FWW: FWW correction method
In sergihervas/iMKT: Integrative McDonald and Kreitman Test

Description Usage Arguments Details Value Examples

MKT calculation corrected using FWW method (Fay et al. 2001 Genetics).

1	FWW(daf, divergence, listCutoffs = c(0, 0.05, 0.1), plot = FALSE)

`daf`	data frame containing DAF, Pi and P0 values
`divergence`	data frame containing divergent and analyzed sites for selected (i) and neutral (0) classes
`listCutoffs`	list of cutoffs to use (optional). Default cutoffs are: 0, 0.05, 0.1
`plot`	report plot (optional). Default is FALSE

In the standard McDonald and Kreitman test, the estimate of adaptive evolution (alpha) can be easily biased by the segregation of slightly deleterious non-synonymous substitutions. Specifically, slightly deleterious mutations contribute more to polymorphism than they do to divergence, and thus, lead to an underestimation of alpha. Because they tend to segregate at lower frequencies than do neutral mutations, they can be partially controled by removing low frequency polymorphisms from the analysis. This is known as the FWW method.

MKT corrected by the FWW method. List with alpha results, graph (optional), divergence metrics, MKT tables and negative selection fractions

## Using default cutoffs
FWW(myDafData, myDivergenceData)
## Using custom cutoffs and rendering plot
FWW(myDafData, myDivergenceData, c(0.05, 0.1, 0.15), plot=TRUE)