This function creates an object of class
"wblr" for further processing
by the other functions of wblr.
Either a dataframe containing at least
An optional vector of right-censored data, or suspensions.
An optional dataframe of interval data having columns specifically named "left" and "right". Left values are the last time at which no failure was evident and may be zero for discovery. Right values are the earliest time at which failure was observed.
Graphical options for plotting the
There are several methods to passing arguments for building an
x is of class
then it must contain
columns. Additional columns in the dataframe will be ignored.
When a single unnamed vector of class
"integer" is supplied, it is treated as a vector
of (life-)time observations.
fail is provided, it is treated as
a vector of (life-)time observations. Take care NOT to supply both
fail in the same function call.
event is provided, it is treated as
a vector of event indicators with possible values of
1. See section "Value" for more details on
x argument is not provided as a dataframe and
susp is provided,
it is treated as a vector of right-censored (life-)time observations
(also called suspended observations or suspensions).
wblr always generates (probability) plot positions for graphically
displaying the (life-)time observations and for (possible) later usage
wblr.fit. The following optiona arguments are most appropriate for
passing in with
A character string defining the distribution target. When used to establish the basis for
contour mapping (without using
wblr.conf with method.conf="lrb") only "weibull" (default)
and "lognormal" are recognized.
Also used with
wblr.fit for specific fitting control.
Plotting position method, it is a character string describing the method of determining vertical plot positions. Implemented methods are "median" (default), "benard","hazen","mean", "kaplan-meier", and "blom".
The method employed for determining rank of failures when suspensions (right censored data) are present in the data set. Implemented methods are "johnson" (default) and "KMestimator".
The method employed for handling duplicate values in the data set.
Implemented methods are "none" (default) "highest", "lowest", "mean", and "sequential".
It is expected that ties handling will be applied to large data sets that will be fitted using the maximum likelihood estimation method, where the effect is only on the graphical presentation. Employing a ties handler on a rank regression model will effectively remove data from the data set, which is likely not intended.
Use of simply
ties as an argument to function
wblr will silently be accepted as
pch Point choice defaults to
1. For more info, see
cex.points Point size defaults to
lwd.points Line width defaults to
interval.col Color defaults to
interval.lty Line type, defaults to
interval.lwd Line width defaults to
Subsequent calls to
wblr.conf will inherit these options.
A named list of class
"wblr". The first list
$data) is a list with up to least three items:
A dataframe containing the provided data formatted with
This is the output of WeibullR function
A dataframe contianing graphical data for exact failure point with their probability plotting positions and adjusted ranks.
If interval data has been provided this dataframe will contain the graphical data for display
$data$dpoints, but with endpoints t1 and t2 for the interval.
William Q. Meeker and Luis A. Escobar, (1998) "Statistical Methods for Reliability Data", Wiley-Interscience, New York
Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition"
John I. McCool, (2012) "Using the Weibull Distribution: Reliability, Modeling and Inference"
Jurgen Symynck, Filip De Bal, Weibull analysis using R, in a nutshell (New Technologies and Products in Machine Manufacturing Technology, Stefan cel Mare University of Suceava, 2010).
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## These code lines all generate the same object ## wblr(c(500,1200,900,1300,510)) wblr(time=c(500,1200,900,1300,510)) ## this input format works, but not recommended. wblr(time=c(500,1200,900,1300,510),event=c(1,1,1,1,1)) wblr(fail=c(500,1200,900,1300,510)) wblr(fail=c(500,1200,900,1300,510),susp=c()) da1 <- data.frame( serial=c("S12","S16","S17","S3","S5"), time=c(500,1200,900,1300,510), event=c(1,1,1,1,1)) ## it is best practice set named objects obj1 <- wblr(da1,label="complete dataset",pch=3,col="orange3") obj2 <- wblr(da1,label="complete dataset",pch=4,pp="benard",col="red") ## Generate a similar dataset, but with suspensions ## wblr(time=c(500,1200,900,1300,510),event=c(1,1,1,0,0)) wblr(data.frame(time=c(500,1200,900,1300,510),event=c(1,1,1,0,0))) wblr(fail=c(500,1200,900),susp=c(1300,510)) wblr(time=c(500,1200,900),susp=c(1300,510)) da3 <- wblr(fail=c(500,1200,900,1300,510), event=c(1,1,1,0,0),label="censored dataset",pch=1,col="blue") ## plot datasets ## ## Not run: plot.wblr(list(da1,da3)) ## End(Not run)
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