WALDCC

Computes Wald-type simultaneous confidence intervals for multinomial
proportions, incorporating a continuity correction. These intervals are
symmetric about the sample proportions and apply a small correction to
improve coverage accuracy, particularly for small samples.

Several authors have proposed methods for constructing simultaneous confidence intervals for multinomial proportions. The package implements seven classical approaches—Wilson, Quesenberry and Hurst, Goodman, Wald (with and without continuity correction), Fitzpatrick and Scott, and Sison and Glaz—along with Bayesian methods based on Dirichlet models. Both equal and unequal Dirichlet priors are supported, providing a broad framework for inference, data analysis, and sensitivity evaluation.

originally by Subbiah M  packaged by Sumathi R

CoinMinD

Simultaneous Confidence Intervals for Multinomial Proportions

Subbiah M

WALDCC function

<dl><dt>inpmat</dt>
<dd>Integer vector of observed cell counts corresponding to a
categorical dataset. All values must be non-negative.</dd>
<dt>alpha</dt>
<dd>Desired statistical significance level</dd></dl>

Arguments

Author

WALDCC: Wald Method with Continuity Correction for Simultaneous Confidence Intervals — WALDCC

<dl>

<dt>inpmat</dt>
<dd>Integer vector of observed cell counts corresponding to a
categorical dataset. All values must be non-negative.</dd>


<dt>alpha</dt>
<dd>Desired statistical significance level</dd>

</dl>

WALDCC: WALDCC: Wald Method with Continuity Correction for Simultaneous Confidence Intervals

Description

Usage

Value

Arguments

Author

Details

References

See Also

Examples