# are.parlmrq.valid: Are the Distribution Parameters Consistent with the Linear...

### 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

 `1` ```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.

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.

`is.lmrq`, `parlmrq`

### Examples

 ```1 2``` ```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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