ciBinomN {EnvStats} | R Documentation |

## Sample Size for Specified Half-Width of Confidence Interval for Binomial Proportion or Difference Between Two Proportions

### Description

Compute the sample size necessary to achieve a specified half-width of a confidence interval for a binomial proportion or the difference between two proportions, given the estimated proportion(s), and confidence level.

### Usage

```
ciBinomN(half.width, p.hat.or.p1.hat = 0.5, p2.hat = 0.4,
conf.level = 0.95, sample.type = "one.sample", ratio = 1,
ci.method = "score", correct = TRUE, warn = TRUE,
n.or.n1.min = 2, n.or.n1.max = 10000,
tol.half.width = 5e-04, tol.p.hat = 5e-04,
tol = 1e-7, maxiter = 1000)
```

### Arguments

`half.width` |
numeric vector of (positive) half-widths.
Missing ( |

`p.hat.or.p1.hat` |
numeric vector of estimated proportions. |

`p2.hat` |
numeric vector of estimated proportions for group 2.
This argument is ignored when |

`conf.level` |
numeric vector of numbers between 0 and 1 indicating the confidence level associated with
the confidence interval(s). The default value is |

`sample.type` |
character string indicating whether this is a one-sample or two-sample confidence interval. |

`ratio` |
numeric vector indicating the ratio of sample size in group 2 to
sample size in group 1 ( |

`ci.method` |
character string indicating which method to use to construct the confidence interval. Possible values are: -
`"score"` (the default), -
`"exact"` , -
`"adjusted Wald"` and, -
`"Wald"` (the`"Wald"` method is**never**recommended but is included for historical purposes).
The exact method is only available for the one-sample case, i.e., when |

`correct` |
logical scalar indicating whether to use the continuity correction when |

`warn` |
logical scalar indicating whether to issue a warning when |

`n.or.n1.min` |
integer indicating the minimum allowed value for |

`n.or.n1.max` |
integer indicating the maximum allowed value for |

`tol.half.width` |
numeric scalar indicating the tolerance to use for the half width for
the search algorithm. The sample sizes are computed so that the actual
half width is less than or equal to |

`tol.p.hat` |
numeric scalar indicating the tolerance to use for the estimated
proportion(s) for the search algorithm.
For the one-sample case, the sample sizes are computed so that
the absolute value of the difference between the user supplied
value of |

`tol` |
positive scalar indicating the tolerance to use for the search algorithm
(passed to |

`maxiter` |
integer indicating the maximum number of iterations to use for
the search algorithm (passed to |

### Details

If the arguments `half.width`

, `p.hat.or.p1.hat`

, `p2.hat`

,
`conf.level`

and `ratio`

are not all the same length, they are
replicated to be the same length as the length of the longest argument.

For the one-sample case, the arguments `p.hat.or.p1.hat`

, `tol.p.hat`

,
`half.width`

, and

`tol.half.width`

must satisfy:

`(p.hat.or.p1.hat + tol.p.hat + half.width + tol.half.width) <= 1`

,

and

`(p.hat.or.p1.hat - tol.p.hat - half.width - tol.half.width) >= 0`

.

For the two-sample case, the arguments `p.hat.or.p1.hat`

, `p2.hat`

,
`tol.p.hat`

,

`half.width`

, and `tol.half.width`

must satisfy:

`((p.hat.or.p1.hat + tol.p.hat) - (p2.hat - tol.p.hat) + half.width + tol.half.width) <= 1`

, and

`((p.hat.or.p1.hat - tol.p.hat) - (p2.hat + tol.p.hat) - half.width - tol.half.width) >= -1`

.

The function `ciBinomN`

uses the search algorithm in the
function `uniroot`

to call the function
`ciBinomHalfWidth`

to find the values of
`n`

(`sample.type="one.sample"`

) or
`n_1`

and `n_2`

(`sample.type="two.sample"`

) that satisfy the requirements for the half-width,
estimated proportions, and confidence level. See the Details section of the help file for
`ciBinomHalfWidth`

for more information.

### Value

a list with information about the sample sizes, estimated proportions, and half-widths.

