ecdf_pool: Empirical CDF Calculation for Quantitative Pooled Testing

In whitneysu/QuantitativePooledTesting: Pooled Testing Strategies with Quantitative Assays

Description

Compute an empirical cumulative distribution function, with results similar to an “ecdf” object.

Usage

ecdf_pool(v, N = length(v), cutoff = max(v), ...)

Arguments

v	Numeric vector of the observations for calculating ecdf. For quantitative pooling strategies, only CDF on the support of v < cutoff is needed; so v can be the fraction of observations less than the cutoff and use N to pass the number of all observations.
N	The number (vector length) of the observations. By default, N = length(v).
cutoff	Cutoff value of quantitative assay that defines test positivity. By default, cutoff = max(v) which calculates the CDF for the entire support of v.
...	Arguments to be passed to subsequent methods.

Value

The function returns a matrix of three columns: The support of (v <= cutoff), empirical PMF and empirical CDF.

References

Liu T, Hogan JW, Daniels, MJ, Coetzer M, Xu Y, Bove G, et al. Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing with Marker-Assisted Deconvolution. Journal of AIDS. 2017;75(5): 580-587.

Examples

ecdf_pool(round(runif(100, 0, 20)), cutoff = 18)

whitneysu/QuantitativePooledTesting documentation built on Feb. 2, 2021, 12:46 p.m.