Are the Distribution Parameters Consistent with the Linear Mean Residual Quantile Function Distribution

Description

Is the distribution parameter object consistent with the corresponding distribution? The distribution functions (cdflmrq, pdflmrq, qualmrq, and lmomlmrq) require consistent parameters to return the cumulative probability (nonexceedance), density, quantile, and L-moments of the distribution, respectively. These functions internally use the are.parlmrq.valid function. The constraints on the parameters are listed under qualmrq. The documentation for qualmrq provides the conditions for valid parameters.

Usage

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are.parlmrq.valid(para, nowarn=FALSE)

Arguments

para

A distribution parameter list returned by parlmrq or vec2par.

nowarn

A logical switch on warning suppression. If TRUE then options(warn=-1) is made and restored on return. This switch is to permit calls in which warnings are not desired as the user knows how to handle the returned value—say in an optimization algorithm.

Value

TRUE

If the parameters are lmrq consistent.

FALSE

If the parameters are not lmrq consistent.

Note

This function calls is.lmrq to verify consistency between the distribution parameter object and the intent of the user.

Author(s)

W.H. Asquith

References

Midhu, N.N., Sankaran, P.G., and Nair, N.U., 2013, A class of distributions with linear mean residual quantile function and it's generalizations: Statistical Methodology, v. 15, pp. 1–24.

See Also

is.lmrq, parlmrq

Examples

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para <- parlmrq(lmoms(c(3, 0.05, 1.6, 1.37, 0.57, 0.36, 2.2)))
if(are.parlmrq.valid(para)) Q <- qualmrq(0.5,para)

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