| 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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