bs.plot {bsgof}

Birnbaum-Saunders Probability Plot

Description

bs.plot produces a Birnbaum-Saunders probability plot.

Usage

bs.plot(x, plot.it=TRUE, a, col.line="black", lty.line=1,  
        xlim=NULL, ylim=NULL, main=NULL, sub=NULL, xlab=NULL, ylab="Probability", ...)

Arguments

x

a numeric vector of data values. Missing values are allowed.

plot.it

logical. Should the result be plotted?

a

the offset fraction to be used; typically in (0,1). See ppoints.

col.line

the color of the straight line.

lty.line

the line type of the straight line.

xlim

the x limits of the plot.

ylim

the y limits of the plot.

main

a main title for the plot, see also title.

sub

a sub title for the plot.

xlab

a label for the x axis, defaults to a description of x.

ylab

a label for the y axis, defaults to "Probability".

...

graphical parameters.

Details

The Birnbaum-Saunders probability plot is based on the linearization proposed by Chang and Tang (1994).

Value

A list with the following components:

x

The sorted data

w

sqrt(x)*qnorm(p)

Author(s)

Chanseok Park

References

Chang, D. S and Tang, L. C. (1994). Graphical analysis for Birnbaum-Saunders distribution. Microelectronics Reliability 34: 17-22.

Birnbaum, Z. W. and Saunders, S. C. (1969). Estimation for a Family of Life Distributions with Applications to Fatigue. J. Appl. Probab. 6(2): 328-347.

See Also

qqnorm, qqplot.

wp.plot for the Weibull probability plot in package weibullness.

Examples

 
# Data set from Birnbaum and Saunders (1969).
attach(BSdata)
data = psi21k
bs.plot(data)

# Adding cosmetic lines
bs.plot(data, main="BS probability plot", lty.line=2, pch=3, col.line="red")

ticklabels=c(0.01, seq(0.1,0.9,by=0.1), seq(0.91,0.99,by=0.01) )
qn = quantile(data, probs=ticklabels) 
ticksat= qnorm(ticklabels)* sqrt( qn )
hline =  qnorm( ticklabels  ) *  sqrt( qn )

abline( h=hline,    col=gray(0.5), lty=3, lwd=0.6 )
abline( v=  seq(0, 2500, by=100), col=gray(0.5), lty=3, lwd=0.5 )

abline( h= qnorm(0.5)*sqrt(median(data)), col=gray(0.1), lty=1, lwd=0.6 )
abline( v= median(data), col=gray(0.1), lty=1, lwd=0.6 )