propTestN {EnvStats} | R Documentation |

## Compute Sample Size Necessary to Achieve a Specified Power for a One- or Two-Sample Proportion Test

### Description

Compute the sample size necessary to achieve a specified power for a one- or two-sample proportion test, given the true proportion(s) and significance level.

### Usage

```
propTestN(p.or.p1, p0.or.p2, alpha = 0.05, power = 0.95,
sample.type = "one.sample", alternative = "two.sided",
ratio = 1, approx = TRUE,
correct = sample.type == "two.sample",
round.up = TRUE, warn = TRUE, return.exact.list = TRUE,
n.min = 2, n.max = 10000, tol.alpha = 0.1 * alpha,
tol = 1e-7, maxiter = 1000)
```

### Arguments

`p.or.p1` |
numeric vector of proportions. When |

`p0.or.p2` |
numeric vector of proportions. When |

`alpha` |
numeric vector of numbers between 0 and 1 indicating the Type I error level
associated with the hypothesis test. The default value is |

`power` |
numeric vector of numbers between 0 and 1 indicating the power associated with
the hypothesis test. The default value is |

`sample.type` |
character string indicating whether to compute sample size based on a one-sample or
two-sample hypothesis test. |

`alternative` |
character string indicating the kind of alternative hypothesis.
The possible values are |

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

`approx` |
logical scalar indicating whether to compute the sample size based on the normal
approximation to the binomial distribution. The default value is |

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

`round.up` |
logical scalar indicating whether to round up the values of the computed sample size(s)
to the next smallest integer. The default value is |

`warn` |
logical scalar indicating whether to issue a warning. The default value is |

`return.exact.list` |
logical scalar relevant to the case when |

`n.min` |
integer relevant to the case when |

`n.max` |
integer relevant to the case when |

`tol.alpha` |
numeric vector relevant to the case when |

`tol` |
numeric scalar relevant to the case when |

`maxiter` |
integer relevant to the case when |

### Details

If the arguments `p.or.p1`

, `p0.or.p2`

, `alpha`

, `power`

, `ratio`

,
and `tol.alpha`

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

The computed sample size is based on the difference `p.or.p1 - p0.or.p2`

.

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

`approx=TRUE`

.When

`sample.type="one.sample"`

and`approx=TRUE`

, sample size is computed based on the test that uses the normal approximation to the binomial distribution; see the help file for`prop.test`

. The formula for this test and the associated power is presented in standard statistics texts, including Zar (2010, pp. 534-537, 539-541). These equations can be inverted to solve for the sample size, given a specified power, significance level, hypothesized proportion, and true proportion.`approx=FALSE`

.When

`sample.type="one.sample"`

and`approx=FALSE`

, sample size is computed based on the exact binomial test; see the help file for`binom.test`

. The formula for this test and its associated power is presented in standard statistics texts, including Zar (2010, pp. 532-534, 539) and Millard and Neerchal (2001, pp. 385-386, 504-506). The formula for the power involves five quantities: the hypothesized proportion (`p_0`

), the true proportion (`p`

), the significance level (`alpha`

), the power, and the sample size (`n`

). In this case the function`propTestN`

uses a search algorithm to determine the required sample size to attain a specified power, given the values of the hypothesized and true proportions and the significance level.

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

When `sample.type="two.sample"`

, sample size is computed based on the test that uses the
normal approximation to the binomial distribution;
see the help file for `prop.test`

.
The formula for this test and its associated power is presented in standard statistics texts,
including Zar (2010, pp. 549-550, 552-553) and
Millard and Neerchal (2001, pp. 443-445, 508-510).
These equations can be inverted to solve for the sample size, given a specified power,
significance level, true proportions, and ratio of sample size in group 2 to sample size in
group 1.

### Value

**Approximate Test ( approx=TRUE)**.

When `sample.type="one.sample"`

, or `sample.type="two.sample"`

and `ratio=1`

(i.e., equal sample sizes for each group), `propTestN`

returns a numeric vector of sample sizes. When

`sample.type="two.sample"`

and at least one element of `ratio`

is
greater than 1, `propTestN`

returns a list with two components called
`n1`

and `n2`

, specifying the sample sizes for each group.

**Exact Test ( approx=FALSE)**.

If `return.exact.list=FALSE`

, `propTestN`

returns a numeric vector of sample sizes.

If `return.exact.list=TRUE`

, `propTestN`

returns a list with the following components:

`n` |
numeric vector of sample sizes. |

`power` |
numeric vector of powers. |

`alpha` |
numeric vector containing the true significance levels.
Because of the discrete nature of the binomial distribution, the true significance
levels usually do not equal the significance level supplied by the user in the
argument |

`q.critical.lower` |
numeric vector of lower critical values for rejecting the null
hypothesis. If the observed number of "successes" is |

`q.critical.upper` |
numeric vector of upper critical values for rejecting the null
hypothesis. If the observed number of "successes" is |

### 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).

In the course of designing a sampling program, an environmental scientist may wish to determine the
relationship between sample size, power, significance level, and the difference between the
hypothesized and true proportions if one of the objectives of the sampling program is to
determine whether a proprtion differs from a specified level or two proportions differ from each other.
The functions `propTestPower`

, `propTestN`

, `propTestMdd`

, and
`plotPropTestDesign`

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

Studying the two-sample proportion test, Haseman (1978) found that the formulas used to estimate the power that do not incorporate the continuity correction tend to underestimate the power. Casagrande, Pike, and Smith (1978) found that the formulas that do incorporate the continuity correction provide an excellent approximation.

### Author(s)

Steven P. Millard (EnvStats@ProbStatInfo.com)

### References

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

Casagrande, J.T., M.C. Pike, and P.G. Smith. (1978).
An Improved Approximation Formula for Calculating Sample Sizes for Comparing Two Binomial Distributions.
*Biometrics* **34**, 483-486.