**One-Sample Case ( sample.type="one.sample")**.

When

`sample.type="one.sample"`

, the function `ciBinomN`

returns a list with these components:
`n` |
the sample size(s) associated with the confidence interval(s) |

`p.hat` |
the estimated proportion(s) |

`half.width` |
the half-width(s) of the confidence interval(s) |

`method` |
the method used to construct the confidence interval(s) |

**Two-Sample Case ( sample.type="two.sample")**.

When

`sample.type="two.sample"`

, the function `ciBinomN`

returns a list with these components:
`n1` |
the sample size(s) for group 1 associated with the confidence interval(s) |

`n2` |
the sample size(s) for group 2 associated with the confidence interval(s) |

`p1.hat` |
the estimated proportion(s) for group 1 |

`p2.hat` |
the estimated proportion(s) for group 2 |

`half.width` |
the half-width(s) of the confidence interval(s) |

`method` |
the method used to construct the confidence interval(s) |

### Note

The binomial distribution is used to model processes with binary
(Yes-No, Success-Failure, Heads-Tails, etc.) outcomes. It is assumed that the outcome of any
one trial is independent of any other trial, and that the probability of “success”, `p`

,
is the same on each trial. A binomial discrete random variable `X`

is the number of
“successes” in `n`

independent trials. A special case of the binomial distribution
occurs when `n=1`

, in which case `X`

is also called a Bernoulli random variable.

In the context of environmental statistics, the binomial distribution is sometimes used to model
the proportion of times a chemical concentration exceeds a set standard in a given period of time
(e.g., Gilbert, 1987, p.143), or to compare the proportion of detects in a compliance well vs. a
background well (e.g., USEPA, 1989b, Chapter 8, p.3-7). (However, USEPA 2009, p.8-27
recommends using the Wilcoxon rank sum test (`wilcox.test`

) instead of
comparing proportions.)

In the course of designing a sampling program, an environmental scientist may wish to determine
the relationship between sample size, confidence level, and half-width if one of the objectives of
the sampling program is to produce confidence intervals. The functions `ciBinomHalfWidth`

,
`ciBinomN`

, and `plotCiBinomDesign`

can be used to investigate these
relationships for the case of binomial proportions.

### Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

### References

Agresti, A., and B.A. Coull. (1998). Approximate is Better than "Exact" for Interval Estimation
of Binomial Proportions. *The American Statistician*, **52**(2), 119–126.

Agresti, A., and B. Caffo. (2000). Simple and Effective Confidence Intervals for Proportions
and Differences of Proportions Result from Adding Two Successes and Two Failures. *The
American Statistician*, **54**(4), 280–288.

Berthouex, P.M., and L.C. Brown. (1994). *Statistics for Environmental Engineers*.
Lewis Publishers, Boca Raton, FL, Chapters 2 and 15.

Cochran, W.G. (1977). *Sampling Techniques*. John Wiley and Sons, New York, Chapter 3.

Fisher, R.A., and F. Yates. (1963).
*Statistical Tables for Biological, Agricultural, and Medical Research*. 6th edition.
Hafner, New York, 146pp.

Fleiss, J. L. (1981). *Statistical Methods for Rates and Proportions*. Second Edition.
John Wiley and Sons, New York, Chapters 1-2.

Gilbert, R.O. (1987). *Statistical Methods for Environmental Pollution Monitoring*.
Van Nostrand Reinhold, New York, NY, Chapter 11.

Millard, S.P., and Neerchal, N.K. (2001). *Environmental Statistics with S-PLUS*.
CRC Press, Boca Raton, Florida.

Newcombe, R.G. (1998a). Two-Sided Confidence Intervals for the Single Proportion: Comparison of
Seven Methods. *Statistics in Medicine*, **17**, 857–872.

Newcombe, R.G. (1998b). Interval Estimation for the Difference Between Independent Proportions:
Comparison of Eleven Methods. *Statistics in Medicine*, **17**, 873–890.

Ott, W.R. (1995). *Environmental Statistics and Data Analysis*.
Lewis Publishers, Boca Raton, FL, Chapter 4.

USEPA. (1989b). *Statistical Analysis of Ground-Water Monitoring Data at RCRA Facilities, Interim Final Guidance*.
EPA/530-SW-89-026. Office of Solid Waste, U.S. Environmental Protection Agency, Washington, D.C.

