pp.f | R Documentation |
There are two major forms (outside of the general plotting-position formula pp
) for estimation of the p_r
th probability of the r
th order statistic for a sample of size n
: the mean is pp'_r = r/(n+1)
(Weibull plotting position) and the Beta quantile function is pp_r(F) = IIB(F, r, n+1-r)
, where F
represents the nonexceedance probability of the plotting position. IIB
is the “inverse of the incomplete beta function” or the quantile function of the Beta distribution as provided in R by qbeta(f, a, b)
. If F=0.5
, then the median is returned but that is conveniently implemented in pp.median
. Readers might consult Gilchrist (2011, chapter 12) and Karian and Dudewicz (2011, p. 510).
pp.f(f, x)
f |
A nonexceedance probability. |
x |
A vector of data. The ranks and the length of the vector are computed within the function. |
An R vector
is returned.
The function uses the R function rank
, which has specific settings to handle tied data. For implementation here, the ties.method="first"
method to rank
is used.
W.H. Asquith
Gilchrist, W.G., 2000, Statistical modelling with quantile functions: Chapman and Hall/CRC, Boca Raton.
Karian, Z.A., and Dudewicz, E.J., 2011, Handbook of fitting statistical distributions with R: Boca Raton, FL, CRC Press.
pp
, pp.median
X <- sort(rexp(10))
PPlo <- pp.f(0.25, X)
PPhi <- pp.f(0.75, X)
plot(c(PPlo,NA,PPhi), c(X,NA,X))
points(pp(X), X) # Weibull i/(n+1)
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