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.

Haseman, J.K. (1978). Exact Sample Sizes for Use with the Fisher-Irwin Test for 2x2 Tables.
*Biometrics* **34**, 106-109.

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

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

### See Also

`propTestPower`

, `propTestMdd`

, `plotPropTestDesign`

,
`prop.test`

, `binom.test`

.

### Examples

```
# Look at how the required sample size of the one-sample
# proportion test with a two-sided alternative and Type I error
# set to 5% increases with increasing power:
seq(0.5, 0.9, by = 0.1)
#[1] 0.5 0.6 0.7 0.8 0.9
propTestN(p.or.p1 = 0.7, p0.or.p2 = 0.5,
power = seq(0.5, 0.9, by = 0.1))
#[1] 25 31 38 47 62
#----------
# Repeat the last example, but compute the sample size based on
# the exact test instead of the approximation. Note that because
# we require the actual Type I error (alpha) to be within
# 10% of the supplied value of alpha (which is 0.05 by default),
# due to the discrete nature of the exact binomial test
# we end up with more power then we specified.
n.list <- propTestN(p.or.p1 = 0.7, p0.or.p2 = 0.5,
power = seq(0.5, 0.9, by = 0.1), approx = FALSE)
lapply(n.list, round, 3)
#$n
#[1] 37 37 44 51 65
#
#$power
#[1] 0.698 0.698 0.778 0.836 0.910
#
#$alpha
#[1] 0.047 0.047 0.049 0.049 0.046
#
#$q.critical.lower
#[1] 12 12 15 18 24
#
#$q.critical.upper
#[1] 24 24 28 32 40
#----------
# Using the example above, see how the sample size changes
# if we allow the Type I error to deviate by more than 10 percent
# of the value of alpha (i.e., by more than 0.005).
n.list <- propTestN(p.or.p1 = 0.7, p0.or.p2 = 0.5,
power = seq(0.5, 0.9, by = 0.1), approx = FALSE, tol.alpha = 0.01)
lapply(n.list, round, 3)
#$n
#[1] 25 35 42 49 65
#
#$power
#[1] 0.512 0.652 0.743 0.810 0.910
#
#$alpha
#[1] 0.043 0.041 0.044 0.044 0.046
#
#$q.critical.lower
#[1] 7 11 14 17 24
#
#$q.critical.upper
#[1] 17 23 27 31 40
#----------
# Clean up
#---------
rm(n.list)
#==========
# Look at how the required sample size for the two-sample
# proportion test decreases with increasing difference between
# the two population proportions:
seq(0.4, 0.1, by = -0.1)
#[1] 0.4 0.3 0.2 0.1
propTestN(p.or.p1 = seq(0.4, 0.1, by = -0.1),
p0.or.p2 = 0.5, sample.type = "two")
#[1] 661 163 70 36
#Warning message:
#In propTestN(p.or.p1 = seq(0.4, 0.1, by = -0.1), p0.or.p2 = 0.5, :
# The computed sample sizes 'n1' and 'n2' are too small,
# relative to the given values of 'p1' and 'p2', for the normal
# approximation to work well for the following element indices:
# 4
#----------
# Look at how the required sample size for the two-sample
# proportion test decreases with increasing values of Type I error:
propTestN(p.or.p1 = 0.7, p0.or.p2 = 0.5,
sample.type = "two",
alpha = c(0.001, 0.01, 0.05, 0.1))
#[1] 299 221 163 137
#==========
# Modifying the example on pages 8-5 to 8-7 of USEPA (1989b),
# determine the required sample size to detect a difference in the
# proportion of detects of cadmium between the background and
# compliance wells. Set the complicance well to "group 1" and
# the backgound well to "group 2". Assume the true probability
# of a "detect" at the background well is 1/3, set the probability
# of a "detect" at the compliance well to 0.4 and 0.5, use a 5%
# significance level and 95% power, and use the upper
# one-sided alternative (probability of a "detect" at the compliance
# well is greater than the probability of a "detect" at the background
# well). (The original data are stored in EPA.89b.cadmium.df.)
#
# Note that the required sample size decreases from about
# 1160 at each well to about 200 at each well as the difference in
# proportions changes from (0.4 - 1/3) to (0.5 - 1/3), but both of
# these sample sizes are enormous compared to the number of samples
# usually collected in the field.
EPA.89b.cadmium.df
# Cadmium.orig Cadmium Censored Well.type
#1 0.1 0.100 FALSE Background
#2 0.12 0.120 FALSE Background
#3 BDL 0.000 TRUE Background
# ..........................................
#86 BDL 0.000 TRUE Compliance
#87 BDL 0.000 TRUE Compliance
#88 BDL 0.000 TRUE Compliance
p.hat.back <- with(EPA.89b.cadmium.df,
mean(!Censored[Well.type=="Background"]))
p.hat.back
#[1] 0.3333333
p.hat.comp <- with(EPA.89b.cadmium.df,
mean(!Censored[Well.type=="Compliance"]))
p.hat.comp
#[1] 0.375
n.back <- with(EPA.89b.cadmium.df,
sum(Well.type == "Background"))
n.back
#[1] 24
n.comp <- with(EPA.89b.cadmium.df,
sum(Well.type == "Compliance"))
n.comp
#[1] 64
propTestN(p.or.p1 = c(0.4, 0.50), p0.or.p2 = p.hat.back,
alt="greater", sample.type="two")
#[1] 1159 199
#----------
# Clean up
#---------
rm(p.hat.back, p.hat.comp, n.back, n.comp)
```

*EnvStats*version 2.8.1 Index]