# CBpiv: P-value determination by pivotal MC with CCC^2 report In pivotals: Functions implementing pivotal Monte Carlo methods

## Description

This is a wrapper function calling C++ code that executes a pivotal analysis to deliver double sided confidence bounds at the B-value quantiles for the model distribution. The bounds are presented as log values for X axis plotting suitable for transformation to a specific linear fit to data of the same size. The pivotal points are expected to be the basis for a curve generation upon ultimate display.

## Usage

 `1` ```CBpiv(x, CI, S = 10^4, Bval=mrank(rep(1,15)), Eta=1.0, Beta=1.0, model = "w2", seed=1234, ProgRpt = FALSE) ```

## Arguments

 `x` The quantity of complete failures for evaluation, or an event vector `CI` The double sided confidence interval of interest. `S` The number of random samples to be drawn for Monte Carlo simulation. S must be a multiple of 10, not less than 1,000. The default of 10^4 is adequate for most instances. S is implemented as an unsigned int in C++ code. The maximum limit is 4x10^9 if system memory permits. `Bval` A vector of B-values at which to determine the confidence bounds. `Eta` The Eta parameter to be used in random sampling. Default = 1.0 `Beta` The Beta parameter to be used in random sampling. Default = 1.0 `model` A character string representing the model of interest. The default value of "w2" for 2-parameter Weibull is the only model currently valid. `seed` an integer used to set the RNG seed. Default = 1234 `ProgRpt` A boolean value to control the generation of percent completion feedback in the R terminal.

## Value

Returns a dataframe holding the Lower bound, the Median, and the Upper bound according to the sequence of B-values provided.

## References

Dr. Robert B. Abernethy, (2008) "The New Weibull Handbook, Fifth Edition"

## Examples

 `1` ```bounds<-CBpiv(10,0.9) ```

pivotals documentation built on May 2, 2019, 4:51 p.m.