parray

This function estimates the parameters of the Rayleigh distribution given the L-moments of the data in an L-moment object such as that returned by <code>lmoms</code>. The relations between distribution parameters and L-moments are
$$\alpha = \frac{2\lambda_2\sqrt{\pi}}{\sqrt{2} - 1}\mbox{,}$$
and
$$\xi = \lambda_1 - \alpha\sqrt{\pi/2}\mbox{.}$$

distribution (parameters)

Distribution: Rayleigh

Extensive functions for Lmoments (LMs) and probability-weighted moments (PWMs),
distribution parameter estimation, LMs for distributions, LM ratio diagrams, multivariate
Lcomoments, and asymmetric (asy) trimmed LMs (TLMs). Maximum likelihood and
maximum product spacings estimation are available. Right-tail and left-tail LM censoring
by threshold or indicator variable are available. LMs of residual (resid) and reversed
(rev) residual life are implemented along with 13 quantile operators for reliability analyses.
Exact analytical bootstrap estimates of order statistics, LMs, and LM var-covars are available.
Harri-Coble Tau34-squared Normality Test is available. Distributions with L, TL, and added
(+) support for right-tail censoring (RC) encompass: Asy Exponential (Exp) Power [L],
Asy Triangular [L], Cauchy [TL], Eta-Mu [L], Exp. [L], Gamma [L], Generalized (Gen) Exp
Poisson [L], Gen Extreme Value [L], Gen Lambda [L, TL], Gen Logistic [L], Gen Normal [L],
Gen Pareto [L+RC, TL], Govindarajulu [L], Gumbel [L], Kappa [L], Kappa-Mu [L],
Kumaraswamy [L], Laplace [L], Linear Mean Residual Quantile Function [L], Normal [L],
3p log-Normal [L], Pearson Type III [L], Polynomial Density-Quantile 3 and 4 [L],
Rayleigh [L], Rev-Gumbel [L+RC], Rice [L], Singh Maddala [L], Slash [TL], 3p Student t [L],
Truncated Exponential [L], Wakeby [L], and Weibull [L].

William Asquith

lmomco

L-Moments, Censored L-Moments, Trimmed L-Moments, L-Comoments,
and Many Distributions

parray function

<dl> <dt>lmom</dt>
<dd>An L-moment object created by <code>lmoms</code> or <code>vec2lmom</code>.</dd> <dt>xi</dt>
<dd>The lower limit of the distribution. If $\xi$ is known then alternative algorithms are triggered and only the first L-moment is required for fitting.</dd> <dt>checklmom</dt>
<dd>Should the <code>lmom</code> be checked for validity using the <code>are.lmom.valid
</code> 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.</dd> <dt>...</dt>
<dd>Other arguments to pass.</dd></dl>

Arguments

Author

Estimate the Parameters of the Rayleigh Distribution — parray

<dl>

 <dt>lmom</dt>
<dd>An L-moment object created by <code>lmoms</code> or <code>vec2lmom</code>.</dd>

 <dt>xi</dt>
<dd>The lower limit of the distribution. If $\xi$ is known then alternative algorithms are triggered and only the first L-moment is required for fitting.</dd>

 <dt>checklmom</dt>
<dd>Should the <code>lmom</code> be checked for validity using the <code>are.lmom.valid
</code> 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.</dd>

 <dt>...</dt>
<dd>Other arguments to pass.</dd>

</dl>

parray: Estimate the Parameters of the Rayleigh Distribution

Description

Usage

Value

Arguments

Author

References

See Also

Examples