optimize_R: Find the otimum R value for a given data set

In rfael0cm/RTIGER: HMM-Based Model for Genotyping and Cross-Over Identification

Find the otimum R value for a given data set

Description

Find the otimum R value for a given data set

Usage

optimize_R(object,
max_rigidity = 2^9, average_coverage = NULL, crossovers_per_megabase = NULL,
save_it = FALSE, savedir = NULL)

Arguments

object	an RTIGER object
max_rigidity	R values will be explored up the value given in this parameter. Default = 2^9
average_coverage	For conservative results set it to the lowest average coverage of a sample in your experiment, or evne to the lowest average coverage in a (sufficiently large) region in one of your samples. The lower the value, the more conservative (higher) our estimates of the false positive segments rates. If it is not provided it will be computed as the average of all data points.
crossovers_per_megabase	For conservative results set it to the highest ratio of a sample in your experiment. The higher the value, the more conservative (higher) our estimates of the false positive segments rates. If it is not provided it will be computed as the average of all samples.
save_it	logical values if the results should be saved. Plots might be complicated to interpret. We suggest to read the manuscript to understand them (https://doi.org/10.1093/plphys/kiad191)
savedir	if results are saved, in which directory.

Value

A value with the optimum rigidity for the data set.

Examples

data("fittedExample")
bestR = optimize_R(myDat)

rfael0cm/RTIGER documentation built on Oct. 31, 2023, 1:25 p.m.