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WeibullR

An R package for Life Data Analysis

 

The WeibullR package provides a flexible data entry capability with three levels of usage.

 

Quick Fit Functions

Functions with intuitive names MLEw2p through MRRln3p for preparing simple fits, bounds, and displays using default options. Only data sets with exact failure times and/or suspensions are processed.

The quick fit functions return a simple named vector of the fitted parameters with appropriate goodness of fit measure(s).

Optional preparation of appropriate interval bounds (at 90\% confidence), or a display of fit and bounds are controlled by two final arguments taking logical entry, such that a function call like MLEw2p(input_data,T,T) will generate a plot with the fitted data and confidence interval bounds.

When the first logical for bounds is set to TRUE, the returned object will be a list with the fitted parameter vector first and dataframe of bound values second.

 

wblr Object Model

Construction of a wblr object is initiated by providing a data set through function wblr.

Modification of the object with the progressive addition of fits and confidence interval bounds is made via functions wblr.fit and wblr.conf.

Fine control over many aspects of fit, confidence, and display are made possible using a flexible options mechanism.

Display for single object models is via S3 methods plot or contour, while multiple objects (provided as a list) can be displayed on a single plot using plot.wblr, plot_contour, or contour.wblr.

 

Back-end Functions

Access to back-end functions providing all the functionality of the upper levels of usage are provided as exported functions.

These functions may provide advanced users with resources to expand analysis further than has been implemented within the WeibullR package.

 

Data Entry

Data entry is made through the Quick Fit functions, wblr, or on the backend through getPPP for rank regression, and mleframe for mle processing.

In all cases the primary argument x can be a vector of exact time failures or a dataframe with time, and eventcolumns as a minimum. An additional column qty may optionally be used to record duplicated data.

If the dataframe entry is not used (in favor of an exact time failure vector), a second argument, s, can be used to enter a vector of last observed success times for right censored data (suspensions).

Beyond the entry of the first two data types, interval data (including discoveries with last known success time=0) are entered via argument interval as a dataframe with columnsleft, and right as a miniumum. As with the primary argument dataframe entry, an additional column qty may optionally be used to record duplicated interval data. Such interval data entry is not supported with the Quick Fit functions.

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Version

Install

install.packages('WeibullR')

Monthly Downloads

415

Version

1.1.10

License

GPL (>= 3)

Maintainer

Jacob T Ormerod

Last Published

March 24th, 2022

Functions in WeibullR (1.1.10)

MLEcontour

Likelihood Ratio Contour for Weibull and Lognormal Fitted Data
FMbounds

Fisher Matrix bounds
MLEln3p

Quick Fit, Maximum Likelihood Estimate for 3-parameter lognormal distributions
LRbounds

Likelihood Ratio bounds
BBB

Beta Binomial Bounds
MLEln2p

Quick Fit, Maximum Likelihood Estimate for 2-parameter lognormal distributions
LLln

Log Likelihood for log-normal fitted data, failures and suspensions only
LLw

Log Likelihood for weibull fitted data, failures and suspensions only
AbPval

Determination of the percentile of r and r-squared, by correlation. Here designated "Abernethy's P-value"
MLEw2p

Quick Fit, Maximum Likelihood Estimate for 2-parameter weibull distributions
MRRln3p

Quick Fit, Median rank regression for log-normal distribution with third parameter optimization
MRRw2p

Quick Fit, Median rank regression for 2-parameter weibull distributions
WeibullR-package

Weibull-R : Weibull Analysis on R
MRRw3p

Quick Fit, Median rank regression for weibull distribution in 3-parameters
getPPP

Alias for getPercentilePlottingPositions, sets data into the format required by lslr.
getPercentilePlottingPositions

Determination of percentile plotting positions for linear regression with many optional methods
getCCC2

Determination of the square of the "Critical Correlation Coefficient" (CCC2).
contour.wblr

S3 Likelihood Ratio Contour Maps From wblr Objects
pivotal.rr

Pivotal 'Monte Carlo' re-sampling of least squares linear regression models
plot.wblr

S3 wblr Object Plotting on pretty canvax
mleframe

Set life(time) data into the format required by mlefit
mlefit

Maximum likelihood regression on Weibull and Lognormal distributions
options.wblr

Options list for wblr Objects
p2y

Transform Probability Value to the Y-Axis of a "plot.wblr" Canvas
xfit

Extract a Fit Summary from a wblr Object
wblr.conf

Add Confidence Interval Bounds to wblr Objects
wblr

Create a wblr Object for Life Data Analysis
xbounds

Extract a bounds dataframe from a wblr Object
MRRln2p

Quick Fit, Median rank regression for 2-parameter log-normal distributions
wblr.fit

Add Fit Distributions to wblr Objects
hrbu

Hirose and Ross beta unbias factors for Weibull MLE
MLEw3p

Quick Fit, Maximum Likelihood Estimation for weibull distribution in 3-parameters
lslr

Least squares linear regression with many optional methods
wblrLoglike

Log likelihood for Weibull and Lognormal fitted data including intervals
plot_contour

Plotting of Likelihood Ratio Contours from wblr Objects
rba

Reduced Bias Adjustment for Weibull and Lognormal MLE
weibayes.mle

Fitting for Minimal Failure Datasets using likelihood optimization
weibayes

Fitting for Minimal Failure Datasets