Apwm2BpwmRC {lmomco} | R Documentation |
Conversion between A- and B-Type Probability-Weighted Moments for Right-Tail Censoring of an Appropriate Distribution
Description
This function converts “A”-type probability-weighted moments (PWMs, \beta^A_r
) to the “B”-type \beta^B_r
. The \beta^A_r
are the ordinary PWMs for the m
left noncensored or observed values. The \beta^B_r
are more complex and use the m
observed values and the m-n
right-tailed censored values for which the censoring threshold is known. The “A”- and “B”-type PWMs are described in the documentation for pwmRC
.
This function uses the defined relation between to two PWM types when the \beta^A_r
are known along with the parameters (para
) of a right-tail censored distribution inclusive of the censoring fraction \zeta=m/n
. The value \zeta
is the right-tail censor fraction or the probability \mathrm{Pr}\lbrace \rbrace
that x
is less than the quantile at \zeta
nonexceedance probability (\mathrm{Pr}\lbrace x < X(\zeta) \rbrace
). The relation is
\beta^B_{r-1} = r^{-1}\lbrace\zeta^r r \beta^A_{r-1} + (1-\zeta^r)X(\zeta)\rbrace \mbox{,}
where 1 \le r \le n
and n
is the number of moments, and X(\zeta)
is the value of the quantile function at nonexceedance probability \zeta
. Finally, the RC
in the function name is to denote R
ight-tail C
ensoring.
Usage
Apwm2BpwmRC(Apwm,para)
Arguments
Apwm |
A vector of A-type PWMs: |
para |
The parameters of the distribution from a function such as |
Value
An R list
is returned.
Author(s)
W.H. Asquith
References
Hosking, J.R.M., 1995, The use of L-moments in the analysis of censored data, in Recent Advances in Life-Testing and Reliability, edited by N. Balakrishnan, chapter 29, CRC Press, Boca Raton, Fla., pp. 546–560.
See Also
Examples
# Data listed in Hosking (1995, table 29.2, p. 551)
H <- c(3,4,5,6,6,7,8,8,9,9,9,10,10,11,11,11,13,13,13,13,13,
17,19,19,25,29,33,42,42,51.9999,52,52,52)
# 51.9999 was really 52, a real (noncensored) data point.
z <- pwmRC(H,52)
# The B-type PMWs are used for the parameter estimation of the
# Reverse Gumbel distribution. The parameter estimator requires
# conversion of the PWMs to L-moments by pwm2lmom().
para <- parrevgum(pwm2lmom(z$Bbetas),z$zeta) # parameter object
Bbetas <- Apwm2BpwmRC(z$Abetas,para)
Abetas <- Bpwm2ApwmRC(Bbetas$betas,para)
# Assertion that both of the vectors of B-type PWMs should be the same.
str(Abetas) # A-type PWMs of the distribution
str(z$Abetas) # A-type PWMs of the original data