# OC2c: Operating Characteristics of an Acceptance Sampling Plan In AcceptanceSampling: Creation and Evaluation of Acceptance Sampling Plans

## Description

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

## Usage

 ```1 2``` ```OC2c(n,c,r=if (length(c)<=2) rep(1+c[length(c)], length(c)) else NULL, type=c("binomial","hypergeom", "poisson"), ...) ```

## 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:

 ```1 2 3 4 5``` ``` 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.

## Value

An object from the family of `OC2c-class`, namely of class `OCbinomial`, `OChypergeom`, or `OCpoisson`.

`OC2c-class`

## Examples

 ``` 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26``` ```## 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 ```

### Example output

```Acceptance Sampling Plan (binomial)

Sample 1
Sample size(s)       10
Acc. Number(s)        1
Rej. Number(s)        2
Acceptance Sampling Plan (binomial)

Sample 1 Sample 2
Sample size(s)      125      125
Acc. Number(s)        1        4
Rej. Number(s)        4        5
Acceptance Sampling Plan (binomial)

Sample 1 Sample 2
Sample size(s)      125      125
Acc. Number(s)        1        4
Rej. Number(s)        4        5

Plan CANNOT meet desired risk point(s):

Quality   RP P(accept) Plan P(accept)
PRP           0.01           0.95     0.89995598
CRP           0.05           0.04     0.01507571
Acceptance Sampling Plan (hypergeom with N=5000)

Sample 1
Sample size(s)       10
Acc. Number(s)        1
Rej. Number(s)        2

Detailed acceptance probabilities:

Pop. Defectives Pop. Prop. defective  P(accept)
0                  0.0 1.00000000
500                  0.1 0.73613783
1000                  0.2 0.37556779
1500                  0.3 0.14904357
2000                  0.4 0.04620019
2500                  0.5 0.01068073
```

AcceptanceSampling documentation built on May 1, 2019, 10:24 p.m.