further be returned unnamed.

Value

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

References

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))

WeibullR documentation built on June 26, 2022, 1:06 a.m.