OC2c: Operating Characteristics of an Acceptance Sampling Plan

Description

The preferred way of creating new objects from the family of "OC2c" classes.

Usage

OC2c(n,c,r=if (length(c)

Value

An object from the family of OC2c-class, namely of class

OCbinomial, OChypergeom, or OCpoisson.

Arguments

n: A vector of length k giving the sample size at each of the k stages of sampling, e.g. for double sampling k=2.
c: A vector of length k giving the cumulative acceptance numbers at each of the k stages of sampling.
r: A vector of length k giving the cumulative rejection numbers at each of the k stages of sampling.
type: The possible types relate to the distribution on which the plans are based on, namely, binomial, hypergeom, and poisson.
...: Additional parameters passed to the class generating function for each type. See Details for options.

Details

Typical usages are:


    OC2c(n, c)
    OC2c(n, c, r, pd)
    OC2c(n, c, r, type="hypergeom", N, pd)
    OC2c(n, c, r, type="poisson", pd)

The first and second forms use a default type of "binomial". The first form can calculate r only when n and c are of length 1 or 2.

The second form provides a the proportion of defectives, pd, for which the OC function should be calculated (default is pd=seq(0, 1, 0.01).

The third form states that the OC function based on a Hypergeometric distribution is desired. In this case the population size N also needs to be specified. In this case, pd indicates the proportion of population defectives, such that pd*N gives the actual number of defectives in the population. If N or pd are not specified they take defaults of N=100 and pd=seq(0, 1, 0.01). A warning is issued if N and D=N*pd are not integers by checking the value, not the object type.

Examples

Run this code

## A standard binomial sampling plan
x <- OC2c(10,1)
x ## print out a brief summary
plot(x) ## plot the OC curve
plot(x, xlim=c(0,0.5)) ## plot the useful part of the OC curve

## A double sampling plan
x <- OC2c(c(125,125), c(1,4), c(4,5), pd=seq(0,0.1,0.001))
x
plot(x) ## Plot the plan

## Assess whether the plan can meet desired risk points
assess(x, PRP=c(0.01, 0.95), CRP=c(0.05, 0.04))

## A plan based on the Hypergeometric distribution
x <- OC2c(10,1, type="hypergeom", N=5000, pd=seq(0,0.5, 0.025))
plot(x)

## The summary
x <- OC2c(10,1, type="hypergeom", N=5000, pd=seq(0,0.5, 0.1))
summary(x, full=TRUE)

## Plotting against a function which generates P(defective)
xm <- seq(-3, 3, 0.05) ## The mean of the underlying characteristic
x <- OC2c(10, 1, pd=1-pnorm(0, mean=xm, sd=1))
plot(xm, x) ## Plot P(accept) against mean