Description Usage Arguments Value References

Algorithms III and IV from Wright (2017), and classical (unconstrained)
Neyman allocation. Algorithm III finds the optimial allocation for a
given total sample size `n`

. Algorithm IV samples until the overall
variance is smaller than a given `v0`

.

1 2 3 4 5 6 7 |

`n` |
Target sample size for Algorithm III (integer) |

`N.str` |
Population size for each stratum (integer vector) |

`S.str` |
Standard deviation for each stratum (numeric vector) |

`lo.str` |
Sample size lower bounds for each stratum (numeric vector) |

`hi.str` |
Sample size upper bounds for each stratum (numeric vector) |

`verbose` |
Print detailed information for each selection (boolean) |

`v0` |
Target variance for Algorithm IV (numeric) |

An object which contains the results; the structure depends on allocation method.

Tommy Wright (2012). The Equivalence of Neyman Optimum Allocation for Sampling and Equal Proportions for Apportioning the U.S. House of Representatives. The American Statistician, 66, pp.217-224.

Tommy Wright (2017), Exact optimal sample allocation: More efficient than Neyman, Statistics & Probability Letters, 129, pp.50-57.

andrewraim/tommysampling documentation built on May 6, 2018, 5:03 a.m.

Embedding an R snippet on your website

Add the following code to your website.

For more information on customizing the embed code, read Embedding Snippets.