cdfln3 | R Documentation |
This function computes the cumulative probability or nonexceedance probability of the Log-Normal3 distribution given parameters (\zeta
, lower bounds; \mu_{\mathrm{log}}
, location; and \sigma_{\mathrm{log}}
, scale) computed by parln3
. The cumulative distribution function (same as Generalized Normal distribution, cdfgno
) is
F(x) = \Phi(Y) \mbox{,}
where \Phi
is the cumulative ditribution function of the
Standard Normal distribution and Y
is
Y = \frac{\log(x - \zeta) - \mu_{\mathrm{log}}}{\sigma_{\mathrm{log}}}\mbox{,}
where \zeta
is the lower bounds (real space) for which \zeta < \lambda_1 - \lambda_2
(checked in are.parln3.valid
), \mu_{\mathrm{log}}
be the mean in natural logarithmic space, and \sigma_{\mathrm{log}}
be the standard deviation in natural logarithm space for which \sigma_{\mathrm{log}} > 0
(checked in are.parln3.valid
) is obvious because this parameter has an analogy to the second product moment. Letting \eta = \exp(\mu_{\mathrm{log}})
, the parameters of the Generalized Normal are \zeta + \eta
, \alpha = \eta\sigma_{\mathrm{log}}
, and \kappa = -\sigma_{\mathrm{log}}
. At this point, the algorithms (cdfgno
) for the Generalized Normal provide the functional core.
cdfln3(x, para)
x |
A real value vector. |
para |
The parameters from |
Nonexceedance probability (F
) for x
.
The parameterization of the Log-Normal3 results in ready support for either a known or unknown lower bounds. Details regarding the parameter fitting and control of the \zeta
parameter can be seen under the Details section in parln3
.
W.H. Asquith
Asquith, W.H., 2011, Distributional analysis with L-moment statistics using the R environment for statistical computing: Createspace Independent Publishing Platform, ISBN 978–146350841–8.
pdfln3
, qualn3
, lmomln3
, parln3
, cdfgno
lmr <- lmoms(c(123,34,4,654,37,78))
cdfln3(50,parln3(lmr))
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