lslr: Least squares linear regression with many optional methods

Description Usage Arguments Value References Examples

View source: R/lslr.r

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

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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 the order of axes presented for regression, whether "XonY" axis(default) or "YonX" axis for alternative study.

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

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

WeibullR documentation built on July 27, 2018, 3 p.m.