This function estimates the parameters (\(\zeta\), lower bounds; \(\mu_{\mathrm{log}}\), location; and \(\sigma_{\mathrm{log}}\), scale) of the Log-Normal3 distribution given the L-moments of the data in an L-moment object such as that returned by lmoms. The relations between distribution parameters and L-moments are seen under lmomln3. The function uses algorithms of the Generalized Normal for core computations. Also, if \(\tau_3 \le 0\), then the Log-Normal3 distribution can not be fit, however reversing the data alleviates this problem.
parln3(lmom, zeta=NULL, checklmom=TRUE, ...)An R
list is returned.
The type of distribution: ln3.
The parameters of the distribution.
The source of the parameters: “parln3”.
An L-moment object created by lmoms or vec2lmom.
Lower bounds, if NULL then solved for.
Should the lmom be checked for validity using the are.lmom.valid function. Normally this should be left as the default and it is very unlikely that the L-moments will not be viable (particularly in the \(\tau_4\) and \(\tau_3\) inequality). However, for some circumstances or large simulation exercises then one might want to bypass this check.
Other arguments to pass.
W.H. Asquith
Let the L-moments by in variable lmr, if the \(\zeta\) (lower bounds) is unknown, then the algorithms return the same fit as the Generalized Normal will attain. However, pargno does not have intrinsic control on the lower bounds and parln3 does. The \(\lambda_1\), \(\lambda_2\), and \(\tau_3\) are used in the fitting for pargno and parln3 but only \(\lambda_1\) and \(\lambda_2\) are used when the \(\zeta\) is provided as in parln3(lmr, zeta=0). In otherwords, if \(\zeta\) is known, then \(\tau_3\) is not used and shaping comes from the choice of \(\zeta\).
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.
lmomln3,
cdfln3, pdfln3, qualn3, pargno