USEPA. (2009). *Statistical Analysis of Groundwater Monitoring Data at RCRA Facilities, Unified Guidance*.
EPA 530/R-09-007, March 2009. Office of Resource Conservation and Recovery Program Implementation and Information Division.
U.S. Environmental Protection Agency, Washington, D.C. p.6-38.

Zar, J.H. (2010). *Biostatistical Analysis*. Fifth Edition.
Prentice-Hall, Upper Saddle River, NJ, Chapter 24.

### See Also

`ciBinomHalfWidth`

, `uniroot`

,
`plotCiBinomDesign`

, `ebinom`

,

`binom.test`

, `prop.test`

.

### Examples

```
# Look at how the required sample size of a one-sample
# confidence interval increases with decreasing
# required half-width:
ciBinomN(half.width = c(0.1, 0.05, 0.03))
#$n
#[1] 92 374 1030
#
#$p.hat
#[1] 0.5 0.5 0.5
#
#$half.width
#[1] 0.10010168 0.05041541 0.03047833
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------
# Note that the required sample size decreases if we are less
# stringent about how much the confidence interval width can
# deviate from the supplied value of the 'half.width' argument:
ciBinomN(half.width = c(0.1, 0.05, 0.03), tol.half.width = 0.005)
#$n
#[1] 84 314 782
#
#$p.hat
#[1] 0.5 0.5 0.5
#
#$half.width
#[1] 0.10456066 0.05496837 0.03495833
#
#$method
#[1] "Score normal approximation, with continuity correction"
#--------------------------------------------------------------------
# Look at how the required sample size for a one-sample
# confidence interval tends to decrease as the estimated
# value of p decreases below 0.5 or increases above 0.5:
seq(0.2, 0.8, by = 0.1)
#[1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8
ciBinomN(half.width = 0.1, p.hat = seq(0.2, 0.8, by = 0.1))
#$n
#[1] 70 90 100 92 100 90 70
#
#$p.hat
#[1] 0.2 0.3 0.4 0.5 0.6 0.7 0.8
#
#$half.width
#[1] 0.09931015 0.09839843 0.09910818 0.10010168 0.09910818 0.09839843
#[7] 0.09931015
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------------------------------------------------------------
# Look at how the required sample size for a one-sample
# confidence interval increases with increasing confidence level:
ciBinomN(half.width = 0.05, conf.level = c(0.8, 0.9, 0.95, 0.99))
#$n
#[1] 160 264 374 644
#
#$p.hat
#[1] 0.5 0.5 0.5 0.5
#
#$half.width
#[1] 0.05039976 0.05035948 0.05041541 0.05049152
#
#$method
#[1] "Score normal approximation, with continuity correction"
#----------------------------------------------------------------
# Compare required sample size for a one-sample
# confidence interval based on the different methods:
ciBinomN(half.width = 0.05, ci.method = "score")
#$n
#[1] 374
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05041541
#
#$method
#[1] "Score normal approximation, with continuity correction"
ciBinomN(half.width = 0.05, ci.method = "exact")
#$n
#[1] 394
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05047916
#
#$method
#[1] "Exact"
ciBinomN(half.width = 0.05, ci.method = "adjusted Wald")
#$n
#[1] 374
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05041541
#
#$method
#[1] "Adjusted Wald normal approximation"
ciBinomN(half.width = 0.05, ci.method = "Wald")
#$n
#[1] 398
#
#$p.hat
#[1] 0.5
#
#$half.width
#[1] 0.05037834
#
#$method
#[1] "Wald normal approximation, with continuity correction"
#----------------------------------------------------------------
## Not run:
# Look at how the required sample size of a two-sample
# confidence interval increases with decreasing
# required half-width:
ciBinomN(half.width = c(0.1, 0.05, 0.03), sample.type = "two")
#$n1
#[1] 210 778 2089
#
#$n2
#[1] 210 778 2089
#
#$p1.hat
#[1] 0.5000000 0.5000000 0.4997607
#
#$p2.hat
#[1] 0.4000000 0.3997429 0.4001915
#
#$half.width
#[1] 0.09943716 0.05047044 0.03049753
#
#$method
#[1] "Score normal approximation, with continuity correction"
## End(Not run)
```

*EnvStats*version 2.8.1 Index]