| cdfpe3 | R Documentation |
This function computes the cumulative probability or nonexceedance probability of the Pearson Type III distribution given parameters (\mu, \sigma, and \gamma) computed by parpe3. These parameters are equal to the product moments: mean, standard deviation, and skew (see pmoms). The cumulative distribution function is
F(x) = \frac{G\left(\alpha,\frac{Y}{\beta}\right)}{\Gamma(\alpha)} \mbox{,}
for \gamma \ne 0 and where F(x) is the nonexceedance probability for quantile x, G is defined below and is related to the incomplete gamma function of R (pgamma()), \Gamma is the complete gamma function, \xi is a location parameter, \beta is a scale parameter, \alpha is a shape parameter, and Y = x - \xi if \gamma > 0 and Y = \xi - x if \gamma < 0. These three “new” parameters are related to the product moments by
\alpha = 4/\gamma^2 \mbox{,}
\beta = \frac{1}{2}\sigma |\gamma| \mbox{,}
\xi = \mu - 2\sigma/\gamma \mbox{.}
Lastly, the function G(\alpha,x) is
G(\alpha,x) = \int_0^x t^{(a-1)} \exp(-t)\, \mathrm{d}t \mbox{.}
If \gamma = 0, the distribution is symmetrical and simply is the normal distribution with mean and standard deviation of \mu and \sigma, respectively. Internally, the \gamma = 0 condition is implemented by pnorm(). If \gamma > 0, the distribution is right-tail heavy, and F(x) is the returned nonexceedance probability. On the other hand if \gamma < 0, the distribution is left-tail heavy and 1-F(x) is the actual nonexceedance probability that is returned.
cdfpe3(x, para)
x |
A real value vector. |
para |
The parameters from |
Nonexceedance probability (F) for x.
W.H. Asquith
Hosking, J.R.M., 1990, L-moments—Analysis and estimation of distributions using linear combinations of order statistics: Journal of the Royal Statistical Society, Series B, v. 52, pp. 105–124.
Hosking, J.R.M., 1996, FORTRAN routines for use with the method of L-moments: Version 3, IBM Research Report RC20525, T.J. Watson Research Center, Yorktown Heights, New York.
Hosking, J.R.M., and Wallis, J.R., 1997, Regional frequency analysis—An approach based on L-moments: Cambridge University Press.
pdfpe3, quape3, lmompe3, parpe3
lmr <- lmoms(c(123,34,4,654,37,78))
cdfpe3(50,parpe3(lmr))
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