convol.dens: Convolution of two empirical distributions

In whitneysu/QuantitativePooledTesting: Pooled Testing Strategies with Quantitative Assays

Description

The function calculates the convolution of two empirical distributions up to the value of cutoff (on the sum of two random variables).

Usage

convol.dens(ecdf1, ecdf2, N1, N2, cutoff)

Arguments

ecdf1	An ecdf that is the matrix output object from the function convol.dens() itself or from the function ecdf_pool(). The ecdf can be truncated up by cutoff.
ecdf2	An ecdf that is the matrix output object from the function convol.dens() itself or from the function ecdf_pool(). The ecdf can be truncated up by cutoff.
N1	The size of the empirical support of ecdf1.
N2	The size of the empirical support of ecdf2.
cutoff	Cutoff of the support of the resulting convolution distribution

Value

The function returns a matrix of three columns: The support of convolution distribution up to 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

experiment_1 <- ecdf_pool(c(0,1,2,4,4,4,4,5,0,9,87,12), cutoff = 8)
example_ecdf_pool <- ecdf_pool(round(runif(100, 0, 20)), cutoff = 18)
convol.dens(example_ecdf_pool$out, example_ecdf_pool$out, cutoff = 18)
convol.dens(experiment_1$out, experiment_1$out, cutoff = 8)

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