propCIs {Rariant} | R Documentation

## Confidence Interval Functions

### Description

Vectorized implementation of confidence intervals

### Usage

    acCi(x1, n1, x2, n2, conf_level = 0.95, clip = TRUE, split = FALSE)
    nhsCi(x1, n1, x2, n2, conf_level = 0.95)

### Arguments

`x1` | Mismatch counts in the test sample.
`n1` | Sequencing depth (total counts) in the test sample.
`x2` | Mismatch counts in the control sample.
`n2` | Sequencing depth (total counts) in the control sample.
`conf_level` | Confidence level $beta$ (default: 0.95).
`clip` | Should the CIs be clipped to the interval [-1,1] if they exceed this?
`split` | Should the sample split method be applied? See 'splitSampleBinom' for details.

### Details

These functions implement a vectorized version of the two-sided Agresti-Caffo, and Newcombe-Hybrid-Score confidence interval for the difference of two binomial proportions.

### Value

A data frame with columns

- dEstimate for the difference of rates 'p1' and 'p2'.

- p1, p2Estimates for the mismatches rates for each sample.

- lower, upperLower and upper bound of the confidence interval.

- wWidth of the confidence interval.

### References

Agresti, Alan, and Brian Caffo. Simple and Effective Confidence Intervals for Proportions and Differences of Proportions Result from Adding Two Successes and Two Failures. The American Statistician 54, no. 4 (2000): 280–288

Newcombe, Robert G. Interval Estimation for the Difference between Independent Proportions: Comparison of Eleven Methods. Statistics in Medicine 17, no. 8 (1998): 873–890.

Fagerland, Morten W., Stian Lydersen, and Petter Laake. Recommended Confidence Intervals for Two Independent Binomial Proportions. Statistical Methods in Medical Research (2011).

### See Also

nhsCi

splitSampleBinom

binMto::Add4 binMto::NHS

### Examples

    ## Generate sample data
    counts = data.frame(x1 = 1:5, n1 = 30, x2 = 0:4, n2 = 30)

    ## Agresti-Caffo
    ci_ac = with(counts, acCi(x1, n1, x2, n2))

    ## Newcombe-Hybrid Score
    ci_nhs = with(counts, nhsCi(x1, n1, x2, n2))

    print(ci_ac)

[Package Rariant version 1.16.0 Index]