prop-cis: Confidence Interval Functions

In julian-gehring/Rariant: Identification and Assessment of Single Nucleotide Variants through Shifts in Non-Consensus Base Call Frequencies

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)

julian-gehring/Rariant documentation built on May 20, 2019, 4:20 a.m.